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Location: Annapolis, MD Equipment and Capital Category Manager Position Objective: Under the general supervision of the Manager, Contracts and Sourcing, the Equipment and Capital Category Manager drives strategic sourcing and contracting initiatives for equipment and capital projects. This role emphasizes systemwide standardization, capital planning, and cross-functional collaboration with Finance, Clinical, Information Services, Bio-Med, Risk Management, and Supply Chain. The Category Manager leads supplier negotiations, ensures contract and regulatory compliance, and oversees technology assessments to support enterprise goals and systemwide alignment. Essential Job Duties: Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. This role includes strategic category management, capital and operational budget collaboration, and leadership in sourcing, contracting and procurement activities. The manager drives value through data analysis, benchmarking, and supplier engagement, while ensuring alignment with organizational priorities and compliance standards. 1. Sourcing and Category Strategy Development: a. Demonstrates expertise in the use of external sources to keep abreast of trends, pricing, benchmarking tools, GPO initiatives, etc. b. Use the RFP process and other decision support tools to perform analysis on supply usage trends in order to identify savings opportunities, drive standardization and equipment selection. c. Conducts comprehensive market research, thorough financial analysis, and consults with appropriate department owners and teams to determine procurement strategy, assisting in lease v. buy v. place analysis. d. Represents Supply Chain as a functional reviewer on all equipment and capital requests and manages requests through functional, clinical, technical and budgetary review processes. e. Thoroughly reviews new requests, using a quality check list to validate departmental account and GL coding, pricing, supplier information/documentation and to ensure operational costs have been identified separately from capital costs f. Encourages consideration of small, woman-owned and minority vendors when presenting options to department users. 2. Strategic Procurement: a. Manages contracts through the appropriate procurement and contracting process. b. Utilizes Symplr, GHx and GPO CLM tools c. Ensures Contract Compliance through collaboration with Value Analysis, Contract Administration and Legal as well as other necessary stakeholders such Finance, Biomed, IS and the Privacy office to ensure Contracts meet corporate standards and are executed in accordance to Supply Chain policies and procedures. d. Responsible for ensuring that approvals and contracts are stored in the appropriate contract CLM Tools 3. PO Execution and Reporting: a. Responsibly for the generation and design of purchase orders for Contracts, Service, Maintenance, Licenses and other POs that support the purchase of equipment and capital projects and non-capital- non routine supply purchases i.e. POs for contracts. b. Continually reviews and updates capital budget report as needed. c. Collaborate with Finance to ensure all service agreements have specified naming conventions in order for those POs to be easily identified and accounted for on the month end PO Accrual report. d. Provides support to Procurement such as: back-up Procurement Analysts, research of PO to Invoice matching errors and providing resolution, research old/outstanding purchase orders to determine if they can be closed or if payment is still required, and continuous collaboration between purchasing and accounts payable on the status of those POs. 4. Implementation Support: a. Collaborates with all affected departments on request and maintains oversight on the implementation process for equipment and capital projects utilizing project plans as required. b. Coordinates with Contract Administration and Supply Chain Systems team to ensure updates to pricing and associated product conversions are executed in accordance with the project schedule. 5. Ongoing Contract Compliance and Vendor Management: a. Assists in the development of compliance reporting and attends QBRs as necessary to manage compliance against contracted strategies. b. Maintains Furniture, Fixtures andEquipment (FF&E) standardization, as well as manages the coordination, procurement and delivery of FF&E for ergonomic requests from Employee Health, requests from departmental leaders and any non­ capital related projects. Coordinates the purchase of furnishings and equipment needs for major capital renovations and capital projects as requested. c. Assists with equipment returns d. Meets on a routine basis with assigned areas to improve service, quality and financial outcomes, developing partnerships with supporting departments such as information services, finance, and other support services. 6. Process Improvement: Participates in department and organizational LEAN/Process Improvement efforts; makes recommendations to the Manager of Sourcing and Contracting on improvements to methods and procedures as they relate to the Capital Equipment sourcing functions. Educational/Experience Requirements: Required Minimum Education. · The minimum level of education for this position is Bachelor’s degree or equivalent years directly relatable Health System procurement experience. Required Minimum Experience: · Five years of strategic sourcing or category management experience in a healthcare or hospital environment required. · Three years of capital planning or project management experience preferred. · Demonstrated experience in cross-functional leadership, contract negotiation, and technology assessment. · Proficiency in budgeting, supply chain analytics, and vendor management. · Experience with Microsoft Office Suite and ERP systems required. · LEAN/Six Sigma or other process improvement certifications preferred. Required Licenses/Certifications: · None required; LEAN/Six Sigma certification preferred. Working Conditions, Equipment, Physical Demands: Medium work. Exerting up to 50 pounds of force occasionally, and/or up to 30 pounds of force frequently, and/or up to 10 pounds of force constantly to move objects. There is a reasonable expectation that employees in this position will not be exposed to blood-borne pathogens. Pay Range $87,360 — $108,000 USD Luminis Health Benefits Overview: •    Medical, Dental, and Vision Insurance •    Retirement Plan (with employer match for employees who work more than 1000 hours in a calendar year) •    Paid Time Off •    Tuition Assistance Benefits •    Employee Referral Bonus Program •    Paid Holidays, Disability, and Life/AD&D for full-time employees •    Wellness Programs •    Employee Assistance Programs and more *Benefit offerings based on employment status Opt-in for text notifications! Luminis Health's two-way SMS texting platform lets you receive notifications and messages from our Talent Acquisition team directly on your phone. To enable this feature, select "yes" when asked to "opt-in to receive text messages" and to "Receive updates from a recruiter about this job via SMS" when completing your application. Once you are opted in, you can easily opt-out at any time. Standard text messaging rates may apply based on the candidate's mobile carrier plan. Luminis Health is not responsible for any charges incurred by the recipient. Candidates are encouraged to review their mobile carrier's plan for applicable text messaging rates and usage charges.

Location: United States About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. Please note: This role will require occasional travel to San Francisco. The role Nebius is looking for a Communications Designer, Developer Experience to be a part of the Design team and embedded with DevRel. At Nebius, we believe builders decide which platforms win. AI engineers, agent builders, ML engineers and inference teams choose what to build on long before it becomes a procurement decision — and we want to earn that choice early, while they're still picking their models, inference and tooling. This role is about making Nebius the brand those builders recognize, respect, and share. You’re welcome to work remotely in the United States. Your responsibilities will include: Building and evolving the visual language for everything developer-facing — one system that holds together across the dev.nebius.com , AI Builder Program, social, events, swag, and developer community. Designing for where builders actually are — meetups, hackathons, conference presence, event environments, and swag that developers actually want to wear and keep. Designing web pages, developer program materials, campaign visuals, and social content for a technical audience. Making technical content look as good as it is: benchmarks, GPU specs, pricing, architecture diagrams — turning dense material into visuals people actually want to read and share. Creating AI-templates and guidelines so the DevRel team can produce on-brand materials at speed without a designer in every loop. Working in the open with DevRel and content: quick concepts, tight iterations, ship-learn-repeat. We expect you to have: A portfolio of marketing that developers actually respect — launch pages, developer program sites, campaigns, event visuals, technical content. Engineers can smell fluff; you don't produce it. You understand the audience: AI builders, ML engineers, DevOps, inference and platform teams, developers moving into AI. You know what catches their attention, what they ignore, and what makes them share something with a colleague. You study the brands developers love — and can explain why their marketing lands with a technical audience, bringing that thinking into your own work. Experience designing for physical spaces and events: booths, signage, environments, printed materials, swag — you understand print technologies, materials, and production constraints, and can take a design from concept to fabrication. System-first mindset: a visual language that scales across surfaces, not a collection of one-offs. AI-proficiency: up-to-date knowledge of AI tools and a strong critical eye for results — from a design expert's perspective, not an average user's. Strong layout, composition, and information design skills: typography, grids, visual hierarchy, and the craft to keep dense technical material clear. Ability to articulate and defend design decisions, incorporate feedback, and iterate quickly. Excitement about defining what an AI cloud brand looks like for the people building on it. #LI-LT1 Key employee benefits in the US: Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) plan: Up to 4% company match with immediate vesting. Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Remote work reimbursement: Up to $85/month for mobile and internet. Disability & life insurance : Company-paid short-term, long-term and life insurance coverage. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $150,000 — $187,500 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: London Workplace: Hybrid ABOUT NIANTIC SPATIAL We are building for the >80% of economic activity that happens beyond the reach of screens. AI has learned from the text, images and video online, but it still lacks an accurate understanding of physical space. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. ABOUT THE ROLE You'll help build the geometric foundation of the next-generation world map — joining our World Map Research Engineering team (Geospatial Alignment) as a Research Engineer or Senior Research Engineer. This is an end-to-end applied research role that spans from strong engineering and data-systems work to R&D in 3D deep learning, transformers, and foundation models. You'll develop new algorithms, produce the research data and evaluation assets needed to advance them, and engineer the resulting capabilities into dependable production systems. The World Map team works with heterogeneous captures from phones and handheld devices, aerial imagery, LiDAR, and 360 cameras, as well as external geographic and elevation data. The central challenge is to harmonize these data sources, quantify uncertainty, and maintain consistency as the map evolves. You'll work at the intersection of cutting-edge research and scalable engineering — developing new approaches to geospatial registration, correspondence, and geometric optimization, then productionizing these technologies to directly improve Niantic Spatial's world model. What You'll Do - Geospatial Registration — Develop and advance algorithms for world-scale georeferencing and registration across imagery, 3D reconstructions, point clouds, and geographic reference data. - 3D Geometry & Correspondence — Build and improve methods spanning multi-view geometry, SfM, SLAM, feature matching, learned correspondence, bundle adjustment, point-cloud registration, and ICP. - Data & Evaluation — Create the data and evaluation flywheel for world-scale mapping — design and maintain large-scale datasets, pipelines, benchmarks, and evaluation methodologies that expose hard failure modes, accelerate research iteration, and support production. - Learning-Based 3D Reasoning — Explore and develop learning-based approaches for geospatial understanding, combining transformers and 3D foundation models with geometric priors and uncertainty modeling. - Production Robustness — Design robust solutions for challenging real-world conditions: sparse or noisy GPS, changing appearance, inconsistent reconstruction quality, large geographic extents, and imperfect reference maps. - Cross-Functional Collaboration — Partner with leading researchers in visual localization, 3D computer vision, and mapping — as well as engineering and product teams — to identify high-impact problems and turn research breakthroughs into end-to-end production systems. - Research Communication — Share results clearly through technical writing, design reviews, and internal presentations; contribute to publications and open research where appropriate. WHAT YOU'LL BRING - Bachelor's or Master's degree in Computer Science, Robotics, Machine Learning, Photogrammetry, or a closely related field — or equivalent research or industry experience. - Strong grounding in 3D computer vision, machine learning, robotics, photogrammetry, or a closely related area. - Demonstrated experience with 3D geometry and several relevant areas — multi-view reconstruction, SfM, SLAM, camera pose estimation, point-cloud registration, image-based localization, georeferencing, or sensor fusion. - The ability to formulate ambiguous real-world problems, choose the right mathematical or learning-based tools, and evaluate solutions rigorously. - Strong Python skills and experience with modern ML and scientific-computing libraries (PyTorch, NumPy, SciPy). - Strong software engineering skills — you can turn research prototypes into reliable, scalable, and maintainable systems. - A track record of taking an algorithm from research idea and experimental prototype through evaluation, benchmarking, and deployment on real-world data. - Clear written and verbal communication; able to collaborate effectively across research and engineering disciplines. NICE TO HAVE - PhD in computer vision, robotics, machine learning, photogrammetry, geodesy, remote sensing, or a related field — not required, but valued. - A strong publication record or active involvement in the research community at venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, SIGGRAPH, 3DV, ICRA, or RSS. - Experience with learning-based approaches for visual or 3D understanding — transformers, point transformers, foundation models, or methods combining learned representations with explicit geometric priors. - Experience with aerial or satellite imagery, GNSS/GPS, geodesy, mapping systems, or large-scale spatial data. - Evidence of technical leadership, mentorship, or successful cross-functional delivery. COMPENSATION & BENEFITS Base salary: Competitive market rate. Compensation also includes an annual bonus, equity, and a comprehensive benefits package. LOCATION & WORK MODEL This role is based in our London office, with three days per week in office. We welcome applications from candidates who require visa sponsorship. INCLUSIVE APPLICATION We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed — you may be exactly who we're looking for. EQUAL OPPORTUNITY Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. CANDIDATE PRIVACY I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: Paris ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE ROLE This is a chance to build the frontier LLM evals that shape the next generations of flagship models. Could a model design a computer chip, or find the backdoor an attacker left in a network? Public benchmarks can't tell us, as they're generally not a good measure of real work. As an Evaluation Engineer, you'll own evals from start to finish. You'll run workshops with customers, industry experts and our customer-facing teams to understand their most important industry use cases (e.g., an engineer updating a part in CAD software), and help decide which are worth building first. You'll also look beyond today's use cases and design moonshot evals for work no model can do yet, so Mistral gets there first. Then you'll turn each one into a benchmark, often one where an agent has to do the work inside real software. You'll source the data, decide how to score it, and run it on every new Mistral model and on competitor models. When our models fall short, you'll work with the post-training teams to close the gap. When they catch up, you'll make the eval harder. You'll write up and present your results so senior leaders can act on them. They also give our go-to-market teams evidence of where Mistral leads. You'll learn new fields quickly. One month you might be learning how chip designers check their work, and the next how a bank investigates a flagged payment. The team is new and fast-paced, and you'll have a lot of autonomy. You'll spend a lot of time speaking with people across Mistral to work out what matters most, then decide what to build. WHAT YOU WILL DO - Run discovery with customers, industry experts and teams across Mistral, then help decide which use cases are worth building first. - Design new benchmarks for industry use cases and frontier capabilities, including how they're scored. - Build evals where agents work inside real software (e.g., CAD and simulation tools), and run them on our models with checks that the results stay reliable over time. - Stress-test existing evals to find flaws and bugs before we rely on them. - Source data with expert vendors. Where customer data can't leave the customer, build realistic proxy or synthetic data instead. - Write up and present what you find so leaders across Mistral can act on it. - Help other teams build their own evals, with tools and guides they can reuse. WHAT WE'RE LOOKING FOR EXPERIENCE - 2+ years of industry experience (e.g., in software, AI, or consulting), or a PhD. - A software engineering or research background, with experience writing code that others can build on. - Experience working directly with customers, industry experts, or other teams (e.g., through consulting, solutions engineering, or research collaborations). - Experience building something with LLMs. - Comfort working with large datasets, including building and cleaning them. TECHNICAL - Strong Python skills. - Comfortable working in a large, shared codebase and getting your code through reviews by other teams. - A strong understanding of LLM training, inference, and evaluation. COMMUNICATION - You enjoy speaking with people and are good at it. - You write clearly. A short doc or deck from you is enough for a busy leader to make a decision. - Confident presenting to both technical and non-technical audiences. - Fluent in English. HOW YOU WORK - High energy, and you don't need a roadmap. You move independently and handle uncertainty with confidence. - Proactive and high-agency. You spot what needs doing and get it done. - You make good decisions about what to build first and can explain why. - Low-ego, collaborative, and eager to learn. - Curious, with a track record of learning things outside your work or studies. You can get to grips with a new field in a few weeks. NICE TO HAVE - Experience building AI evaluations, especially for industry applications. - Published research on evals or contributions to open-source evaluation frameworks. - Experience building datasets, generating synthetic data, developing labelling guidelines, or working with annotation vendors. - A background in an industry outside software (e.g., engineering, finance, or science). WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: Hybrid About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: San Francisco, CA About the team The Finance team is a critical pillar of Cloudflare’s success, translating strategy into financial outcomes, and serving as a trusted voice to the public markets, the Board, and our shareholders. The Investor Relations team is a small, high-impact group that tells Cloudflare’s financial story to the public markets — translating one of the most differentiated technology platforms in the world into a clear, credible, and compelling investment narrative. About the role Cloudflare is looking for an Associate, Investor Relations & Strategic Finance to join our lean IR team and help power how we engage with Wall Street. You will be a core contributor to the quarterly earnings cycle, our consensus and financial modeling work, investor events including our annual Investor Day, and strategic finance projects for senior leadership. This is a hands-on, high-visibility role. The work you produce will go directly to the CFO, CEO, Board, and public markets, so we hold ourselves to an exceptionally high bar on accuracy. You will work closely with Finance, Accounting, Legal, Communications, FP&A, and the executive team, and you will get broad exposure to the business and to the public markets. We are AI-native in how we work, and we expect you to treat modern tooling as a force multiplier on insight, productivity, and rigor. What you’ll do Quarterly earnings and consensus Support the end-to-end quarterly earnings process, helping draft and assemble the press release, supplemental financial information, prepared remarks, Q&A prep, and investor deck. Build and maintain our consensus model: collect and analyze sell-side estimates and models, and benchmark them against internal forecasts. Support the guidance process, including share count forecasting, sensitivity analyses, and supporting materials for leadership. Drive tie-out and validation work so that every figure, claim, and disclosure across the press release, supplemental, scripts, Q&A, deck, 10-Q, and 10-K is accurate and consistent. Help prepare for earnings Q&A: anticipated questions, peer transcript research, key stats and proof points, and live-call backup material. Coordinate the post-earnings analyst callback program, including scheduling, notes, and feedback summaries. Investor engagement Support day-to-day engagement with sell-side analysts and institutional investors, including responding to inbound requests and maintaining our investor database. Help plan and run investor events, including sell-side conferences, one-on-ones, on-site investor visits, and non-deal roadshows. Prepare briefing materials for the CEO, CFO, and other executives ahead of investor meetings, including investor profiles, talking points, and Q&A prep. Maintain core IR collateral, including the investor presentation, fact sheets, and IR website content. Analyze shareholder ownership and support investor targeting. Investor Day and strategic finance projects Support the planning and execution of our annual Investor Day, including the invite tracker and registration, content drafting, validation, and day-of logistics. Contribute to strategic finance projects such as peer benchmarking, competitive analyses, long-range financial modeling, and ad-hoc analyses for senior leadership. Analytics and tooling Monitor and synthesize analyst coverage, peer results, investor sentiment, and industry trends into clear, actionable summaries for leadership. Build and maintain the models, dashboards, trackers, and validation tools that let a small team operate at scale. Use AI-native tooling and automation to improve speed, accuracy, and insight across the IR function. Examples of desirable skills, knowledge and experience 3–5 years of experience in investment banking, equity research, investor relations, FP&A, corporate finance, or equity investing; exposure to public technology companies is a plus. Strong financial modeling skills , including building and maintaining operating models, consensus models, and share count and dilution forecasts. Solid understanding of financial statements , public-company reporting, and valuation; familiarity with SaaS or consumption-based revenue models is a plus. Strong written communication skills , with the ability to draft clear, concise materials for senior executives and to turn complex financial or technical concepts into simple narratives. Exceptional attention to detail , and pride in getting every number right. An ownership mindset : you are organized, take initiative, and can manage multiple deadlines in a fast-paced environment, especially during earnings season. High-trust collaboration , with the ability to work effectively with teams across Finance, Accounting, Legal, and Communications. AI-native curiosity and skill : fluency with modern AI tooling and a track record of using it to amplify your impact. Sound judgment with confidential information , including awareness of materiality, fair disclosure, and Reg FD (or a strong willingness to learn). Genuine interest in cybersecurity, cloud infrastructure, developer platforms, or AI, and in the public markets. Compensation Compensation may be adjusted depending on work location. For Bay Area based hires: Estimated annual salary of $117,000 - $161,000 Equity This role is eligible to participate in Cloudflare’s equity plan. Benefits Cloudflare offers a complete package of benefits and programs to support you and your family.  Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun!  The below is a description of our benefits for employees in the United States, and benefits may vary for employees based outside the U.S. Health & Welfare Benefits Medical/Rx Insurance Dental Insurance Vision Insurance Flexible Spending Accounts Commuter Spending Accounts Fertility & Family Forming Benefits On-demand mental health support and Employee Assistance Program Global Travel Medical Insurance Financial Benefits Short and Long Term Disability Insurance Life & Accident Insurance 401(k) Retirement Savings Plan Employee Stock Participation Plan Time Off Flexible paid time off covering vacation and sick leave Leave programs, including parental, pregnancy health, medical, and bereavement leave What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.

Location: New York, New York, United States About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . Role: Enterprise AI - Head of Platform Engineering Location: Manhattan, New York City Experience Level: 15+ years, including at least 5 years leading platform or infrastructure teams Work Model: Hybrid, with three days per week in the office near Madison Square Park Why Join Enterprise AI? Enterprise AI is building the technical platform that enables enterprises to deploy advanced AI systems in production. As Head of Platform Engineering, you will lead the technical platform team and help scale the organization as it moves from early platform development toward enterprise adoption. You will work closely with architecture and delivery leaders to establish the technical foundations, operating model, and product capabilities required to support demanding enterprise customers. This is a high-ownership role for a technical leader who can build production-grade infrastructure, hire and develop a strong team, and help turn an internal platform into a reliable enterprise product. As the Head of Platform Engineering, You Will Build and Scale the Platform Set the technical direction for a model-agnostic, multi-tenant enterprise AI platform and establish the architecture, reliability, and operating foundations required for scale. Lead Platform Engineering Build and lead a high-caliber platform organization. Hire quickly at a high bar, develop the team, and create clear ownership across platform engineering, architecture, evaluation, and delivery. Enable Enterprise Deployment Develop platform capabilities that support secure, observable, and reliable AI deployments across enterprise environments, including customer-controlled infrastructure where required. Partner Across Architecture and Delivery Work closely with architecture and delivery leaders to translate customer needs into platform priorities, establish clear technical seams, and ensure the platform supports real-world enterprise deployments. Drive Productization Help evolve the platform from an internal capability into a reliable, externally deployable enterprise product. Establish clear standards for performance, security, observability, evaluation, and operational readiness. Demonstrate Customer Value Partner with delivery teams to support client use cases and ensure that platform deployments are evaluated against measurable business and technical outcomes. The Anti-Sell Hands-on technical leadership: This is not a role for someone who has been on a pure management track for five or more years. You must have personally written production code within the last two years. Enterprise-grade complexity: The platform must support multi-tenant enterprise software, secure deployments, customer-controlled environments, and demanding enterprise requirements. High hiring and execution bar: You will be expected to hire quickly, maintain a high bar, and lead a growing platform organization while delivering against an ambitious product roadmap. Security and regulatory accountability: The role requires deep experience with enterprise security, identity, and hosting, including experience working with regulated customers. Breadth of experience: Candidates with a solely consumer or solely services background may not be a fit for this role. Our Enterprise AI Platform Leaders Typically Have Deep platform engineering experience: Typically 15+ years in software engineering, including at least five years leading platform or infrastructure teams that have shipped multi-tenant enterprise software. Multi-tenant and self-hosted architecture expertise: Proven experience designing and operating platforms for enterprise customers, including self-hosted or customer-controlled deployments. Hands-on LLM systems expertise: Direct experience with LLM orchestration, evaluation, serving, and fine-tuning, as well as the ability to contribute production code. Productization experience: A track record of taking a platform from an internal tool to an externally licensed or deployable product. Enterprise security and team leadership: Deep knowledge of enterprise security, identity, and hosting with regulated customers. You should also have a proven record of hiring quickly at a high bar and leading platform or infrastructure teams. Top-Tier Candidates Will Have Experience building or scaling a platform inside a services or forward-deployed organization. Experience in financial services or another highly regulated industry. Experience supporting enterprise AI deployments across complex customer environments. Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

Location: San Francisco; Seattle; New York City ABOUT THE ROLE The Enterprise Agent Evals & Efficiency team makes Perplexity's agents better at completing real work across enterprise connectors, tools, and data. Our goal is to help agents choose the right tools, use the right context, and complete complex workflows reliably, with less time and compute. This team owns the improvement loop: define what successful work looks like, build evaluations that measure it, understand where agents fail, and ship changes that improve outcomes. This is a hands-on engineering role with end-to-end ownership, from building realistic enterprise evaluation environments to shipping improvements across the agent harness, tools, and skills. You'll create complex scenarios spanning connected systems, data, and permissions, use them to identify where agents struggle, and improve the tools agents call, the skills they follow, the context they receive, and the models they use. Working with AI Research, Connectors, and Platform teams, you'll turn experimental results into measurable gains in task success, latency, and cost per successful task. The goal is not just to measure agents, but to make more enterprise work reliable and practical to automate. KEY RESPONSIBILITIES - Build realistic enterprise evaluation environments spanning connected applications, representative datasets, user roles, and permissions, with complex, multi-step workflows and reproducible system state. - Own evaluation infrastructure, task suites, and automated graders that measure how well agents complete enterprise work, calibrated against human judgment and grounded in end-to-end outcomes. - Analyze agent trajectories and production failures, identify root causes, and ship improvements across the agent harness, tools, and skills that measurably improve task success and reliability. - Develop reusable skills and improve tool descriptions, schemas, and result handling so agents can select the right tools, execute workflows correctly, and recover from errors with fewer unnecessary calls. - Build and evaluate model-selection and routing strategies that match each task to the right model and effort level, balancing quality, latency, reliability, and cost. - Improve agent efficiency through context management, skill loading, caching, and execution strategies that reduce wasted tokens and cost per successful task without compromising quality or safety. - Build regression gates and launch-readiness signals for model, connector, tool, skill, and harness changes, and validate that evaluation gains translate into better production outcomes. QUALIFICATIONS - 5+ years of software engineering or machine learning engineering experience, with a track record of owning complex production systems from design through deployment and operation. - Strong system design skills, including building reliable, scalable infrastructure for experimentation, evaluation, or data-intensive applications. - Strong proficiency in a backend language such as Python, Go, or Rust, and the ability to work effectively across a multi-language production stack. - Hands-on experience building, evaluating, or improving LLM-based applications or agents, including tool use and multi-step workflows. - Deep expertise in at least one of the following: - Evaluation infrastructure, benchmark design, and automated grading. - Agent harnesses, tool interfaces, and reusable skills for complex workflows. - Realistic test environments or simulations spanning multiple systems, datasets, permissions, and stateful workflows. - Model selection, routing, and inference efficiency, including context management and caching. - Strong experimental and statistical judgment, including choosing representative tasks, defining meaningful success criteria, diagnosing failures, and distinguishing real improvements from noise. - Sound technical judgment in balancing task quality, latency, reliability, and cost, with a track record of translating evaluation findings into shipped improvements. - A track record of leading ambiguous, cross-team initiatives from architecture through production adoption, setting technical direction, and raising engineering quality through mentorship. If you’re excited about this role, we encourage you to apply even if you don’t meet every qualification. We value demonstrated impact, deep ownership, and the initiative to turn ambiguity into results.

Location: San Francisco; Seattle; New York City ABOUT THE ROLE The Enterprise Agent Evals & Efficiency team makes Perplexity's agents better at completing real work across enterprise connectors, tools, and data. Our goal is to help agents choose the right tools, use the right context, and complete complex workflows reliably, with less time and compute. This team owns the improvement loop: define what successful work looks like, build evaluations that measure it, understand where agents fail, and ship changes that improve outcomes. This is a hands-on engineering role with end-to-end ownership, from building realistic enterprise evaluation environments to shipping improvements across the agent harness, tools, and skills. You'll create complex scenarios spanning connected systems, data, and permissions, use them to identify where agents struggle, and improve the tools agents call, the skills they follow, the context they receive, and the models they use. Working with AI Research, Connectors, and Platform teams, you'll turn experimental results into measurable gains in task success, latency, and cost per successful task. The goal is not just to measure agents, but to make more enterprise work reliable and practical to automate. KEY RESPONSIBILITIES - Build realistic enterprise evaluation environments spanning connected applications, representative datasets, user roles, and permissions, with complex, multi-step workflows and reproducible system state. - Own evaluation infrastructure, task suites, and automated graders that measure how well agents complete enterprise work, calibrated against human judgment and grounded in end-to-end outcomes. - Analyze agent trajectories and production failures, identify root causes, and ship improvements across the agent harness, tools, and skills that measurably improve task success and reliability. - Develop reusable skills and improve tool descriptions, schemas, and result handling so agents can select the right tools, execute workflows correctly, and recover from errors with fewer unnecessary calls. - Build and evaluate model-selection and routing strategies that match each task to the right model and effort level, balancing quality, latency, reliability, and cost. - Improve agent efficiency through context management, skill loading, caching, and execution strategies that reduce wasted tokens and cost per successful task without compromising quality or safety. - Build regression gates and launch-readiness signals for model, connector, tool, skill, and harness changes, and validate that evaluation gains translate into better production outcomes. QUALIFICATIONS - 5+ years of software engineering or machine learning engineering experience, with a track record of owning complex production systems from design through deployment and operation. - Strong system design skills, including building reliable, scalable infrastructure for experimentation, evaluation, or data-intensive applications. - Strong proficiency in a backend language such as Python, Go, or Rust, and the ability to work effectively across a multi-language production stack. - Hands-on experience building, evaluating, or improving LLM-based applications or agents, including tool use and multi-step workflows. - Deep expertise in at least one of the following: - Evaluation infrastructure, benchmark design, and automated grading. - Agent harnesses, tool interfaces, and reusable skills for complex workflows. - Realistic test environments or simulations spanning multiple systems, datasets, permissions, and stateful workflows. - Model selection, routing, and inference efficiency, including context management and caching. - Strong experimental and statistical judgment, including choosing representative tasks, defining meaningful success criteria, diagnosing failures, and distinguishing real improvements from noise. - Sound technical judgment in balancing task quality, latency, reliability, and cost, with a track record of translating evaluation findings into shipped improvements. - A track record of leading ambiguous, cross-team initiatives from architecture through production adoption, setting technical direction, and raising engineering quality through mentorship. If you’re excited about this role, we encourage you to apply even if you don’t meet every qualification. We value demonstrated impact, deep ownership, and the initiative to turn ambiguity into results.

Location: Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Prague, Czechia; Lisbon, Portugal; Madrid, Spain CloudLinux is a global remote-first company. We are driven by our principles: do the right thing, employees first, we are remote first, and we deliver high-volume, low-cost Linux infrastructure and security products that help companies to increase the efficiency of their operations. Every person on our team supports each other and does what we can to ensure we all are successful. Check out our website for more information: cloudlinux.com We are currently hiring Staff Engineers for various projects within the Imunify360 product. Imunify360 is CloudLinux's multi-layer security suite for Linux web servers: antivirus and malware cleanup, WAF, IDS/IPS, Proactive Defense, patch management (KernelCare/Patchman), and IP/domain reputation. It runs on hundreds of thousands of shared, VPS and dedicated hosting servers, under cPanel, Plesk, DirectAdmin and standalone. The engineering org is 60+ engineers in squads covering backend, core, server agent, malware research, WAF/WebShield, and Patchman. Core services are Python; agents and performance-critical paths are Go and Rust. We run on Hetzner and our own bare metal. Check out our website for more information: https://imunify360.com/ Role Mission & Scope Most complex technical challenges in Imunify360 go beyond individual squads. A single detection change can impact the scanner, agent, UI, reputation backend, and release process. While squad leads own delivery within their teams, the Staff Engineer ensures technical alignment across the product. As a Staff Engineer, you'll be a hands-on technical leader working across the entire product , focusing on the most critical and high-impact challenges as priorities evolve. You'll drive technical decisions, improve architecture, establish engineering standards, and help teams build sustainable solutions. This is an individual contributor role , without direct people management responsibilities. You'll remain actively involved in development, spending approximately 30–50% of your time writing production code , rather than focusing solely on architectural design. While deep expertise in a particular area is valued, this role requires the flexibility to work across different components, collaborate with multiple squads, and tackle challenges wherever your expertise can make the greatest impact. What you will own Architecture and technical direction Own the end-to-end architecture of Imunify360 as a product: agent ↔ server ↔ cloud backends, data flows, and the contracts between them. Write and drive ADRs and RFCs for cross-squad changes. Run the Wed/Fri architecture sync together with the Technical Director. Keep a living tech-debt and risk register (security / compliance / uptime) with owners and dates, and get the top items funded on the roadmap. Hard cross-cutting problems (examples of what lands on your desk) Agent resource footprint on dense shared hosts: CPU, memory and I/O budgets for scanning, file monitoring (inotify/fanotify) and WAF, measured on real kernels, not benchmarks from elsewhere. Detection pipeline quality: false positive / false negative rates, signature and ML model rollout with staged gates and rollback. Upgrade and release safety across a large, heterogeneous fleet (CentOS/AlmaLinux/CloudLinux/Ubuntu, multiple control panels, old kernels). Backend scale and cost of reputation, ThreatIntel and telemetry services. Migrating hot paths from Python to Go/Rust where the data shows it pays off, and saying no where it doesn't. Engineering standards Set the bar for design reviews, code review, testing strategy, observability (Prometheus/Grafana/Sentry) and secure SDLC (OWASP ASVS, CIS Benchmarks as relevant). Lead or co-lead SEV1/SEV2 incidents when the cause is unclear or cross-squad; make sure the blameless post-mortem ships within 24 h and its actions get closed. Drive the AI-augmented SDLC in practice: show what good agentic coding looks like (prompt specificity, context provision, review quality, tool boundaries) on real changes, not slides. People multiplier (no direct reports) Mentor senior engineers and squad leads toward staff-level scope. Act as the technical bar-raiser in hiring loops for senior and lead roles. Be the engineering voice in product and sales discussions: estimates, feasibility, and no dated customer commitment without a written squad-lead estimate. Key Milestones 30 days: Shipped at least one non-trivial change to production. Can explain the full request path from agent to backend. Has a personal list of top 10 technical risks. 90 days: Owns one cross-squad initiative with an approved ADR, a measured baseline, and a staged rollout plan. Published risk register reviewed in arch sync. 6 months: That initiative is in production with before/after metrics. At least one engineering standard (e.g. release gating, agent perf budget, design review template) is adopted by 3+ squads. 12 months: Recognized by squad leads as the default escalation point for cross-cutting technical decisions. At least one senior engineer grown into broader scope with your mentorship. Requirements Must have 10+ years building and operating production software, with 3+ years in a Staff/Principal/Tech Lead scope spanning several teams. Strong production experience in Python and at least one of Go or Rust . Deep Linux internals : processes, filesystems, networking, kernel interfaces (inotify/fanotify, eBPF or similar), packaging (RPM/DEB), systemd. Track record of designing and running distributed systems at scale: async backends, queues, databases, caching, failure modes. You have owned systems that run on customer machines (agents, daemons, on-prem software) and know what upgrade safety, backward compatibility and resource budgets mean there. Security mindset: threat modeling, secure coding, handling of untrusted input (files, HTTP traffic, user content). Writes clear, short technical documents and drives decisions in async, remote teams across time zones. Uses AI coding tools (Cursor, Claude, MCP-based tooling) daily and can judge their output critically. Nice to have Experience in server security: AV/malware detection, WAF (ModSecurity/Coraza), IDS/IPS, rootkit detection, CVE/patch management. Experience with WordPress and Node.JS web application hosting at scale ML in production for classification/detection, including evaluation and safe model rollout. Observability at scale: Prometheus, Grafana, Sentry, ClickHouse or similar. CI/CD ownership: GitLab CI, Jenkins, build systems for multi-distro packaging. Open-source contributions in security or systems tooling. Benefits What's in it for you? Stay hands-on technically while owning complex, high-impact engineering work. Work on challenging Linux, automation, and cybersecurity problems across mature and newer products. Influence technical direction and make meaningful architectural decisions. Work independently with a high level of ownership and autonomy. Fully remote work with flexible working hours. 24 paid vacation days, 10 national holidays, and unlimited sick leave. Private medical insurance compensation. Co-working and gym/sports reimbursement. Education budget. Opportunity to receive a reward for innovative ideas that the company can patent. By applying for this position, you consent to the processing of your personal data as described in our Privacy Policy ( https://cloudlinux.com/candidate-privacy-notice ), which provides detailed information on how we maintain and handle your data.

Location: Belgrade, Serbia Sperasoft is seeking a Material Artist to integrate with our art team. You will partner with art and design teams to develop compelling materials. Your role involves creating materials that meet our technical specifications and adapting stock textures, such as from Megascans, to fit our production pipelines. If you excel at materials creation, delivering production-ready assets, and adhere to strict technical requirements, this is the perfect opportunity for you. Responsibilities Develop high-quality benchmark materials utilizing Substance Designer, Substance Painter, ZBrush, or other preferred DCC tools. Ensure all materials maintain compatibility and interchangeability within the project. Collaborate closely with Level Artists to create user-friendly and convenient materials for level construction. Work with the Lead to verify materials are correctly integrated and function as intended with project-specific shaders, including features like tinting and height blends. Uphold the integrity and uniformity of the material library. Independently research, evaluate, and implement the latest industry trends and material creation techniques. Demonstrate the ability to select the most appropriate software for any given task, without a strict focus on Substance Designer. Be prepared to pivot to simple prop modeling tasks when material creation assignments are not immediately available. Requirements Essential Skills & Experience: Deep and highly proficient knowledge of Substance Designer. Knowledge of PBR (Physically Based Rendering) material standards. Experience in creating textures for complex semi-procedural shaders. Understanding of material blending systems and material optimization for real-time engines. Proven ability to produce materials in a wide variety of styles and themes, with a good eye for appropriate stylization and detail. At least one shipped title within the CG industry. Proficiency with Maya or a similar Digital Content Creation (DCC) tool is a plus. Soft Skills & Communication: Autonomy and independence. Good communication skills. Intermediate English level or higher Benefits Working with an international team of world-class professionals on exciting and challenging projects Learning & Development opportunities – mentoring, lectures, participation at industry conferences and events Medical Care package Sensibly flexible working hours Breakfasts, snacks and fruits available during the day, tea and coffee machines

Location: Yerevan, Armenia Sperasoft is seeking a Material Artist to integrate with our art team. You will partner with art and design teams to develop compelling materials. Your role involves creating materials that meet our technical specifications and adapting stock textures, such as from Megascans, to fit our production pipelines. If you excel at materials creation, delivering production-ready assets, and adhere to strict technical requirements, this is the perfect opportunity for you. Responsibilities Develop high-quality benchmark materials utilizing Substance Designer, Substance Painter, ZBrush, or other preferred DCC tools. Ensure all materials maintain compatibility and interchangeability within the project. Collaborate closely with Level Artists to create user-friendly and convenient materials for level construction. Work with the Lead to verify materials are correctly integrated and function as intended with project-specific shaders, including features like tinting and height blends. Uphold the integrity and uniformity of the material library. Independently research, evaluate, and implement the latest industry trends and material creation techniques. Demonstrate the ability to select the most appropriate software for any given task, without a strict focus on Substance Designer. Be prepared to pivot to simple prop modeling tasks when material creation assignments are not immediately available. Requirements Essential Skills & Experience: Deep and highly proficient knowledge of Substance Designer. Knowledge of PBR (Physically Based Rendering) material standards. Experience in creating textures for complex semi-procedural shaders. Understanding of material blending systems and material optimization for real-time engines. Proven ability to produce materials in a wide variety of styles and themes, with a good eye for appropriate stylization and detail. At least one shipped title within the CG industry. Proficiency with Maya or a similar Digital Content Creation (DCC) tool is a plus. Soft Skills & Communication: Autonomy and independence. Good communication skills. Intermediate English level or higher Benefits Working with an international team of world-class professionals on exciting and challenging projects; Learning & Development opportunities – mentoring, lectures, participation at industry conferences and events; Medical Care package; Sensibly flexible working hours; Breakfasts, snacks and fruits available during the day, tea and coffee machines; Additional benefits – care bonus to cover health, educational and safety needs.

Location: Remote - United States Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From “How do I fix a leaky faucet?” to “What should I watch next?”, the Answers Relevance team leverages AI to connect people with the real-world wisdom of Reddit communities. We’re hiring Senior and Staff Machine Learning Engineers to make Answers smarter, more relevant, and more helpful. Our SOTA AI systems helps surface the right conversations, but the heart of Answers is still people helping people ~ Redditors sharing what they know with each other! What You’ll Work On You’ll lead ML projects from idea to production, partnering with Product, Data Science, and Engineering across recommendations, search, messaging, and AI systems. You don’t need experience in every area listed below. A background in a few may make you a great fit! Recommendations, search, content understanding, or large-scale data and ML infrastructure SOTA large scale RecSys Foundational retriever models RAG systems, retrieval methods, LLM evaluation, or production LLM features Agentic AI, such as tool calls, MCPs, multi-agent systems, or orchestration Self-hosted open-weight models such as Qwen or Llama; prompt engineering, fine-tuning, post-training, or prompt optimization Improving grounding, instruction following, and hallucination; evaluating retrieval and generation quality Scaling LLM systems, including latency, caching, and cost; safety measures for prompt injection and unsafe inputs Defining product success metrics, feedback loops, and evaluation approaches, including human review What You’ll Do Build and improve large-scale ML systems, including retrieval and ranking, embeddings, and LLM-powered features Set technical direction and deliver measurable user and business impact Work with partner teams to shape product and technical roadmaps Evaluate new AI/ML approaches and support responsible LLM development Mentor engineers and contribute to long-term ML planning Required Qualifications 4–12 years of experience building, deploying, and operating ML systems in production Broad knowledge of ML fundamentals and experience applying them in production; expert-level modeling depth isn’t required Experience developing and productionizing models with TensorFlow, PyTorch, or Hugging Face Transformers Experience writing production-quality code, including testing, evaluation, and monitoring; Python experience required, Golang experience a plus Experience with ML systems such as data pipelines, feature engineering, model training and serving, and production monitoring Strong communication and collaboration skills, and a record of delivering measurable product impact Benefits Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave #LI-Remote Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $216,700 — $322,000 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Location: London Workplace: Hybrid ABOUT BEAMERY With the rapid expansion of AI and automation, the future of work has never been more challenging for organizations. Beamery’s unique jobs, skills and tasks data platform helps organizations navigate these challenges and make more informed decisions across Talent Lifecycle Management. Our solutions power recruitment, mobility, upskilling, diversity, work architecture and workforce planning for some of the world’s most forward-thinking companies. We believe that where you work is much more than just a job. Millions of people are being left behind every day in their careers, and we’re on a mission to fix this by creating equal access to meaningful work, skills and careers for all. We are an equal opportunity employer committed to building a representative Beamery, creating an equitable, inclusive and engaging environment for our people. So, what are you waiting for? Join us and help us transform the future of work once and for all. What’s ahead — and why it’s an exciting time to join the team:  Building agentic AI with advanced orchestration (multi-agent workflows, tool use, evaluation loops) that turns complex talent data into decisions. - Training and fine-tuning our own models on unique data about jobs, skills and tasks, and benchmarking them against the best in our market. - Joining an award-winning AI team (ACM RecSys Challenge 2026) that turns new ideas into innovative solutions with scientific rigour. - Sharing our work and learning from industry peers through challenges, conferences and publications. But it’s not all about creating high-quality products, we also very much value the company culture we have worked hard to create; built on trust, empathy & honesty ensuring our workforce is able to bring their full selves to work. ABOUT THE TEAM The Data Science team builds the AI models behind Beamery's Data Foundry, the common data and AI layer powering all Beamery products, and works hand in hand with the product teams to empower their features with AI (such as Talent Match and recommendations). At the heart of our work is the Beamery taxonomy, the language of work (skills, role titles and tasks) that every Beamery product speaks, built on more than a decade of research and kept current by the Taxonomist, our agentic system, so it reflects the real pulse of the job market. We combine LLMs, Transformers and embedding models with classic machine learning, always choosing the best balance between quality, latency and cost. We see ourselves as pioneers in AI for HR Tech: in 2026 the team finished 1st in the industry track and 2nd overall out of 40 teams at the ACM RecSys Challenge. ABOUT THE ROLE You will join the team as a Senior Data Scientist and own our matching, recommendation and taxonomy mapping models, working closely with the product engineering teams that bring them to our customers. You will be responsible for the full cycle of your work, from defining how quality is measured and designing the models to running robust scientific experiments, tracking technical and business metrics, and writing and reviewing code. You will also advise the product teams on how to implement AI features with an evaluation-first mindset, and keep pushing the boundaries of Beamery's AI. KEY RESPONSIBILITIES - Build, improve and own our matching and recommendation models (e.g. candidate to job matching, internal mobility recommendations etc.). - Apply an evaluation-first mindset: build golden datasets from real recruiter and user decisions, define quality metrics, design and run robust scientific experiments, track and report both technical and business metrics (using LLM-as-a-judge and human feedback), and communicate results and metrics clearly to the product teams. - Write clean and tested Python code using AI coding agents (such as Claude Code or Codex), and review code with care, both from your peers and from AI agents, so the team's codebase stays consistent, readable and safe. - Follow recent research (e.g. LLMs, embeddings, recommender systems, explainability, model evaluation) and bring the most relevant ideas into our product roadmap. - Document your work and present it clearly to product, engineering and business stakeholders, and contribute to the team's research visibility (such as challenges, conferences and publications). REQUIRED SKILLS AND EXPERIENCE - A minimum of 2-4 years of experience as a Data Scientist, Senior Data Scientist (or a similar role) in an AI startup or an AI team, with a dynamic environment and a variety of use cases. - Strong experience with recommender systems, search, ranking or matching (e.g. learning-to-rank, scoring models, Gradient Boosting Trees, embedding models). - Fluency in evaluation: designing datasets and quality metrics (such as AUC, NDCG, precision and recall), and using LLM-as-a-judge and human feedback. - A strong foundation in NLP and GenAI, including Transformers, LLMs and embedding models (training or fine-tuning them is a strong plus). - High-quality software development skills in Python (clean design, testing, packaging, maintainable code etc.), practical knowledge of CI/CD, and hands-on experience with agentic coding, with the judgement to know when AI-generated code is right or wrong. - The ability to simplify complex problems, to work with ambiguity, and to collaborate with data scientists, engineers, product managers and business stakeholders. - A Master's degree or PhD, or equivalent experience, in Computer Science, Data Science, Mathematics or a related field. NICE TO HAVE - Experience with explainable or conversational recommendations. - Experience in HR Tech, talent acquisition or workforce data. - Publications, open-source contributions or strong results in ML challenges (e.g. RecSys, Kaggle). WHAT WE OFFER - The chance to own core AI models that are used by some of the world's most forward-thinking organisations. - A team that publishes, competes and is recognised in the research community, while shipping real products. - Work with the latest AI technologies and tools, including AI coding agents as part of the daily workflow. - Close collaboration with experienced data scientists, QA, platform and product engineers. At this time, we are unable to provide Visa sponsorship for this role. Beamery is for Everybody. Diversity and open expression are fundamental to us. We acknowledge the challenges in our industry and strive to develop an inclusive culture where everybody can contribute. We are dedicated to creating an inclusive environment for everyone, regardless of ethnicity, religion, color, sexual orientation, gender identity, race, national origin, age, disability status, or caregiver status. If, for whatever reason, you need us to make reasonable adjustments and adaptations to our recruitment process, please email accommodations@beamery.com Visit our Diversity, Equality and Inclusion page https://careers.beamery.com/life-at-beamery/diversity-equality-inclusion/to learn more about progress and commitments.

Location: San Francisco, CA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Mexico (Remote) Workplace: Remote ABOUT THE OPPORTUNITY We are looking for experienced Government professionals to support a cutting-edge AI benchmarking project focused on Mexican public sector environments. As a Subject Matter Expert, you will create and review realistic, high-quality scenarios that reflect operational, administrative, compliance, public safety, social service, and community engagement challenges across government institutions. Your expertise will help ensure AI systems understand and respond accurately within highly regulated and citizen-focused environments. WHAT YOU’LL DELIVER - Design realistic government scenarios in Spanish or English grounded in Mexican public sector contexts (e.g., administrative operations, social services, compliance reviews, public safety coordination, community programs, citizen service delivery) - Adapt structured evaluation rubrics for operational problem-solving, policy interpretation, case management, and decision-making tasks - Review AI and human-generated responses for factual accuracy, procedural compliance, cultural appropriateness, and operational realism - Contribute to “gold standard” solutions reflecting public sector and government best practices QUALIFICATIONS - Native Spanish with strong professional English proficiency - 5+ years of professional experience in government, public administration, social services, public safety, compliance, or community programs - Background in one or more relevant roles: Administrative Services Management, Child/Family/School Social Work, Compliance & Regulatory Affairs, Police or Public Safety Supervision, Recreation & Community Services, or related government functions - Strong understanding of public sector workflows, operational procedures, regulatory requirements, and Mexican government environments WHY COLLABORATE WITH LILT? - Your schedule, your rules. As an independent contractor, work when you want, as much or as little as you want. No fixed hours, no check-ins, no micromanaging. - Get paid quickly and fairly. We respect your time and your expertise. Competitive rates, prompt payments, no chasing invoices. - Work on projects that actually matter. Contribute to cutting-edge AI and language technology that is shaping how humans and machines communicate. - Be part of something bigger. Join a global community of linguists, subject matter experts, and language professionals who are advancing human knowledge together. - Grow without limits. As a Lilt contractor you get access to diverse, innovative projects that expand your portfolio and sharpen your skills across industries and domains. - Have fun doing what you love. Bring your language skills to life on projects that are as interesting as they are impactful. What to Consider Before Applying - Not ideal as a full time job or primary income source. Work availability fluctuates with project demand, making this better suited as a supplemental income stream. As a 1099 contractor, you won't receive benefits such as health insurance, paid time off, or retirement contributions, and hours are not guaranteed. - Requires reliable availability and commitment. Once you accept a task, we expect quality work and on-time delivery. Most tasks require a minimum of 2 hours per day or 10 hours per week. If your schedule is unpredictable, this may not be the right fit. - Geographic restrictions may apply. We cannot engage contractors in regions subject to international embargo or sanctions. As a 1099 contractor, you are solely responsible for your own tax obligations. We recommend consulting a tax professional before engaging. HOW TO JOIN OUR EXPERT COMMUNITY Step 1: Apply with the English-language version of your Resume/C.V. highlighting your expertise in the domain. Step 2: Register in Tipalti to ensure a smooth payment process. Step 3: Finalize onboarding and join the Production team. AI is changing how the world communicates — and LILT is leading that transformation. LILT's mission is to make the world's information available to everyone, no matter the language they speak. Join our global community who thrive on innovation and excellence. Our collective knowledge, uniqueness, and skills deliver multilingual AI and human-verified services to Enterprises, Governments, and AI Developers around the world. Earn money. Have fun. Advance human knowledge. Work on diverse projects from anywhere, any time you want. Get paid quickly and fairly, and build your professional network in a supportive community—all through a streamlined application process tailored to your expertise. Information collected and processed as part of your application process, including any job applications you choose to submit, is subject to LILT's Privacy Policy at https://lilt.com/legal/privacy. At LILT, we are committed to a fair, inclusive, and transparent hiring process. As part of our recruitment efforts, we may use artificial intelligence (AI) and automated tools to assist in the evaluation of applications, including résumé screening, assessment scoring, and interview analysis. These tools are designed to support human decision-making and help us identify qualified candidates efficiently and objectively. All final hiring decisions are made by people. If you have any concerns, require accommodations, or would like to opt-out of the use of AI in our hiring process, please let us know at recruiting@lilt.com. LILT is an equal opportunity employer. We extend equal opportunity to all individuals without regard to an individual’s race, religion, color, national origin, ancestry, sex, sexual orientation, gender identity, age, physical or mental disability, medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other classification protected by applicable local, state or federal laws. We are committed to the principles of fair employment and the elimination of all discriminatory practices.

Location: Poland; Brazil; Peru; Sweden; Argentina; Romania; Cyprus; Czech Republic; Hungary; Mexico; Chile; Bulgaria Workplace: Remote BUILDING THE FUTURE OF OPEN FINANCE Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system. THE TEAM xStocks brings real-world assets on-chain as tokenized securities that are regulated, fully-backed, and tradable across multiple blockchains. Built within Payward, the xStocks team runs the infrastructure that issues, custodies, and settles these instruments across EVM, Solana, TON, Tron, and more. The platform moves real value, so security is not a feature bolted on at the end. It is a property of everything the team ships. This is a hybrid engineering and security seat built for someone who is genuinely both: you ship products alongside the engineering team when the security queue is quiet, and you own security work end to end when it lands. You report into engineering and partner closely with the Payward security, CISO, and compliance functions. THE OPPORTUNITY - Own security deliverables end to end. Coordinate and review smart-contract audits, respond to security questionnaires and due-diligence requests, support CISO and compliance initiatives, and turn findings into tracked, closed-out fixes. - Run offensive assessments against our own platforms. Pentest the web apps, APIs, backend services, and on-chain components, and build the tooling to do it repeatedly rather than once. - Continuously assess posture. Threat-model new and existing systems, review architecture and key-management flows, and maintain a living view of the risks and their mitigations. - Review code for security. Be a default reviewer on changes touching auth, cryptography, custody, signing, and fund movement across our chains. - Harden the platform. Improve secrets handling, IAM and least-privilege, dependency and supply-chain hygiene, and detection and logging for security-relevant events. - Secure custody and signing workflows. Review and harden transaction signing flows, MPC and HSM integrations, signer permissions, admin roles, approval policies, and break-glass procedures. - Contribute to product engineering. Pick up regular backlog work in TypeScript so you stay close to the codebase you are defending. - Support incidents. Help with detection, triage, response, and post-mortems, and feed the lessons back into the program. WHAT YOU BRING - An engineer first. You write and review production code comfortably, TypeScript and Node.js in our stack, and you understand systems deeply enough to break them. - Strong application and infrastructure security fundamentals. The OWASP-class vulnerability landscape, authn and authz, injection, SSRF, supply-chain risk, and secure design. You have done real offensive work in pentesting, vuln research, or red-team. - Applied cryptography and blockchain security. Symmetric and asymmetric primitives, hashing, signatures, and key management, plus the security model of EVM, Solana, and ideally TON or Tron. You can read a smart contract and reason about how it fails. - Ownership. You can take an ambiguous security item, an audit, a questionnaire, or an incident, and drive it to closure without being managed through it. - Clear communication. You can explain a risk to engineers, to a CISO, and to an auditor, and tailor the message to each. - An automation instinct. You do not want to catch the same issue manually twice, so you turn findings into tests, alerts, controls, documentation, or platform improvements. - Risk judgment. You can tell a theoretical issue from a material one, and communicate severity, likelihood, impact, and remediation tradeoffs clearly. NICE TO HAVE - Experience commissioning or remediating third-party smart-contract audits. - Familiarity with custody and signing infrastructure such as HSMs, MPC, and ForDefi-style providers, and with on-chain transaction security. - Cloud security depth in GCP or AWS, IAM, and container and runtime hardening. - Detection engineering and security observability, including SIEM and log-based detections. - Relevant certifications such as OSCP, OSWE, or GWAPT. - Prior work in a regulated fintech or tokenization/DeFi environment. Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis. Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution. We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. OUR COMMITMENT Payward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto. We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision. As an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws. Stay connected Follow us on X https://twitter.com/krakenfx Learn on the Kraken Blog https://blog.kraken.com/#:~:text=Enter%20your%20email%20address Connect on LinkedIn https://www.linkedin.com/company/kraken-exchange/ Candidate Privacy Notice https://www.kraken.com/legal/candidate-privacy-notice