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Location: San Francisco, CA | 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 As a Partner Marketing Manager for Global and Regional System Integrators at Anthropic, you'll own marketing for our highest-performing services partners. That includes Global Premier GSIs and the regional systems integrators (RSIs) closing the most partner-attached Claude deals. You'll report to the lead for Services Partner Marketing and work alongside each partner's Partner Account Manager. You'll build a joint marketing plan with each partner and run executive programs and joint announcements. You'll also turn partner deliveries into the customer proof our field needs. Partners are now attached to a large and fast-growing share of Anthropic's biggest enterprise deals. Your job is to make those partnerships deliver more every quarter. This role is hands-on. You'll think strategically about each partner relationship while running the campaigns, events and enablement yourself. Our scaled programs team supports the rest of the partner network. You'll focus on the partners where hands-on marketing moves the most business, and use those scaled programs where they fit. Key responsibilities Partner Marketing Strategy & Execution Own joint marketing plans for an assigned group of high-performing GSI and RSI partners, including quarterly planning, campaign calendars, and success metrics tied to target accounts and pipeline goals Develop joint campaigns and executive programs (CXO dinners, executive briefings, tentpole event activations) aimed at C-suite and senior decision-makers at enterprise customers Work with Partner Account Managers and the Alliances team to identify priority accounts and build account-based plays that leverage partner relationships Lead joint announcements from first draft through approvals, holding partner copy to Anthropic's messaging and brand standards Spot RSIs gaining traction and help them grow into higher tiers Content, Proof & Enablement Build joint value propositions, messaging, and enablement materials (pitch decks, solution briefs, case studies) that help partners position Claude with their enterprise clients Turn partner deliveries into proof: capture wins for sellers, and work with Customer Marketing to produce partner-led customer stories that meet each partner's tier requirements Get your partners carrying Anthropic's priority messages and launches, and build them into Anthropic's own campaigns with the Campaigns team Put the team's co-marketing guidelines, campaign kits and self-serve assets to work across your partners Program Management & Measurement Lead end-to-end execution with your partners, coordinating across product marketing, demand generation, field marketing, events and agencies Manage partner funding (MDF/co-op) for your partners and track ROI on joint investments Track and report partner-sourced and influenced pipeline, story output, and campaign ROI, with 30/60/90-day pipeline tracking on every activation Represent Anthropic at partner events, executive briefings, and industry conferences as needed Feed what works back into playbooks the scaled programs team can run across the wider partner network Minimum qualifications Have built and run partner marketing programs with Global System Integrators, major consulting firms, or regional systems integrators, and can shift between large firms and smaller, faster-moving ones Understand enterprise buyer journeys and can create marketing programs that resonate with C-level executives Are skilled at developing joint value propositions that articulate clear business outcomes for enterprise customers Build trusted relationships with partner marketing and sales teams, and can move a partner on priorities you don't control Can own a partner portfolio end to end within a team strategy, and know when to escalate Are highly organized and detail-oriented, and can run multiple partner plans at once without dropping quality Write clearly and precisely, with experience creating executive-level content and holding partner copy to a high bar Are data-driven and comfortable tracking marketing metrics, analyzing performance, and optimizing programs based on results Thrive in fast-paced, high-growth environments and are comfortable with ambiguity and changing priorities Have a "get it done" mentality and aren't afraid to jump in wherever needed to ensure program success Care about the societal impacts of AI and are excited about enabling enterprises to adopt AI responsibly Preferred qualifications Have 7+ years of marketing experience, with significant focus on partner marketing in B2B technology environments Experience in the AI/ML industry or working with AI-powered products Established relationships within the GSI, consulting or regional SI ecosystem Worked on the partner side, at a GSI or SI, in alliance or technology partner marketing Experience with tiered partner programs, including benefits and promotion reviews Experience producing partner-led customer stories with enterprise customers Understanding of enterprise AI use cases across different industries and functions Experience with CRM systems (such as Salesforce) and partner relationship management tools Used Claude or other AI tools to scale their own marketing work Role-specific policy: For this role, we expect all staff to be able to work from our San Francisco, Seattle, or New York office at least 3 days a week, though we encourage you to apply even if you might need some flexibility for an interim period of time for relocation. If based in Seattle or New York, this role will require traveling to the San Francisco office once a month. Additionally, this role may require up to 20% of the time for partner meetings and partner events. Deadline to apply: None. Applications will be reviewed on a rolling basis. 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: $255,000 — $320,000 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: Remote (United States | Canada) Workplace: Remote 1Password is growing. We’ve surpassed $500M in ARR and we’re continuing to accelerate, earning a spot on the Forbes Cloud 100 for four years in a row and teaming up with iconic partners like Oracle Red Bull Racing. About 1Password At 1Password, we’re building the foundation for a safe, productive digital future. Our mission is to unleash employee productivity without compromising security by ensuring every identity is authentic, every application sign-in is secure, and every device is trusted. We innovated the market-leading enterprise password manager and pioneered Unified Access Management, a new cybersecurity category built for the way people and AI agents work today. As one of the most loved brands in cybersecurity, we take a human-centric approach in everything from product strategy to user experience. Over 180,000 businesses, from Fortune 100 leaders to the world’s most innovative AI companies, trust 1Password to help their teams securely adopt the SaaS and AI tools they need to do their best work. If you're excited about the opportunity to contribute to the digital safety of millions, to work alongside a team of curious, driven individuals, and to solve hard problems in a fast-paced, dynamic environment, then we want to hear from you. Come join us and help shape a safer, simpler digital future. As a Senior Sales Engineer, you’ll act as a strategic technical advisor and trusted partner to our Enterprise sales team and customers. You’ll elevate sales efficiency and customer engagement by delivering deep technical expertise, tailored demos, and customer-specific insights throughout the sales cycle. Working closely with Account Executives, you’ll craft solutions that align 1Password’s capabilities with customer challenges and goals, navigating complex requirements, influencing technical stakeholders, and clearly articulating value from discovery to close. Experience with frameworks like MEDDPICC is a strong plus, as you’ll help qualify opportunities, define technical success criteria, and drive alignment within deals. This role requires agility and foresight, identifying opportunities, positioning our product suite, and ensuring proposed solutions meet technical needs. You’ll also collaborate cross-functionally with Product, Marketing, Sales Enablement, and Support to strengthen customer-facing resources. As a senior team member, you’ll mentor peers, sharing knowledge to uplevel both technical expertise and soft skills across the Solutions Engineering team. This role is a remote opportunity based in the US or Canada. ************ Must be based in MST or PST time zone ************** How we’re using AI today Across GTM, we’re transforming the way we work with AI. We expect every seller, CSM, and team member to become a power user, using it daily to work smarter and move faster. AI is already embedded in how we operate – from real-time coaching that sharpens how we sell and interact with customers, to surfacing insights that help us anticipate needs – creating more time for meaningful customer engagement. If you’re excited to reshape how you think, make decisions, and drive growth with AI, you’ll feel right at home here. What we're looking for: - 7+ years of experience in customer-facing technical sales roles within a B2B environment. - Proven track record of selling into Fortune-100 customers - Experience in a multi-product SaaS environment. - Proven track record in enterprise sales, with the ability to build relationships, influence stakeholders, and support closing complex deals. - Skilled at translating technical concepts for both technical and non-technical audiences. - Strong presentation, communication, and collaboration skills across sales, product, marketing, and support teams. - Ability to quickly learn, adapt, and navigate dynamic customer conversations and requirements. - Experienced with modern sales methodologies such as MEDDPICC for opportunity qualification and deal progression. - High degree of autonomy with the ability to manage multiple projects, maintain quality, and meet deadlines. - Willingness to mentor colleagues and share best practices to support team growth. - Ability to travel up to 25% - Familiarity with SaaS, security concepts, and developer technologies such as SCIM, CLI, SAML/SSO/OAuth, AD, API, SSH and AI (MCP). What you can expect: - Serve as the primary technical advisor/resource within 1Password’s Go-to-Market team. - Proactively scope and recommend tailored technical solutions and support that address both expressed and unspoken customer needs—aligning with use cases, business goals, and decision criteria for a variety of Enterprise customers including those on the higher end of the segment. - Build strong relationships & deliver technical coaching to Account Executives, empowering them to speak confidently about product capabilities and align messaging with customer challenges, use cases, and requirements. - Provide technical education to customers regarding the 1Password suite of products. USA-based roles only: The annual base salary for this role is between $124,000 USD and $180,000 USD, and is commission-eligible. This role is also immediate participation in 1Password's benefits program (health, dental, 401k and many others), utilization of our generous paid time off, an equity grant and, where applicable, participation in our incentive programs. Canada-based roles only: The annual base salary for this role is between $127,000 CAD and $184,000 CAD, and is commission-eligible. This role is also eligible for immediate participation in 1Password’s generous benefits program (health, dental, RRSP and many others), utilization of our generous paid time off, an equity grant and, where applicable, participation in our incentive programs. At 1Password, we approach each individual's compensation with a promise of fair market value and internal equity commensurate with experience and specific skill set. This posting is for an existing vacancy. Our culture At 1Password, we prioritize collaboration, clear and transparent communication, receptiveness to feedback, and alignment with our core values: keep it simple, lead with honesty, and put people first. You’ll be part of a team that challenges the status quo, and is excited to experiment and iterate in search of the best solution. That said, 1Password is not for everyone https://blog.1password.com/inside-the-culture-powering-1passwords-next-chapter/. Our work is demanding, we strive for excellence, and the pace is fast. We need people who are keen to take on challenging problems, who seek feedback to grow, and who are driven to make an impact. If you're looking for a place where you can settle into a comfortable routine, this might not be the right fit for you. We’re looking for individuals who are proven experts in their fields, as well as those who are highly adaptable, can thrive in ambiguity and through change, are curious, and above all deliver results. How we work with AI We are committed to leveraging cutting-edge technology—including AI—to achieve our mission. We also understand that thinking critically about AI in its current forms will help us create better solutions for our customers and ourselves with its future forms, which will help us continue to close the gap between security and privacy and achieve our mission. We want team members at all levels to take the approach of actively learning AI best practices, identifying opportunities to apply AI in meaningful ways, and driving innovative solutions in their daily work. Embracing the future of AI isn't just encouraged—it's an essential part of how we will be successful at 1Password. This approach extends to our hiring process—candidates are welcome to use AI tools responsibly and thoughtfully during the application process. Our approach to work We recognize the power of both in person collaboration and remote work. Some roles are designated as remote-first, with an expectation that individuals work from their homes majority of the time, while others are designated as in-office roles, with an expectation of being on-site on a regular basis. We recognize that certain roles benefit from regular, in-person connection to support collaboration, team cohesion, and customer engagement. For all roles, occasional travel may be required. This includes things like: department-wide offsites, quarterly department meetings and periodic customer events. These events are typically held in varying locations across Canada, USA and/or EMEA. In leadership roles, you can expect to travel once per month on average. Note: All go-to market roles will have an in-person onboarding in Toronto. What we offer We believe in working hard, and rewarding that hard work through our benefits. While not an exhaustive list, here is a glance at what we currently offer: Health and wellbeing 👶 Maternity and parental leave top-up programs 🩺 Competitive health benefits 🏝 Generous PTO policy Growth and future 📈 RSU program for most employees 💸 Retirement matching program 🔑 Free 1Password account Community 🤝 Paid volunteer days 🏆 Peer-to-peer recognition through Bonusly 🌎 Remote-first work environment *Some roles in our GTM team are currently being hired for in-person hybrid work in Toronto and Austin. These roles will specify on the posting. You belong here. 1Password is proud to be an equal opportunity employer. We are committed to fostering an inclusive, diverse and equitable workplace that is built on trust, support and respect. We welcome all individuals and do not discriminate on the basis of gender identity and expression, race, ethnicity, disability, sexual orientation, colour, religion, creed, gender, national origin, age, marital status, pregnancy, sex, citizenship, education, languages spoken or veteran status. Be yourself, find your people and share the things you love. Accommodation is available upon request at any point during our recruitment process. If you require an accommodation, please speak to your talent acquisition partner or email us at nextbit@agilebits.com and we’ll work to meet your needs. Remote work is a part of our DNA. Given that our company was founded remotely in 2005, we can safely say we're experts at building remote culture. That said, remote work at 1Password does mean working from your home country. If you've got questions or concerns about this, your talent partner would be happy to address them with you. Successful applicants will be required to complete a background check that may consist of prior employment verification, reference checks, education confirmation, criminal background, publicly available social media, credit history, or other information, as permitted by local law. 1Password uses artificial intelligence (AI) and machine learning (ML) technologies, including natural language processing and predictive analytics, to assist in the initial screening of employment applications and improve our recruitment process. See here https://www.ashbyhq.com/downloadables/ashby-bias-audit-08-2024.pdf for the latest third party bias audit information. If you prefer not to have your application assessed using AI/ML features, you may opt out by completing this form https://jobs.ashbyhq.com/1password/automation-notice. For additional information see our Candidate Privacy Notice https://1password.com/files/candidate-privacy-notice.pdf.

Location: US - Headquarters - Maryland - Columbia Who is Tenable? Tenable® is the Exposure Management company. Over 40,000 organizations around the globe rely on Tenable to understand and reduce cyber risk. Our global employees support 65 percent of the Fortune 500, 50 percent of the Global 2000, and large government agencies. Come be part of our journey! What makes Tenable such a great place to work? Ask a member of our team and they’ll answer, “Our people!” We work together to build and innovate best-in-class cybersecurity solutions for our customers; all while creating a culture of belonging, respect, and excellence where we can be our best selves. When you’re part of our #OneTenable team, you can expect to partner with some of the most talented and passionate people in the industry, and have the support and resources you need to do work that truly matters. We deliver results that exceed expectations and we win together! Applicants must be authorized to work for any employer in the U.S. without sponsorship. We are unable to provide sponsorship for work visas of any kind at the time of hire, or at any point during employment. This includes, but is not limited to: F1-OPT, F1-CPT, H-1B, TN, J-1, etc Your Role: As an AI Security Engineer on Tenable's Information Security team, you will help shape and mature our approach to securing AI both within Tenable's products and across the enterprise. AI introduces a new class of risk at the intersection of application security, cloud risk, and data governance, and you will be on the front lines of defining how we assess, test, and address it. You will apply hands-on AI engineering experience alongside deep product security and application security skills to design security controls for AI systems, lead assessments, and help engineering teams ship AI features securely. You will establish security standards for responsible AI use, influence decisions across the organization, and build tooling that raises our security posture. This role is ideal for a practitioner who has spent real time building with AI systems, looking under the hood of model internals, and wants to channel that experience into a security specialty. Your Opportunity: Design, implement, and maintain end-to-end security controls across the AI/ML lifecycle. Conduct safety research, inspecting model internals (e.g., detecting hidden deception, latent knowledge, or unwanted concepts across layers) as a core methodology. Perform AI safety assessments and threat modeling for AI/ML systems, identifying risks such as data poisoning, model evasion, model extraction, adversarial inputs, and integrity attacks. Design and implement robust security guardrails, controls, and boundary mechanisms for Large Language Models (LLMs), Small Language Models (SLMs), and open-source models. Act as a subject matter expert in AI safety research by inspecting model internals to detect hidden deception, latent knowledge, or unwanted concepts, ensuring highly transparent and aligned AI outputs. Continuously monitor the AI landscape for novel vulnerabilities and attack techniques, applying deep engineering experience to proactively defend enterprise AI deployments. Develop security reference architectures for AI deployment patterns, including MCP servers and agentic AI workflows and harnesses. Deploy controls to ensure model integrity, governance, and proper access control across models and feature stores. Build AI-driven tooling to strengthen cybersecurity posture across identity, incident management, vulnerability management, third-party risk, and emerging AI threat vectors. Perform AI safety, security assessments, threat modeling for AI/ML systems, identifying risks such as data poisoning, model evasion, model extraction, adversarial inputs, and integrity attacks. Collaborate with software engineers, and product teams to integrate security best practices into AI development, training, validation, and deployment processes. Drive Secure Software Development Lifecycle (SSDLC) and DevSecOps practices for AI-powered products, including threat modeling, security testing, penetration testing, and CI/CD pipeline security automation. Create security guidance, documentation, and training for internal engineering and development teams; develop metrics that drive desired security behaviors and outcomes. Research emerging trends in AI security and contribute to innovative solutions that support Tenable's AI initiatives. What You'll Need: 5 or more years of professional experience in information security or application security, with at least 1–2 years focused on securing AI/ML systems. Master’s Degree in Computer Science, Cybersecurity, Data Science, or a related field preferred; advanced degree preferred. Deep understanding of AI-specific security threats, including adversarial ML, data poisoning, prompt injection, model inversion, model evasion, and inference attacks. Strong coding experience in Python and/or Go. Experience with frameworks & libraries such as PyTorch, Hugging Face Transformers, TensorFlow or similar. Experience using specialized interpretability and probing tools/libraries such as TransformerLens, NNsight, Captum, or LIT (Learning Interpretability Tool). Familiarity with mechanistic & Architectural Concepts: inspecting internal model states, residual streams, attention patterns, activation steering, Sparse Autoencoders (SAEs), or feature attribution. Strong understanding of the ML/AI lifecycle and the security risks associated with each stage. Knowledge of cloud security platforms: AWS, Azure, or GCP including AI/ML-related services. Knowledge of data security principles, including encryption, masking, and tokenization. Knowledge of SSDLC, DevSecOps, and security testing practices, including SAST, DAST, SCA, and threat modeling. Knowledge of application security architecture best practices and patterns, including modern web applications, Docker, and microservices. Ability to work cross-functionally across engineering, product, and business teams. Strong written and verbal comAAmunication skills; able to clearly translate complex AI security risks for both technical and non-technical audiences. Strong problem-solving skills, attention to detail, and ability to lead projects with end-to-end ownership. Self-motivated and effective working independently and across distributed teams. And Ideally: Hands-on experience with AI red teaming, adversarial prompt testing, or LLM security assessments. Familiarity of AI security frameworks and standards, including OWASP LLM Top 10 and the NIST AI Risk Management Framework. Experience with application security assessment tools (Burp Suite, ZAP, Tenable WAS, etc). Familiarity with AI governance frameworks (EU AI Act, NIST AI RMF, etc.). Background in cloud security or large-scale SaaS product environments. Security certifications: CISSP, CSSLP, SANS GIAC, CEH, or AI-focused security certifications are a plus, but not necessary. #LI-Hybrid #LI-MS1 This is the base pay range for this position. Compensation for the role will depend on a number of factors, including the candidate's qualifications, skills, competencies, location and experience, and may fall outside of the range shown. Employees are also eligible for variable compensation in addition to base pay (commission for sales roles, bonus for non-sales roles), depending on company and individual performance. Tenable also offers a variety of comprehensive and competitive benefits which include: medical, dental, vision, disability and life insurance; 401(k) retirement savings with company match; an employee stock purchase plan; an employee referral program; flexible spending accounts; an Employee Assistance Program (EAP); education assistance; parental leave; paid time off (PTO); company-paid holidays; health and wellness events; and community programs. US Pay Range $122,500 — $163,166.67 USD We’re committed to promoting Equal Employment Opportunity (EEO) at Tenable - through all equal employment opportunity laws and regulations at the international, federal, state and local levels.  If you need a reasonable accommodation due to a disability during the application or recruiting process, please contact Recruiting@Tenable.com for further assistance. Tenable Data Consent Statement Tenable is committed to protecting the privacy and security of your personal data. Depending on your location, one or more of the following notices may apply to you: General Applicant Privacy Notice California Applicant Privacy Notice EU/EEA/UK Applicant Privacy Notice

Location: Singapore 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. The role: Network Engineering Manager APAC / Network Team Lead APAC We are looking for a Staff Network Engineer / Player-Coach Team Lead to lead the growth, deployment, and operational execution of our APAC Network Infrastructure emerging team . In this role, you will combine direct people leadership with high-end technical expertise. You will lead a growing regional team of 2–5 jun/mid-to-senior network engineers , driving their professional development, cross-project prioritization across our Global Network , and overall execution aligned with global business objectives, with a priority on the APAC Region and backbone network development across EU and the US for APAC. As a hands-on technical manager, you will remain deeply embedded in engineering execution, dedicating approximately 50–70% of your time to hands-on engineering during your first year, with the role evolving naturally alongside organizational scale and regional team expansion. Crucially, this position demands a high degree of operational autonomy . Due to the timezone differences, you will serve as the primary regional networking authority, bridging global network architecture defined with our EU/EMEA/the US engineering headquarters and regional execution across APAC colocation sites, cable landings, and data centers. This position is remote within the APAC region (with preferred hubs in India, Singapore), with regular visits to regional DC facilities and our European headquarters in Amsterdam. Your responsibilities will include: Team Leadership & People Management Deploy APAC network part and BackBone. Lead, mentor, and structurally develop a regional engineering team of 2–5 jun/mid-to-senior network engineers across the APAC. Own regional task planning, backlog prioritization, change review governance, and end-to-end execution within the team. Drive and owning the Launch and Deploy process of New DataCentre and Customer in it Drive technical coaching, and personalized career roadmaps for team members. Foster a disciplined culture of radical ownership, engineering excellence, comprehensive runbook documentation, and Git-driven automation. Act as the regional net escalation point for production network incidents Global Follow-the-Sun Alignment: Co-own operational hand-off workflows and shared incident coverage with EMEA (HQ in Amsterdam) and the US network teams to guarantee seamless, round-the-clock global production network stability and customer workability. Cross-Functional Alignment & Strategic Autonomy Serve as the primary regional network owner for the next teams in APAC: datacenter operations team, site expansion teams. EU/EMEA Coordination: Actively partner with the Global Network Architecture and R&D to adapt core architectural standards (Clos fabrics, SRv6, backbone routing policies) to APAC market realities and carrier ecosystems. Autonomously drive regional connectivity delivery: partner closely with Technical Program Managers (TPMs) on submarine cable systems, cross-border DCI circuits, local Internet Exchanges (IXs), and regional transit providers. Coordinate with HWaaS, Compute, and Cloud Platform engineering teams to guarantee timely site bring-up, Day-0 Out-of-Band (OOB) readiness, and high-throughput fabric availability for production workloads. Bridge the gap between global strategic roadmaps and autonomous local incident resolution, ensuring APAC operations execute reliably during EMEA off-hours. Technical Leadership & Hands-on Work (50–60%) Full Regional Network Ownership: Own and guarantee overall network infrastructure readiness, capacity, and availability across emerging APAC network infrastructure, ensuring alignment with HQ blueprints and processes Actively contribute to the design, deployment, and operation of massive data center fabrics and backbone infrastructure. Support and participate in the evolution high-performance Ethernet-based GPU cluster interconnects. Participate in complex, high-severity troubleshooting and root-cause analysis (RCA) for critical infrastructure incidents. Oversee and contribute to network automation pipelines, tooling, and telemetry/observability development. We expect you to have: Expert-Level Technical Background: Service Provider or/and Data Center Clos networks. BGP, IS-IS, Segment Routing (SR-MPLS / SRv6), and advanced traffic balancing Ethernet switching, EVPN-VXLAN architectures, and L3 VPNs. Leadership Experience: Proven track record as a Tech Lead, Lead/Staff Engineer, or People Manager leading mid-to-senior engineering teams. Vendor Ecosystem: Juniper, Arista, Cisco, NVIDIA It will be an added bonus if you have: Hands-on experience with GPU cluster, RoCEv2/ECN or InfiniBand networks Solid understanding of Public Cloud networking models and Software-Defined Networking (SDN) overlays. Proficiency in Python or Go for infrastructure automation and production tooling within Linux environments. 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: Zurich, Switzerland 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. The role As a Senior Machine Learning Engineer on the Applied AI team at Nebius Token Factory, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, quantization and model compression, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality and reliability. You will work with kernel and platform engineers to investigate serving problems, compare configurations, and resolve performance and quality regressions. You will evaluate model- and engine-level optimizations together with distributed inference designs, including request routing, scheduling, prefill/decode coordination, and multi-node GPU execution. Your improvements will be validated through reproducible benchmarks and safe production rollouts under real-world workloads. Your responsibilities Own optimization projects for specific model families, customer endpoints, or serving backends. Compare inference engines and recommend serving configurations suited to individual workloads. Diagnose model-quality and performance regressions during production rollouts. Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo. Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. For prefill–decode disaggregation (PDD), evaluate KV-cache transfer, worker placement, and capacity balancing to determine which workloads benefit from the architecture. Design and improve LLM request routers and scheduling policies that balance worker utilization, queueing, request characteristics, and KV-cache locality while meeting latency and reliability targets. Scale dense and mixture-of-experts inference across multiple GPU nodes. Select and tune tensor, pipeline, data, and expert parallelism—including wide expert parallelism (WideEP)—with attention to hardware topology, expert load balance, and communication overhead. Build reproducible benchmark harnesses measuring TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Validate architecture choices under representative traffic and consistent GPU budgets. Collaborate with GPU kernel and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Produce design documents, performance reports, rollout plans, and customer-facing technical explanations. Must-haves Strong engineering skills in Python and PyTorch. Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference. Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system. A strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost. Experience designing or optimizing distributed inference system architecture, with hands-on work in one or more areas such as request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism. Strong communication and collaboration skills across research, kernel, infrastructure, product, and customer teams. Nice-to-haves Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, or SmoothQuant. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or related inference acceleration methods. Experience supporting agentic workloads involving tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. Familiarity with CUDA or Triton; the role does not require kernel engineering to be the candidate’s primary specialization. Contributions to open-source projects such as vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, or KServe. Implementation experience with cache-aware request routing, PDD, or WideEP, including diagnosing communication bottlenecks and load imbalance. Familiarity with GPU communication libraries and interconnects, such as NCCL, NVLink, InfiniBand, or RoCE. 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: Amsterdam, Netherlands; Berlin, Germany; Israel; Remote - Europe 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. The role As a Senior Machine Learning Engineer on the Applied AI team at Nebius Token Factory, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, quantization and model compression, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality and reliability. You will work with kernel and platform engineers to investigate serving problems, compare configurations, and resolve performance and quality regressions. You will evaluate model- and engine-level optimizations together with distributed inference designs, including request routing, scheduling, prefill/decode coordination, and multi-node GPU execution. Your improvements will be validated through reproducible benchmarks and safe production rollouts under real-world workloads. Your responsibilities Own optimization projects for specific model families, customer endpoints, or serving backends. Compare inference engines and recommend serving configurations suited to individual workloads. Diagnose model-quality and performance regressions during production rollouts. Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo. Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. For prefill–decode disaggregation (PDD), evaluate KV-cache transfer, worker placement, and capacity balancing to determine which workloads benefit from the architecture. Design and improve LLM request routers and scheduling policies that balance worker utilization, queueing, request characteristics, and KV-cache locality while meeting latency and reliability targets. Scale dense and mixture-of-experts inference across multiple GPU nodes. Select and tune tensor, pipeline, data, and expert parallelism—including wide expert parallelism (WideEP)—with attention to hardware topology, expert load balance, and communication overhead. Build reproducible benchmark harnesses measuring TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Validate architecture choices under representative traffic and consistent GPU budgets. Collaborate with GPU kernel and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Produce design documents, performance reports, rollout plans, and customer-facing technical explanations. Must-haves Strong engineering skills in Python and PyTorch. Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference. Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system. A strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost. Experience designing or optimizing distributed inference system architecture, with hands-on work in one or more areas such as request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism. Strong communication and collaboration skills across research, kernel, infrastructure, product, and customer teams. Nice-to-haves Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, or SmoothQuant. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or related inference acceleration methods. Experience supporting agentic workloads involving tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. Familiarity with CUDA or Triton; the role does not require kernel engineering to be the candidate’s primary specialization. Contributions to open-source projects such as vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, or KServe. Implementation experience with cache-aware request routing, PDD, or WideEP, including diagnosing communication bottlenecks and load imbalance. Familiarity with GPU communication libraries and interconnects, such as NCCL, NVLink, InfiniBand, or RoCE. 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: Remote: India About Neo4j: Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems. It includes enterprise-ready knowledge graphs for accurate, explainable, and governed AI; the most comprehensive, trusted, and easy-to-deploy graph capabilities across any environment and data source; and an unmatched ecosystem trusted by 84 of the Fortune 100 and supported by the world’s largest graph community. Intelligence that works. Results that matter. Built to work everywhere and integrate with everything across every cloud for dynamic, personalized, and autonomous AI systems. We deliver quicker results, contextual knowledge, and solutions that impact customers and employees across the business. Our Vision: At Neo4j, we have always strived to help the world make sense of data. As business, society and knowledge become increasingly connected, our technology promotes innovation by helping organizations to find and understand data relationships. We created, drive and lead the graph database category, and we’re disrupting how organizations leverage their data to innovate and stay competitive. As our Senior Enterprise Account Executive, you will partner with executive leadership at top Indian organisations to unlock the power of data and AI using Neo4j’s industry-leading data intelligence platform. Why Top Revenue Leaders Choose Neo4j Category Creator & Market Leader: Backed by over $600M in funding ($325M Series F) at a $2B+ valuation, with $200M+ in ARR. Proven ROI: A recent Forrester Total Economic Impact™ study showed a 417% ROI for Neo4j customers. Enterprise Dominance: Trusted by 84% of the Fortune 100, including premier global financial services providers. The AI Wave: Neo4j sits at the center of the next major enterprise shift: combining Knowledge Graphs with LLMs (GraphRAG) to eliminate AI hallucinations in mission-critical environments. What You’ll Do Grow the India Market: Lead strategic account planning and execution across a high-value portfolio of large enterprise customers alongside high-growth greenfield prospects. Drive High-Impact Sales Cycles: Execute value-based, consultative sales strategies (MEDDPICC) across C-level, Line-of-Business, and Chief Data/AI Officer stakeholders. Land & Expand: Balance hunting new enterprise logos with driving workload expansion within established accounts—scaling adoption into new business units and high-value use cases. Partner with Cloud Ecosystems: Co-sell and align strategic account plans with local hyperscaler ecosystems (AWS, Microsoft Azure, Google Cloud) and Global Systems Integrators (GSIs). Orchestrate High-Performance Squads: Align internal Pre-Sales Engineering, Field Marketing, and Customer Success resources to qualify, scope, and deliver impactful Proofs of Value (POVs). Maintain Sales Rigor: Drive predictable forecasting and pipeline hygiene within Salesforce.com. What You’ll Bring Location Requirement: This role is only open to candidates based in Mumbai as you will be required to regularly meet with customers and partners located there. 10+ years of enterprise software sales experience, with a direct focus on selling complex data platforms, cloud infrastructure, analytics, or enterprise software into the India market. Proven track record of exceeding quotas and closing multi-year enterprise deals ($500k–$1M+ ACV) into major customers. Methodology mastery & expertise in structured sales frameworks (MEDDPICC, Challenger, Command of the Message) navigating complex multi-stakeholder deals. Strong existing relationships across C-suite executives, enterprise data architects, and line-of-business owners across large enterprise customers. Preferred Experience Experience selling modern cloud-native database platforms, distributed systems, analytics infrastructure, or AI/ML technologies. Experience co-selling alongside public cloud providers (AWS, Azure, GCP) and major consulting partners in India. Willingness to travel to meet with customers, partners and teams as required. Why Join Neo4j? Neo4j is, without question, the most popular graph intelligence platform in the world. We have customers in every industry globally, and our products are a proven product/market fit. Joining our team is an opportunity to shape the future of data and analytics. Below are just a few exciting facts about Neo4j. Neo4j is one of the fastest-scaling technology companies in this industry. It recently surpassed $200M in annual recurring revenue (ARR), doubling its ARR over the past three years. Raised the biggest funding round in database history ($325M Series F). Backed by world-class investors like Eurazeo, GV (formerly Google Ventures), and Inovia Capital, Neo4j has raised over $600M in funding and is currently valued at over $2Bn. This puts Neo4j among the most well-funded database companies in history. 84% of the Fortune 100 and 58% of the Fortune 500 use Neo4j. Examples include Boston Scientific , BT Group , Caterpillar , Cisco , Comcast , Department for Education UK , eBay , NBC News , Novo Nordisk , Worldline , and others . Co-founder and CEO Emil Eifrem has built an amazing culture that prides itself on relationships, inclusiveness, innovation, and customer success. Countless industry awards . Massive enterprises and individual developers/data scientists love Neo4j. A strong sense of community and ecosystem is built around the platform. A recent Forrester Total Economic Impact™ Study cited Neo4j as delivering 417% ROI to customers. Research shows that members of underrepresented communities are less likely to apply for jobs when they don’t meet all the qualifications. If this is part of the reason you hesitate to apply, we’d encourage you to reconsider and give us the opportunity to review your application. At Neo4j, we are committed to building awareness and helping to improve these issues. One of our central objectives is to provide an inclusive, diverse, and equitable workplace for everyone to develop their potential and have a positive, career-defining experience. We look forward to receiving your application. Neo4j Values: Neo4j is a Silicon Valley company with a Swedish soul. We foster collaboration and each of us is empowered to contribute and put our innovative stamp on projects. We hire candidates who reflect the following Neo4j core values: (we)-[:VALUE]->(relationships) (we)-[:FOCUS_ON]->(userSuccess) (we)-[:THRIVE_IN]->(:Culture {type: [‘Open’, ‘Inclusive’]}) (we)-[:ASSUME]->(:Intent {direction:’Positive’}) (we)-[:WELCOME]->(:Discussions {nature: ‘IntellectuallyHonest’}) (we)-[:DELIVER_ON]->(ourCommitments) Neo4j is committed to protecting and respecting your privacy. Please read the privacy notice regarding Neo4j's recruitment process to understand how we will handle the personal data that you provide. More information at www.neo4j.com . ©2026 Neo4j, Inc., Neo Technology®, Neo4j®, Cypher®, Neo4j Bloom™, Neo4j Graph Data Science Library™, Neo4j® Aura™, and Neo4j® AuraDB™ are registered trademarks or a trademark of Neo4j, Inc. All other marks are owned by their respective companies.

Location: San Francisco, CA | New York City, NY | 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 We are looking for software engineers to help build safety and oversight mechanisms for our AI systems. As a software engineer on the Safeguards team, you will work to monitor models, prevent misuse, and ensure user well-being. This role will focus on building systems to detect unwanted model behaviors and prevent disallowed use of models. You will apply your technical skills to uphold our principles of safety, transparency, and oversight while enforcing our terms of service and acceptable use policies. Responsibilities: Develop monitoring systems to detect unwanted behaviors from our API partners and potentially take automated enforcement actions; surface these in internal dashboards to analysts for manual review Build abuse detection mechanisms and infrastructure Surface abuse patterns to our research teams to harden models at the training stage Build robust and reliable multi-layered defenses for real-time improvement of safety mechanisms that work at scale You may be a good fit if you have: A Bachelor’s degree in Computer Science, Software Engineering or comparable experience Proficiency in Python and Typescript An ability to work across the stack Strong communication skills and ability to explain complex technical concepts to non-technical stakeholders Strong candidates may also: 8+ years of experience in a software engineering position Have experience with integrity, spam, fraud, or abuse detection and mitigation Have experience building trust and safety detection mechanisms and intervention for AI/ML systems Have experience with prompt engineering, jailbreak attacks, and other adversarial inputs Have worked closely with operational teams to build custom internal tooling 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: AU: Melbourne: (260 Burwood Rd) Workplace: Hybrid At Xero, we’re here to supercharge small businesses. By making small business more efficient every day, connecting them with big business technology and empowering a community behind them, their potential is limitless. When that happens, we’re not only helping small business, we’ll be building a stronger economy that can change the world.At Xero, we’re here to supercharge small businesses. By making small business more efficient every day, connecting them with big business technology and empowering a community behind them, their potential is limitless. When that happens, we’re not only helping small business, we’ll be building a stronger economy that can change the world. THE ROLE & TEAM Product Data Science & Engineering is a portfolio team within Product & Technology. Partnering across Product, Engineering, Design, Marketing, and Commercial leadership, we set Xero’s data science strategy across broad product areas and operate in an AI-native way—using AI to transform how we work, not just what we ship. The team co-own Xero's metrics stack, experimentation strategy, and enterprise data architecture and builds AI-powered customer experiences including the Jax chatbot and agentic workflows—in partnership with Anthropic, OpenAI, and Google. They also lead AI measurement and productionisation. We work in an AI-native way. That means AI changes how data science itself gets done here, not just what we ship. This is a new strategic leadership role created to scale Xero's data science and AI capabilities. You will take full ownership of the data science, analytics, ML, and AI strategy and execution across Accounting & Bookkeeping, Small Business, and AI products. This role combines strategic product vision (including Jax, agentic workflows, and AI-powered features) with platform thinking across the metrics stack, experimentation, data quality, and governance, driving enterprise-level impact across every product team. WHAT YOU’LL DO - You will translate company-level objectives into strategic sub-objectives across analytics, ML, AI products, and experimentation, elevating both the quality of decisions and the leverage Xero gains from its data and AI investments. - Lead, mentor, and grow a team of data scientists, analytics engineers, ML engineers, and managers, scaling the function from ~12 to ~30. - Design team structures and operating models that preserve high throughput across multiple locations and time zones. - Operate as an AI-native leader, driving how AI changes how data science operates and ships value across Xero. - Act as the primary data partner to Product & Technology, advising C-suite and SLT on data utilization, retention strategy, new data domains, and AI investment decisions. - Represent data science in executive forums and partner with Product, Engineering, Design, Marketing, Commercial, Legal, Privacy and Risk on strategy, prioritisation and governance. - Own AI governance, model risk, data quality, privacy posture, and experimentation integrity across the portfolio, ensuring company-wide compliance and integrity. - Drive behavioral, technical, and cultural standards across the vertical. Own and improve global hiring practices, build talent maps, and coach senior leaders and individual contributors to raise the bar across Xero. - Proactively identify delivery and strategic risks, designing robust organizational processes that yield consistent, high-quality outcomes for Xero. WHAT YOU’LL BRING WITH YOU We welcome candidates from analyst, scientist, engineer, or AI/ML-focused backgrounds—a linear data science career is not required. What matters most is that you have operated, shipped, and led at scale within a fast-paced technology environment. As a self-starter and executive partner, you will shape and lead strategic agendas alongside C-suite and senior leadership, driving throughput across multi-location, global teams. You excel at bringing structure to complex cross-functional challenges, managing ambiguity, and maintaining a strong "Xero-first" mindset focused on delivering strategic value and accountable execution. - Ability to act as a proactive self-starter and executive partner, shaping strategic agendas with C-suite and SLT, presenting to board-level leadership, and influencing key investment decisions. - Proven ability to manage ambiguity, bring structure to complex cross-functional challenges, sustain throughput across multi-timezone teams, and hold stakeholders accountable to a "Xero-first" standard. - Skilled in leading managers, coaching leaders into senior roles, developing high-performing teams, and constructively escalating strategic risks when necessary. - Experienced in navigating teams through significant scale-ups, operating model transformations, and continuous evolution in fast-paced environments. - Practical experience leveraging frontier-model platforms (e.g., Anthropic, OpenAI, Google) to build agentic workflows, chatbots, and AI-powered products, as well as transforming team operating models using AI workflows. - Substantial experience leading data science, ML, or analytics functions, with a proven track record of managing other people leaders across multi-team structures. - Extensive experience building and running enterprise-scale data science functions, AI/ML productionisation, AI governance, model risk frameworks, and experimentation integrity. - Deep technical fluency across applied ML, analytics engineering, instrumentation, eventing, and schema governance, with a strong engineering mindset focused on building scalable platforms and reusable architectures. - Prior exposure to SaaS or small business segments, with a track record of driving scale, data quality, and privacy compliance. - Technical bachelor's or master's degree (PhDs welcome for applied, high-impact organizational challenges), supported by public thought leadership or open-source contributions in data or AI. WHERE AND HOW YOU CAN WORK This role is based in Sydney or Melbourne with a flexible hybrid working setup, spending 1 to 2 days per week in the local office to connect and collaborate. Apply even if your experience isn't a perfect match! At Xero, we hire based on your skills, passion, and the unique perspective you can bring to enhance our culture and team.

Location: Remote - Bengaluru, India MinIO is the data and memory foundation for enterprise AI. Built for the speed, scale, and economics that AI and analytics demand, AIStor and MemKV unify every layer of the data stack, from agentic and inference context memory to tables and objects across core, edge, and cloud. Trusted by 77% of the Fortune 100, MinIO is redefining how AI factories, intelligent applications, and autonomous agents secure, persist, and unlock the full value of their data. This is the senior product role on a small, high-leverage product team. You'll own key pieces of the product line — from how we position it, to how we price it, to how the field sells it. You'll work closely with engineering and be the person Marketing and Field teams looks to for the answer to "why us, why now”. We need a product leader who lives close to customers and to revenue, who can hold a deeply technical conversation with multiple stakeholders - from technical buyers to executive, and who treats GTM as a first-class part of the product — not something that happens after the product is "done." What You Will Do: Growth strategy for our Data Intelligence products — where we play, who we sell to, how we win, and the sequencing of bets that gets us there Partnerships — work closely with key partners to develop ground breaking products The customer and buyer motion — being in front of inference platform teams, data platform leads, and the buyers who decide. Joining strategic deals. Running the win/loss loop and feeding it back into strategy. Positioning and narrative — partnering with Marketing on how the AI stories land in market, what we publish, and how a first-time buyer encounters MinIO Field partnership — building the assets sellers actually use, supporting the deals that matter, making sure the field can run an inference-buyer or lakehouse-buyer conversation confidently Pricing and packaging — how Data Intelligence products are sold, how the AI and Analytics motions are priced differently if they should be, how we capture value as workloads grow Competitive intelligence — knowing the AI training inference competitive landscape, and shaping how we differentiate Partner GTM for AI — the AI ecosystem is partner-heavy, and unlocking it is part of the role Influence on the product — through customer signal, pipeline data, and competitive insight you will be the most informed external voice in the room to influence direction. Your Skills and Experience: 8+ years in product management, with at least 3 years on infrastructure, data, or AI/ML products sold to technical buyers Demonstrated fluency in modern AI and analytics stacks — you don't need to have trained a foundation model, but you should be conversant in how training and inference pipelines actually consume storage, key tradeoffs and why it matters, and how data platform teams make build/buy decisions A track record of working closely with field teams on real revenue — you've sat in on deals, you've built enablement that sellers actually used, you've changed a deal's outcome with a well-timed product conversation Partnership with marketing on crafting compelling narratives, presented at external conferences, webinars Strong analytical instincts — you reach for data to test your hypotheses, not to confirm them The ability to write clearly and think rigorously in writing. Most of the work that compounds in this role is written Excellent partnership with engineering — you've earned engineers' trust before by being technically credible and by making prioritization defensible Real comfort with pricing and packaging decisions  — if you've owned or co-owned them, not just commented on them, that's a big plus Equal Opportunity Policy (EEO) MinIO is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law.

Location: London, United Kingdom 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. The role As a Senior Machine Learning Engineer on the Applied AI team at Nebius Token Factory, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, quantization and model compression, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving quality and reliability. You will work with kernel and platform engineers to investigate serving problems, compare configurations, and resolve performance and quality regressions. You will evaluate model- and engine-level optimizations together with distributed inference designs, including request routing, scheduling, prefill/decode coordination, and multi-node GPU execution. Your improvements will be validated through reproducible benchmarks and safe production rollouts under real-world workloads. Your responsibilities Own optimization projects for specific model families, customer endpoints, or serving backends. Compare inference engines and recommend serving configurations suited to individual workloads. Diagnose model-quality and performance regressions during production rollouts. Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo. Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. For prefill–decode disaggregation (PDD), evaluate KV-cache transfer, worker placement, and capacity balancing to determine which workloads benefit from the architecture. Design and improve LLM request routers and scheduling policies that balance worker utilization, queueing, request characteristics, and KV-cache locality while meeting latency and reliability targets. Scale dense and mixture-of-experts inference across multiple GPU nodes. Select and tune tensor, pipeline, data, and expert parallelism—including wide expert parallelism (WideEP)—with attention to hardware topology, expert load balance, and communication overhead. Build reproducible benchmark harnesses measuring TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Validate architecture choices under representative traffic and consistent GPU budgets. Collaborate with GPU kernel and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Produce design documents, performance reports, rollout plans, and customer-facing technical explanations. Must-haves Strong engineering skills in Python and PyTorch. Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference. Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system. A strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost. Experience designing or optimizing distributed inference system architecture, with hands-on work in one or more areas such as request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism. Strong communication and collaboration skills across research, kernel, infrastructure, product, and customer teams. Nice-to-haves Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, or SmoothQuant. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or related inference acceleration methods. Experience supporting agentic workloads involving tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. Familiarity with CUDA or Triton; the role does not require kernel engineering to be the candidate’s primary specialization. Contributions to open-source projects such as vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, or KServe. Implementation experience with cache-aware request routing, PDD, or WideEP, including diagnosing communication bottlenecks and load imbalance. Familiarity with GPU communication libraries and interconnects, such as NCCL, NVLink, InfiniBand, or RoCE. 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: EMEA; European Union Why Cast AI? Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production. The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation. Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS. Global team, diverse perspectives We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. Unicorn momentum In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started. Join us as we build the future of autonomous infrastructure. About the role Kimchi is building products at the frontier of how engineering organizations adopt and operate AI — across AI coding and agents, inference, model orchestration, governance, and the infrastructure that makes all of it work reliably at scale. This is a highly dynamic space. Customer expectations, technology capabilities, competitors, and even the problems worth solving are changing quickly. That means we’re looking for a Senior Product Manager who is exceptionally good at working before the problem is well defined. You won’t inherit a neat backlog of customer requirements and execute against it. You’ll be expected to explore emerging problem spaces, talk to customers, understand technology shifts, connect weak signals, form hypotheses, and decide which problems are important enough for Kimchi to solve. A large part of the job is turning ambiguity into clarity while operating in a dynamic greenfield environment. You’ll work closely with engineering, design, customers, and GTM to take promising opportunities from an initial insight through validation, 0→1 product development, adoption, and iteration. What we're looking for: Significant experience owning software products end-to-end, with the judgment and independence expected from a Senior Product Manager. Strong 0→1 experience. You have personally taken products or significant product areas from an unclear opportunity through discovery, validation, launch, and iteration. A track record of navigating ambiguity. You can give examples where neither the problem nor the solution was obvious at the beginning and explain how you created clarity. Excellent problem selection and product judgment. You don’t just solve problems well; you are deliberate about deciding which problems are worth solving in the first place. Experience working in greenfield, rapidly changing, or emerging domains where information is incomplete and plans frequently need to change. Strong customer discovery skills. You can go beyond what customers ask for and uncover the underlying problem, context, and behavior. Strong analytical thinking. You are comfortable combining qualitative evidence, product data, market signals, and technical constraints to make decisions. Strong written and verbal communication. You can simplify complex topics without oversimplifying the underlying problem. Experience working closely with highly technical engineering teams and enough technical curiosity to understand the systems you are building. A bias toward experimentation and learning rather than large upfront plans. Comfort making decisions with imperfect information and changing your mind when new evidence emerges. Hands-on use of modern AI tools in your own work. You should be actively exploring how LLMs and agents change both the products we build and the way product work itself gets done. What would make you stand out: You don’t need to check every box, but we’d be especially interested if you have experience with one or more of: Developer tools or products built for software engineers AI/ML products, LLM infrastructure, AI agents, or AI-powered developer tools Infrastructure, platforms, APIs, or technically complex B2B products Products where the primary challenge was discovering product-market fit rather than optimizing an established product Early-stage startups or new product initiatives inside larger companies Enterprise products where you have had direct access to customers during discovery and product development Responsibilities: Identify problems worth solving. Explore customer needs, usage data, technology shifts, competitive products, and market signals to determine where Kimchi should invest. Turn ambiguity into product direction. Take broad or poorly understood opportunities and structure them into clear problems, hypotheses, assumptions, and experiments. Lead 0→1 product development. Move from an insight or hypothesis to a real product that customers can use, learn from, and ultimately depend on. Validate before scaling. Find the fastest way to test whether a problem matters and whether our proposed solution creates meaningful value. Be equally comfortable investing further or killing an idea when the evidence says we should. Make hard prioritization decisions. There will always be more interesting opportunities than we can pursue. You’ll decide what deserves attention now, what can wait, and what we should deliberately not build. Work directly with customers. Run discovery, observe workflows, understand underlying motivations and constraints, and distinguish individual requests from broader problems. Partner deeply with engineering and design. Shape solutions collaboratively. Understand enough of the technology to explore possibilities and trade-offs with strong technical teams. Define what success means. Establish the customer and business outcomes we expect, determine how to measure them, and use evidence to guide subsequent decisions. Drive adoption, not just launches. Stay accountable after something ships. Understand whether customers are discovering it, using it repeatedly, getting value from it, and why. Communicate clearly. Write concise product narratives, one-pagers, PRDs, and decision documents that make complex problems understandable and align teams around what matters and why. We are a distributed team, so exceptional writing skills are essential for every member. Help shape Kimchi itself. This is still a rapidly evolving product and organization. You’ll influence not only individual features, but how we think about the product, our customers, and the market. What’s in it for you? Competitive salary Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. *As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. *Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: Remote - US About MariaDB Are you ready to shape the infrastructure for a groundbreaking cloud database platform? MariaDB is making a big impact on the world. Whether you’re checking your bank account, buying a coffee, shopping online, making a phone call, listening to music, taking out a loan or ordering takeout – MariaDB is the backbone of applications used every day. Companies small and large, including 75% of the Fortune 500, run MariaDB, touching the lives of billions of people. With massive reach through Linux distributions, enterprise deployments and public clouds, MariaDB is uniquely positioned as the leading database for modern application development. MariaDB Cloud is revolutionizing the way businesses manage their data in the cloud. We're on a mission to provide a seamless, powerful, and user-centric Database-as-a-Service (DBaaS) solution that sets new industry standards. MariaDB Cloud is a Multi-cloud Database-as-a-Service platform offering, specializing in managing MariaDB, one of the most popular open-source relational database systems. MariaDB Cloud is more than just a database; it's a comprehensive and innovative platform that delivers continuous availability, autonomous operation, and serverless capabilities. We're redefining the database experience to make it more accessible, intuitive, and efficient than ever before. The Opportunity About the Role We are seeking a Senior Software Engineer to join a small, high-ownership team building robust cloud infrastructure for our globally deployed relational database engine. You will design and implement the application layers, business logic, and API workflows at the intersection of data management, cloud operations, and relational database services—critical components that enterprise applications depend on for correctness, performance, and governance. This is a hands-on, end-to-end role. You will lead features from whiteboard to production: owning implementation, writing clean and scalable code, and leveraging deep expertise in cloud orchestration to ensure robust deployment across multi-cloud environments. Proficiency in Terraform is essential for managing our complex infrastructure and ensuring reproducible deployments. Our team is small by design, offering high individual impact and direct visibility into engineering strategy. The ideal candidate has scaled infrastructure across multiple cloud providers and approaches every challenge with an ownership mindset—shipping production-grade systems that set new industry standards. Key Responsibilities 8+ years of experience in software development with deep expertise in distributed systems and cloud-native architecture. Proven track record of designing and operating multi-cloud infrastructure at scale across AWS, GCP, and Azure. Advanced proficiency in Terraform for Infrastructure as Code (IaC) and complex cloud orchestration. Expertise in containerization and orchestration using Kubernetes and Docker in production-grade environments. Experience building and maintaining robust CI/CD pipelines for automated, scalable deployments. Strong command of Go or Python for developing cloud-native applications, controllers, and automation tooling. BS/MS in Computer Science or a related technical field. Experience designing and implementing REST and/or gRPC APIs used by external consumers or frontend systems. Knowledge of MariaDB server is a plus. Required Qualifications Experience 5+ years of professional software development experience, with at least 2 years focused on data-intensive applications, database-adjacent features, or enterprise software. Demonstrated end-to-end ownership of a non-trivial production feature or module: you have taken something from functional specification through implementation to production. Hands-on production experience deploying, debugging, and managing application services across at least two major public clouds (AWS, Azure, GCP). Experience building or integrating with vector databases, embedding pipelines, semantic search, or retrieval-augmented generation (RAG) systems. Prior work building LLM-based applications or workflows using AI agent frameworks (LangChain, LangGraph, CrewAI, AutoGen, or equivalent). Technical Skills Strong proficiency in Python for AI application development, alongside high proficiency in at least one backend language (such as Go, Java, or C++). Deep, practical application of relational databases: writing complex SQL, structural indexing, query optimization, transactions, and schema design. Experience implementing temporal data models, event-driven tracking, audit logs, or utilizing system-versioned tables. Solid understanding of application security fundamentals: authentication, authorization, RBAC, row-level security, and multi-tenant data isolation at the application layer. Proficiency with modern application monitoring tooling: structured logging, distributed tracing (OpenTelemetry), and functional alerting. Experience designing and implementing REST and/or gRPC APIs used by external consumers or frontend systems. Preferred Qualifications Experience contributing to or maintaining open-source projects in the database, AI/ML application, or agent-tooling ecosystem. Hands-on experience developing MCP servers or integrating A2A (Agent2Agent) protocols into production systems. Familiarity with agent memory frameworks such as Mem0, Graphiti, or Letta. Experience leveraging graph databases or multi-hop graph traversal (Neo4j, Kuzu, FalkorDB, or similar) within applications. Background with in-memory data grids or semantic caching systems (Redis or similar) to optimize AI token usage. Experience packaging applications for on-premises or air-gapped deployment environments, including regulated infrastructure. Knowledge of MariaDB or MySQL application development, system-versioned tables, or temporal SQL. Location & Job Info Full-Time - Remote - US Why Join Us Compensation The annual anticipated U.S. base salary range for this full-time position is $141,000-$185,000. Actual salaries may vary and fall outside of this range depending on factors such as a candidate’s qualifications, geographic location, skills, experience, and competencies. In addition, we are often open to a wide variety of profiles, and recognize that the person we hire may be less experienced (or more senior) than the job description as posted. Salary is one component of MariaDB’s total rewards package.  We provide a competitive benefits package. Additional benefits includes health insurance, life, and disability insurance, funds toward professional development resources, Flexible Paid Time Off (FPTO), paid holidays, 401(k), and parental leave, a massive degree of flexibility and freedom, just to name a few! Salaries for candidates outside the U.S. will vary based on local compensation structures. How to Apply If you are interested in this position, please submit your application along with your CV/Resume through our ATS Greenhouse on our MariaDB Careers site. At MariaDB, we celebrate the diverse experiences and perspectives of our employees because this drives innovation and success. MariaDB is an equal opportunity employer dedicated to creating a welcoming and inclusive workplace for everyone. MariaDB does not sponsor work visas or relocation. MariaDB is committed to providing accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter. MariaDB will not accept agency resumes without a prior contractual agreement with HR. Please do not forward resumes to any recruiting alias or employee directly. MariaDB is not responsible for paying any fees associated with any unsolicited submitted CV/Resumes.

Location: Ukraine; Czech Republic; Latvia; Spain; Albania; Lithuania; Greece; Bosnia & Herzegovina; Croatia; Estonia; Serbia; Poland; Armenia; Portugal; Italy; Malta; Türkiye; Montenegro; Romania Workplace: Remote Location: Fully remote (EMEA timezone) Start date: ASAP Languages: Fluent English required Industry: Cloud Computing / AI / European Deep-Tech SaaS ABOUT THE ROLE Pragmatike is recruiting on behalf of a fast-scaling, well-funded distributed cloud infrastructure startup building next-generation AI-native cloud services. The company is redefining how compute is delivered by providing GPU-powered infrastructure for AI/ML workloads, secure storage, and high-speed data transfer through a decentralized architecture that significantly reduces environmental impact compared to traditional cloud providers. We are seeking a ML Ops Engineer with strong experience in production-grade model serving and infrastructure for AI systems. This is a highly technical, hands-on role focused on building scalable, reliable, and efficient ML inference platforms powering real-time AI applications. You will be responsible for designing and operating the core infrastructure that serves machine learning models at scale. You will work closely with infrastructure, platform, and applied AI teams to ensure high availability, low latency, and cost-efficient inference systems. Strong ownership, production mindset, and experience with distributed GPU systems are essential. YOUR RESPONSIBILITIES - Build and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent - Design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models - Develop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers - Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance - Design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health - Manage model registries and CI/CD pipelines enabling automated and reproducible model deployments - Own the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities - Define engineering best practices and contribute to platform scalability in a fast-moving startup environment REQUIRED QUALIFICATIONS - 4+ years of experience in ML Ops, Platform Engineering, SRE, or similar infrastructure roles focused on ML systems - Hands-on experience with model serving frameworks such as vLLM, TGI, Triton, or equivalent - Strong background in container orchestration and operating GPU-based workloads in production - Experience with MLOps tooling including model registries, experiment tracking, and automated deployment pipelines - Proficiency in Python and infrastructure-as-code tools (e.g., Terraform, Helm, or similar) - Strong understanding of distributed systems, performance tuning, and production reliability engineering - Ability to effectively use AI coding assistants to accelerate development and debugging workflows - Ownership mindset with the ability to operate independently in a remote-first environment PREFERRED QUALIFICATIONS - Experience with ML platforms such as Kubeflow, MLflow, or KubeAI - Knowledge of GPU scheduling, CUDA/ROCm optimization, or multi-tenant inference systems - Experience with cost optimization across different GPU types and inference workloads - Background in early-stage startups or greenfield infrastructure projects - Proven experience building production systems from scratch rather than maintaining legacy platforms WHY JOIN US - Take ownership of critical infrastructure powering a rapidly scaling AI-native cloud platform - Build foundational ML inference systems from the ground up in a high-growth, well-funded startup - Work at the intersection of distributed systems, GPU computing, and sustainable cloud architecture - Gain deep expertise in next-generation AI infrastructure and large-scale model serving systems - Influence core engineering decisions and define best practices that will scale with the company. Pragmatike is committed to a fair, transparent, and inclusive recruitment process. We do not discriminate based on age, disability, gender, gender identity or expression, marital or civil partner status, pregnancy or maternity, race, religion or belief, sex, or sexual orientation. In accordance with GDPR, your personal data will be processed lawfully, fairly, and securely, and used solely for recruitment purposes, including sharing it with our client(s) for employment consideration.

Location: Ukraine; Czech Republic; Bulgaria; Latvia; Spain; Hungary; Albania; Lithuania; Greece; Bosnia & Herzegovina; Croatia; Estonia; Serbia; Poland; Armenia; Portugal; Italy; Malta; Türkiye; Montenegro; Romania Workplace: Remote Location: Fully remote (EMEA timezone) Start date: ASAP Languages: Fluent English required Industry: Cloud Computing / AI / European Deep-Tech SaaS ABOUT THE ROLE Pragmatike is recruiting on behalf of a fast-scaling, well-funded distributed cloud infrastructure startup building next-generation AI-native cloud services. The company is redefining how compute is delivered by providing GPU-powered infrastructure for AI/ML workloads, secure storage, and high-speed data transfer through a decentralized architecture that significantly reduces environmental impact compared to traditional cloud providers. We are seeking a AI Infrastructure Engineer with strong experience in production-grade model serving and infrastructure for AI systems. This is a highly technical, hands-on role focused on building scalable, reliable, and efficient ML inference platforms powering real-time AI applications. You will be responsible for designing and operating the core infrastructure that serves machine learning models at scale. You will work closely with infrastructure, platform, and applied AI teams to ensure high availability, low latency, and cost-efficient inference systems. Strong ownership, production mindset, and experience with distributed GPU systems are essential. YOUR RESPONSIBILITIES - Build and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent - Design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models - Develop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers - Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance - Design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health - Manage model registries and CI/CD pipelines enabling automated and reproducible model deployments - Own the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities - Define engineering best practices and contribute to platform scalability in a fast-moving startup environment REQUIRED QUALIFICATIONS - 4+ years of experience in ML Ops, Platform Engineering, SRE, or similar infrastructure roles focused on ML systems - Hands-on experience with model serving frameworks such as vLLM, TGI, Triton, or equivalent - Strong background in container orchestration and operating GPU-based workloads in production - Experience with MLOps tooling including model registries, experiment tracking, and automated deployment pipelines - Proficiency in Python and infrastructure-as-code tools (e.g., Terraform, Helm, or similar) - Strong understanding of distributed systems, performance tuning, and production reliability engineering - Ability to effectively use AI coding assistants to accelerate development and debugging workflows - Ownership mindset with the ability to operate independently in a remote-first environment PREFERRED QUALIFICATIONS - Experience with ML platforms such as Kubeflow, MLflow, or KubeAI - Knowledge of GPU scheduling, CUDA/ROCm optimization, or multi-tenant inference systems - Experience with cost optimization across different GPU types and inference workloads - Background in early-stage startups or greenfield infrastructure projects - Proven experience building production systems from scratch rather than maintaining legacy platforms WHY JOIN US - Take ownership of critical infrastructure powering a rapidly scaling AI-native cloud platform - Build foundational ML inference systems from the ground up in a high-growth, well-funded startup - Work at the intersection of distributed systems, GPU computing, and sustainable cloud architecture - Gain deep expertise in next-generation AI infrastructure and large-scale model serving systems - Influence core engineering decisions and define best practices that will scale with the company. Pragmatike is committed to a fair, transparent, and inclusive recruitment process. We do not discriminate based on age, disability, gender, gender identity or expression, marital or civil partner status, pregnancy or maternity, race, religion or belief, sex, or sexual orientation. In accordance with GDPR, your personal data will be processed lawfully, fairly, and securely, and used solely for recruitment purposes, including sharing it with our client(s) for employment consideration.

Location: Ukraine; Czech Republic; Latvia; Spain; Albania; Lithuania; Greece; Bosnia & Herzegovina; Croatia; Estonia; Serbia; Poland; Armenia; Portugal; Italy; Malta; Türkiye; Montenegro; Romania Workplace: Remote Location: Fully remote (EMEA timezone) Start date: ASAP Languages: Fluent English required Industry: Cloud Computing / AI / European Deep-Tech SaaS ABOUT THE ROLE Pragmatike is recruiting on behalf of a fast-scaling, well-funded distributed cloud infrastructure startup building next-generation AI-native cloud services. The company is redefining how compute is delivered by providing GPU-powered infrastructure for AI/ML workloads, secure storage, and high-speed data transfer through a decentralized architecture that significantly reduces environmental impact compared to traditional cloud providers. We are seeking a ML Ops Engineer with strong experience in production-grade model serving and infrastructure for AI systems. This is a highly technical, hands-on role focused on building scalable, reliable, and efficient ML inference platforms powering real-time AI applications. You will be responsible for designing and operating the core infrastructure that serves machine learning models at scale. You will work closely with infrastructure, platform, and applied AI teams to ensure high availability, low latency, and cost-efficient inference systems. Strong ownership, production mindset, and experience with distributed GPU systems are essential. YOUR RESPONSIBILITIES - Build and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent - Design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models - Develop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers - Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance - Design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health - Manage model registries and CI/CD pipelines enabling automated and reproducible model deployments - Own the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities - Define engineering best practices and contribute to platform scalability in a fast-moving startup environment REQUIRED QUALIFICATIONS - 4+ years of experience in ML Ops, Platform Engineering, SRE, or similar infrastructure roles focused on ML systems - Hands-on experience with model serving frameworks such as vLLM, TGI, Triton, or equivalent - Strong background in container orchestration and operating GPU-based workloads in production - Experience with MLOps tooling including model registries, experiment tracking, and automated deployment pipelines - Proficiency in Python and infrastructure-as-code tools (e.g., Terraform, Helm, or similar) - Strong understanding of distributed systems, performance tuning, and production reliability engineering - Ability to effectively use AI coding assistants to accelerate development and debugging workflows - Ownership mindset with the ability to operate independently in a remote-first environment PREFERRED QUALIFICATIONS - Experience with ML platforms such as Kubeflow, MLflow, or KubeAI - Knowledge of GPU scheduling, CUDA/ROCm optimization, or multi-tenant inference systems - Experience with cost optimization across different GPU types and inference workloads - Background in early-stage startups or greenfield infrastructure projects - Proven experience building production systems from scratch rather than maintaining legacy platforms WHY JOIN US - Take ownership of critical infrastructure powering a rapidly scaling AI-native cloud platform - Build foundational ML inference systems from the ground up in a high-growth, well-funded startup - Work at the intersection of distributed systems, GPU computing, and sustainable cloud architecture - Gain deep expertise in next-generation AI infrastructure and large-scale model serving systems - Influence core engineering decisions and define best practices that will scale with the company. Pragmatike is committed to a fair, transparent, and inclusive recruitment process. We do not discriminate based on age, disability, gender, gender identity or expression, marital or civil partner status, pregnancy or maternity, race, religion or belief, sex, or sexual orientation. In accordance with GDPR, your personal data will be processed lawfully, fairly, and securely, and used solely for recruitment purposes, including sharing it with our client(s) for employment consideration.

Location: New York Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW The Senior Solutions Architect at is a critical role that bridges the gap between our product team and client-facing teams. You will partner closely with our Go-to-market teams (Sales, Customer Success, etc.) to shape how customers adopt Harvey across use cases, practice groups, and geographies. This role is accountable for translating complex requirements into secure, scalable architectures that deliver clear business outcomes. You will work directly with technical and non-technical stakeholders—from engineering and IT to legal operations, security, and executive sponsors—to align on the right approach. WHAT YOU'LL DO - Lead technical discovery, demos, and deep-dive sessions with prospects, engaging stakeholders across engineering, IT, security, business teams, and executive sponsors, and owning pre-sales and post-sales technical conversations. - Serve as the primary technical partner for key accounts, guiding customers from initial discovery through deployment, adoption, and ongoing expansion. - Partner closely with Customer Success Managers to grow adoption and expansion within existing accounts, owning the technical relationship with customer engineering and architecture leaders. - Design and articulate secure, scalable solution architectures that integrate Harvey into customers’ existing systems, workflows, and data environments. - Collaborate with Harvey’s Security and Trust and Information Security teams to address customer risk, compliance, and data-governance questions and to shape scalable patterns for secure deployments. - Act as the customer’s technical voice back into Harvey, influencing product roadmap and internal priorities based on real-world feedback and emerging post-sales needs. WHAT YOU HAVE - 5+ years of experience in solutions architecture, sales engineering, or similar externally facing technical roles, or a mix of software engineering and customer-facing technical roles. - Proven track record designing and implementing solutions for enterprise customers in SaaS, cloud, or AI/ML environments. - Experience partnering with Account Executives and post-sales teams (e.g., Customer Success, Account Management) to drive adoption, renewals, and expansion within enterprise accounts. - Meaningful experience engaging with executives and C-suite stakeholders, including presenting complex technical concepts in a way that supports strategic decision-making. - Comfort engaging with customer security, risk, and compliance stakeholders, ideally in a SaaS or AI platform context. - Strong communication skills, with the ability to translate between technical and non-technical audiences and build trust with senior stakeholders. - Curiosity about AI and a desire to work in a fast-moving environment where products and customer use cases are evolving quickly. COMPENSATION $180,200 - $247,700 USD OTE with a 80/20 Split DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-TM1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Задачи: — Руководить проектированием и развитием масштабируемых распределённых систем на Go — Отвечать за интеграцию и конфигурацию AI — от выбора моделей и интеграции API до prompt engineering и развертывания в продакшене — Переводить бизнес-цели в технические дорожные карты и планы поставки — Проводить архитектурные и код-ревью, наставлять старших инженеров — Представлять инженерную команду на встречах с внешними заинтересованными сторонами и оспаривать нереалистичные требования — Улучшать процессы CI/CD, наблюдаемости и инженерные процессы — Балансировать между кодированием, руководством командой и архитектурным контролем Требования: — 5–6+ лет коммерческого опыта с Go — 2+ года в роли технического лидера или ведущего инженера — Глубокие знания распределённых систем, микросервисов и конкурентности в Go (goroutines, каналы, синхронизация) — Практический опыт внедрения, настройки или интеграции AI/ML решений в продакшн-бэкендах, включая работу с LLM, RAG pipeline, OpenAI API, LangChain или аналогичными фреймворками — Опыт с CI/CD, облачной инфраструктурой и современными DevOps практиками — Высшее образование в IT — Готовность работать по CEST и использовать корпоративный ноутбук — Готовность выполнить live coding на Go во время интервью Условия: — Работа над долгосрочным продуктом с реальным бизнес-эффектом — Международная распределённая команда с коллаборативной культурой — Возможность взять ответственность за зрелое приложение — Гибкие условия работы и удалёнка