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Location: Bremen, Germany Mission The purpose of this role is to ensure professional, compliant, and execution-oriented award and post-award management of externally funded research projects, with a primary focus on German  funding schemes. The Grant Manager will support Principal Investigators, Faculty, research teams, and administrative stakeholders in delivering funded projects on time, within budget, and in full compliance with funder, legal, financial, and institutional requirements. This role is central to building Constructor Labs’ and Constructor University research operations capability and enabling high-quality collaboration between researchers, finance, legal, HR, external partners, and funding bodies. Duties and Responsibilities: Pillar 1: Pre-Award Grant Management Monitor funding landscapes (German Funding Scemes like DFG, BMBFTR, DAAD, and others); maintain the grants calendar and build a forward-looking application pipeline. Match funding opportunities to the research activities of Constructor Labs Principal Investigators (PIs) and Constructor University Faculty; initiate and coordinate proposal development. Co-develop competitive grant proposals with PIs and faculty, including budget design, and compliance requirements. Coordinate grant agreement and consortium agreement negotiations with Legal, and support partner contract negotiations where Constructor University or Constructor Labs leads. Pillar 2 Post-Award Grant Management Own the full post-award lifecycle across a portfolio of funded research projects — from contract setup through implementation, reporting, amendments, and close-out — ensuring milestones, deliverables, budgets, and contractual commitments are tracked and met. Monitor project budgets, eligible costs, spending rates, and financial risks in close cooperation with Controlling Team; coordinate amendments, budget reallocations, extensions, partner changes, and other modifications with all relevant internal and external stakeholders. Prepare and coordinate scientific, narrative, and administrative reports for funders, including DFG, BMBFTR, DAAD, and other relevant funding schemes; track funder rules, reporting templates, and compliance obligations and ensure they are reflected in internal processes. Serve as the primary liaison between researchers, Finance, Legal, HR, consortium partners, and funders; support consortium management including partner reporting, documentation collection, and meeting coordination. Support the implementation and operation of standardised software and digital tools across all active projects, as approved by the Director of Research and Transfer. Qualifications and Experience: Experience: 2–5 years of experience in grant management, research project management, research administration, sponsored projects, or third-party funded project administration. Hands-on experience managing research projects in a university, research institute, RTO, technology organisation, or research-intensive environment. Practical understanding of German research funding systems, ideally including DFG, BMBFTR, DAAD, or comparable public funding programmes. Experience with project budgets, financial monitoring, eligible-cost rules, funder reporting, audits, and documentation standards. Experience working with Principal Investigators, researchers, finance teams, legal teams, HR, external partners, and funding bodies. Experience managing several projects simultaneously with different deadlines, funder rules, reporting cycles, and stakeholder expectations. Skills & Competencies Strong project management discipline: able to convert complex grant agreements into clear workplans, deadlines, responsibilities, and reporting routines, and manage multiple projects in parallel. Strong understanding of financial and administrative compliance in externally funded research projects; able to manage budgets, forecasts, cost eligibility questions in a structured way. Strong stakeholder coordination and communication skills: able to work effectively with researchers, Finance, Legal, HR, external partners, and funders, and explain funding requirements in a practical, service-oriented way. High ownership mindset: proactively chases inputs, resolves inconsistencies, anticipates risks, and moves projects forward without waiting for perfect conditions. Excellent attention to detail, accuracy, and documentation habits. Experience implementing or operating research or project management information systems and building portfolio dashboards for leadership reporting is a strong advantage. Education & Languages Bachelor’s or Master’s degree in natural sciences, business administration, economics, public administration, science management, project management, research administration, or a related field. Fluent German and English (written and spoken). What This Role Is (and Is Not) This role IS A hands-on pre- and post-award project management role for externally funded research projects. Operational, detail-oriented, and compliance-driven. Central to ensuring that funded research projects are delivered on time, within budget, and in accordance with funder requirements. A cross-functional role connecting researchers, finance, legal, HR, external partners, and funding agencies. This role is NOT A purely strategic or advisory role. A fundraising-only or proposal-writing-only position. A senior RTO leadership role with responsibility for the full research funding strategy. A purely academic research role.

Location: New York DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus. We are seeking a Quantitative Researcher with 3–5 years of experience to develop and improve systematic trading signals using machine learning and statistical methods. The role focuses on non-linear signal modeling, regime-aware frameworks, and the integration of new data sources into existing research and risk systems. The researcher will work closely with traders and other quants, with clear ownership and direct impact on live trading. What you’ll be doing: · Design non-linear signal combination frameworks, improving upon existing linear and mean-variance approaches. · Build regime-aware models and conditional signal frameworks based on market states (e.g., volatility, correlations, risk conditions). · Research, develop, and validate machine learning–based trading signals on intraday and daily data. · Integrate signals into risk sizing, portfolio construction, and optimization frameworks. · Work closely with traders and infrastructure teams to transition research into production. Qualifications/Skills: · 3+ years of experience in quantitative research, systematic trading, or applied ML in financial markets. · Strong foundation in machine learning, statistics, and optimization with advance degree (masters/PhD) · Proficiency in Python and common data/ML libraries · Experience working with time-series data and building predictive models. · Strong analytical skills and ability to communicate results clearly to trading teams. The annual base salary range for this position is $150,000 to $250,000 depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus. In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts. For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice . California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice . [#LI-LR]

Location: Remote, United States GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As a Senior Product Manager , you will own GitLab's Secret Detection offering and the Vulnerability Research function that produces the detection content across security products. Secret Detection is one of the highest-signal, highest-volume security capabilities on the platform. Leaked credentials are the most common initial access vector in real breaches, and as AI agents write, commit, and configure more of the software, the surface area for exposed secrets grows faster than the number of humans watching it. You will own the full loop: detect a secret with high precision, tell the customer whether it is still live, get it revoked, and prevent the next one from ever landing. In parallel, you will own vulnerability research as a product asset rather than a back-office function. The detection rules, advisory data, and malicious package and reference intelligence your team produces are what make GitLab's security scanners worth paying for, and they need to ship on a cadence with measurable quality. This is an outcome-owning role - you will carry adoption, retention, and revenue targets for your area and be expected to explain how your roadmap moves them. Some examples of our projects: Push protection and pre-receive blocking that stops a credential before it reaches a repository, without wrecking developer flow Secret validity checking and automated revocation partnerships with major cloud and SaaS token issuers Detection content pipelines that turn threat research into shipped rules, with precision and recall tracked per rule Intelligence-driven detection of malicious packages, dependencies, and references entering the software supply chain What you'll do Own the business outcomes for Secret Detection and Vulnerability Research, including adoption, expansion, competitive win rate, and revenue contribution. Bring a point of view on packaging and pricing, not just features. Set the strategy for the full secret lifecycle: prevention, detection, validation, revocation, and reporting across GitLab.com, Dedicated, and Self-Managed. Treat detection content as a product. Define how rules, advisories, and intelligence feeds are sourced, validated, versioned, and measured, and make quality visible to customers. Hold the line on detection quality. False positives are a product defect and you will own the metrics that prove precision is improving. Work at the level of the technology. Read the rule syntax, question the entropy heuristics, understand why a scanner missed something, and challenge engineering with informed alternatives. Use AI to compress the distance between question and answer. Pull your own data, prototype your own flows, synthesize research and competitive input yourself, and bring conclusions rather than requests for someone else to investigate. Build the case for where AI belongs in the product: triage, rule generation, remediation guidance, and reducing the human review burden per finding. Partner with engineering, security research, threat intelligence, Field, and GitLab's own Security team, who are one of your most demanding users. Communicate in writing, asynchronously, with enough precision that a distributed team can act without a meeting. What you'll bring Domain depth in application security, vulnerability management, or security research. You have worked on or adjacent to scanners, detection content, threat intelligence, or SDLC security tooling and you know how these products actually get evaluated in a bake-off. Technical credibility sufficient to earn the respect of a security engineering team. You do not need to have written the scanner, but you should be able to reason about detection logic, data pipelines, CI integration, and the tradeoffs between coverage and noise. Commercial reasoning. You start from revenue mechanics, buyer motion, and competitive displacement, then work inward to product decisions. Candidates who reason only from feature lists outward are not a fit. Evidence of using AI as a force multiplier in your own work: research, analysis, data pulls, prototyping, drafting. Consuming a chat assistant occasionally is not the same as restructuring how you work. Judgment under ambiguity. You bring structured options and a recommendation instead of escalating an open question. Bias for clarity. You can take a noisy, technical, politically contested problem and produce one page that everyone can align on. Bonus: hands-on background as a developer, security engineer, red teamer, or researcher; experience with credential and token ecosystems; experience commercializing a data or intelligence asset. About the team This role sits in GitLab's Security product management organization, which owns application security testing, vulnerability management, supply chain security, secrets management, and AI governance. The Security Section is central to GitLab's Ultimate tier and to the shift toward consumption-based product revenue, so the work is visible to senior leadership and directly tied to company results. You will work asynchronously with engineering, design, and research counterparts across multiple regions, and with the Field teams who take this to market. How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Location: United Kindom, United Kingdom; Buenos Aires, Argentina; Bogotá, Colombia; Sao Paulo, Brazil; Montevideo, Uruguay; Bangalore, India; Chittagong, Bangladesh; Karachi, Pakistan; Tokyo, Japan; Hanoi, Vietnam; Taipei, Taiwan; Bangkok, Thailand; London, United Kingdom; Lugano, Switzerland; Barcelona, Spain; Dubai, United Arab Emirates; Sofia, Bulgaria; Prague, Czechia; Belgrade, Serbia; Copenhagen, Denmark; Tallinn, Estonia; Athens, Greece; Tbilisi, Georgia; Budapest, Hungary; Dublin, Ireland; Sliema, Malta; Oslo, Norway; Amsterdam, Netherlands; Warsaw, Poland; Lisbon, Portugal; Bucharest, Romania; Stockholm, Sweden; Brussels, Belgium; Tel Aviv, Israel; Istambul, Türkiye; Nicosia, Cyprus Workplace: remote Join Tether and Shape the Future of Digital Finance At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction. Innovate with Tether Tether Finance: Our innovative product suite features the world’s most trusted stablecoin, USDT , relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services. But that’s just the beginning: Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities. Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET , our flagship app that redefines secure and private data sharing. Tether Education : Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity. Tether Evolution : At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways. Why Join Us? Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry. If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you. Are you ready to be part of the future? About the job As a member of the AI model team, you will drive innovation in architecture development for cutting-edge models of various scales, including small, large, and multi-modal systems. Your work will enhance intelligence, improve efficiency, and introduce new capabilities to advance the field. You will have a deep expertise in Large Language Model (LLM) and Multi-Modal architectures, a strong grasp of pre-training optimization, and a hands-on, research-driven approach. Your mission is to explore and implement novel techniques and algorithms that lead to groundbreaking advancements: multi-modal data curation and alignment, strengthening baselines, and identifying and resolving existing pre-training bottlenecks to push the limits of cross-modal AI performance. Responsibilities Large-Scale Pre-Training: Conduct foundational pre-training for LLMs and Multi-Modal models (integrating text, vision, audio, or other modalities) on large, distributed servers equipped with multi-nodes & thousands of NVIDIA GPUs. Architecture & Alignment Innovation: Design, prototype, and scale innovative architectures, tokenizers, and cross-modal alignment layers to enhance model intelligence and multi-modal understanding. Data Strategy: Source, filter, and curate massive-scale textual and multi-modal datasets, establishing robust data pipelines for efficient pre-training. Experimental Research: Independently and collaboratively execute experiments, analyze results, and refine training methodologies for optimal performance and token efficiency. Optimization & Debugging: Investigate, debug, and eliminate bottlenecks in model efficiency, computational performance, and multi-modal alignment stability during long training runs. System Scalability: Contribute to the advancement of distributed training systems to ensure seamless scalability and hardware efficiency on target platforms. A degree in Computer Science  or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences). Hands-on experience contributing to large-scale LLM or Multi-Modal pre-training runs on large, distributed servers equipped with thousands of NVIDIA GPUs, ensuring scalability and impactful advancements in model performance. Familiarity and practical experience with large-scale, distributed training frameworks, libraries and tools. Deep knowledge of state-of-the-art transformer and non-transformer modifications aimed at enhancing intelligence, efficiency and scalability. Strong expertise in PyTorch and Hugging Face libraries with practical experience in model development, continual pretraining, and deployment. Important information for candidates Recruitment scams have become increasingly common. To protect yourself, please keep the following in mind when applying for roles: Apply only through our official channels. We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/ Verify the recruiter’s identity. All our recruiters have verified LinkedIn profiles. If you’re unsure, you can confirm their identity by checking their profile or contacting us through our website. Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms. Double-check email addresses. All communication from us will come from emails ending in @ tether.to or @ tether.io We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately. When in doubt, feel free to reach out through our official website.

Location: Boston, MA Workplace: OnSite At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. As a Temporary Clinical Research Associate, you will provide monitoring and clinical operations support across WHOOP’s portfolio of clinical research studies. You will support assigned studies at various stages of the clinical lifecycle, including startup, active study conduct, monitoring, and closeout. You will conduct monitoring activities, review study documentation, support Trial Master File quality, track study issues, and help ensure that studies are conducted in accordance with approved protocols, Good Clinical Practice, applicable regulations, and WHOOP procedures. This role requires a detail-oriented and collaborative CRA who can independently manage assigned monitoring activities, communicate effectively with research sites and internal teams, and escalate risks or compliance concerns appropriately. Depending on study timelines and business needs, you may support one or more studies during the contract period. This is a 6 month, temporary position with an hourly pay rate of $45/hr. RESPONSIBILITIES: - Conduct remote and onsite monitoring activities for assigned studies, including Site Initiation Visits, Interim Monitoring Visits, Closeout Visits, and other study-specific visits. - Perform monitoring activities in accordance with approved protocols, monitoring plans, Good Clinical Practice, applicable regulations, and WHOOP procedures. - Review informed consent documentation, participant eligibility, source documentation, case report forms, protocol adherence, safety documentation, and study data for accuracy and completeness. - Prepare accurate and timely monitoring visit reports, follow-up communications, action-item trackers, and study status updates. - Identify, document, track, and escalate protocol deviations, data quality concerns, safety issues, compliance risks, and operational challenges. - Support site readiness activities, including review of essential documents, training completion, systems access, and operational preparedness. - Assist with site and study team training on protocols, study procedures, documentation requirements, and clinical systems. - Create and maintain assigned study logs, trackers, monitoring templates, and clinical operations documentation. - Collaborate with Clinical Operations, Clinical Science, Clinical Research Coordinators, Data Management, Regulatory, Quality, investigators, research sites, and external vendors. - Support study closeout activities, including document reconciliation, action-item resolution, final monitoring activities, and Trial Master File completion. QUALIFICATIONS - Bachelor’s degree in life sciences, nursing, public health, clinical research, or a related field, or equivalent relevant experience. - 3+ years of experience in clinical research, including at least 1 year performing clinical monitoring or CRA responsibilities. - Strong attention to detail and the ability to identify and clearly document study risks, compliance concerns, and data quality issues. - Strong written communication skills, including the ability to prepare clear monitoring reports and site follow-up correspondence. DESIRED: - Experience supporting medical-device, digital-health, wearable-technology, or remote-monitoring studies. - Familiarity with ISO 14155 or clinical research involving medical devices. - Experience working in a sponsor, CRO, or research-site environment. - Experience supporting studies related to cardiovascular health, women’s health, sleep, metabolic health, or digital biomarkers.

Location: Amsterdam, Netherlands; London, United Kingdom; Remote - Europe; 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. We are seeking a Staff Applied AI Researcher to join a fast-growing team building an agent-native search platform - the web access layer for AI systems. You can think of this as Google search, but for AI agents: a system designed for machines, not humans. We are building agentic search, where AI systems actively plan, retrieve, evaluate, and refine information rather than simply returning results. As AI becomes the primary interface to the web, this layer will replace the role of traditional search engines. We are designing these systems to search and reason over web data in real time, under strict latency and reliability constraints. This means solving retrieval, ranking and passage extraction under entirely new access patterns and at significant scale, with systems operating over constantly changing, unstructured data and serving tens of thousands of production workloads 24/7. This role comes with ownership over key parts of our applied AI research direction and system design, with a strong expectation of defining new approaches and shipping measurable impact in production. In the first year, you will own a specific part of the search stack, e.g. ranking or iterative retrieval. What you'll work on: Designing an agent-native search system that handles everything from low-latency single-hop search to complex deep search tasks requiring iterative planning, querying, refinement and reasoning. Training ranking and retrieval models optimized for agent-driven workflows under real world constraints Bringing the latest advances from the research community to our search stack and ship it to production. Your responsibilities: Drive applied research and technical direction across retrieval and ranking systems Design and evolve multi-stage retrieval architectures (query understanding, rewriting, retrieval, reranking and iterative retrieval) Lead experimentation on modern retrieval approaches (embedding research, sparse and hybrid retrieval, reranking, context compression) and bring them into production Develop methods for grounding LLMs in real-time web data at scale, using trust, quality and freshness signals to surface reliable sources Partner with the evaluation team to define new metrics for agentic systems, where correctness is not reducible to clicks Analyze trade-offs across relevance, latency, and cost, and work closely with engineering to deploy systems that operate at web scale Mentor researchers and engineers and help raise the technical bar of the team Must haves: 8+ years of experience in applied AI, ML, or software engineering Proven track record of shipping ML or AI systems to production at scale Deep experience with search, retrieval, ranking or building RAG-based assistants Strong understanding of modern deep learning, especially transformers and embeddings Experience with LLM-integrated or knowledge-intensive systems Experience designing evaluation frameworks and metrics for ML systems Strong programming skills in Python Top-tier publications ( ICLR , ICML , NeurIPS, AAAI , EMNLP , ACL ) Ability to operate in a fast-moving, product-driven environment with high ownership and autonomy Nice to haves Background in agentic AI systems (agents, tool use, autonomous workflows) Experience with RL post-training of LLMs or VLMs 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: United States, Remote Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. Reporting to the Director of Global Vulnerability Management, you will serve as a Senior Security Research Engineer and technical lead within SIE’s Global Vulnerability Management team. You will lead complex work that advances our Threat Exposure Management capability and supports our transition to a Continuous Threat Exposure Management operating model, while remaining directly engaged in vulnerability research, analysis, security validation, prioritization, and remediation support. You will partner across Information Security, engineering, technology, and business teams to improve how SIE discovers, prioritizes, validates, and mobilizes action on security risks. You will translate annual TEM goals into defined workstreams, delivery plans, success measures, and repeatable operating practices that improve visibility, decision-making, remediation outcomes, and program scale. Responsibilities Lead complex Global Vulnerability Management workstreams that advance SIE’s Threat Exposure Management capability and transition toward a Continuous Threat Exposure Management operating model across discovery, prioritization, validation, and mobilization. Translate annual TEM goals into defined delivery plans, milestones, success measures, dependencies, and repeatable operating practices. Monitor progress, identify barriers, and adjust execution to improve outcomes. Lead hands-on vulnerability research and technical analysis to confirm security conditions, eliminate false positives, characterize exploitability and business impact, and provide actionable remediation or mitigation guidance. Improve the discovery and assessment of vulnerabilities across SIE network, cloud, endpoint, application, container, and other technology environments. Develop risk-based prioritization using vulnerability and threat intelligence, exploitation evidence, asset and business context, control effectiveness, and remediation considerations. Lead security validation activities and develop proofs of concept when appropriate to distinguish material risk, evaluate defensive controls, and support informed decisions. Partner with Information Security teams and technology owners to mobilize remediation, clarify ownership, establish effective handoffs, resolve barriers, and measure progress through remediation, mitigation, or accepted disposition. Evolve GVM platforms, integrations, data, analytics, automation, and AI-enabled workflows to improve coverage, data quality, consistency, decision speed, and operational scale. Communicate vulnerability trends, material risks, remediation progress, and program outcomes to technical, operational, and leadership audiences through clear reports and recommendations. Mentor engineers and partner teams, contribute to technical standards and procedures, and improve the quality of vulnerability analysis, validation, documentation, and delivery. Maintain current knowledge of relevant vulnerabilities, exploitation techniques, threat activity, security technologies, and industry practices. Some travel may be required. Qualifications 8+ years of relevant experience in information security, information technology, systems engineering, or a related field, including substantial experience in vulnerability management, security research, or exposure management. Bachelor’s degree or equivalent in computer science, information security, or a related discipline. Experience leading complex technical workstreams across multiple teams, translating objectives into delivery plans, and achieving measurable outcomes without relying on direct reporting authority. Hands-on experience with vulnerability research, technical analysis, security validation, risk-based prioritization, and remediation or mitigation guidance. Experience assessing vulnerabilities across several technology domains, such as operating systems, networks, cloud services, applications, endpoints, containers, databases, or hybrid infrastructure. Experience with vulnerability discovery, assessment, validation, or exposure-management platforms and their supporting integrations. Knowledge of adversarial tactics, exploitation techniques, threat intelligence, and attack frameworks such as MITRE ATT&CK, with the ability to apply that information to vulnerability prioritization. Experience using scripting, programming, APIs, or automation to improve security analysis and operational workflows. Relevant technologies may include Python, SQL, Bash, PowerShell, JavaScript, or comparable languages. Experience analyzing and contextualizing security data to connect technical findings with asset, service, business, threat, control, and remediation information. Familiarity with software engineering practices such as version control, testing, code review, CI/CD, reusable components, and controlled production releases. Ability to communicate complex security conditions, priorities, tradeoffs, and recommendations clearly to technical, operational, and leadership audiences. Experience mentoring engineers or analysts and influencing technical practices across teams. Desired qualifications Experience helping evolve a traditional vulnerability-management function toward a TEM or CTEM operating model. Experience with continuous security validation, adversarial exposure validation, exploit research, or proof-of-concept development. Experience securing cloud, container, application, or build-pipeline environments and integrating security capabilities into engineering workflows. Experience with security-data and analytics platforms such as Snowflake, Domo, or comparable technologies. Experience applying automation, advanced analytics, or AI-enabled capabilities to vulnerability analysis, prioritization, validation, or remediation workflows. At SIE, we consider several factors when setting each role’s base pay range, including the competitive benchmarking data for the market and geographic location. Please note that the base pay range may vary in line with our hybrid working policy and individual base pay will be determined based on job-related factors which may include knowledge, skills, experience, and location. In addition, this role is eligible for SIE’s top-tier benefits package that includes medical, dental, vision, matching 401(k), paid time off, wellness program and coveted employee discounts for Sony products. This role also may be eligible for a bonus package. Click here to learn more. This is a flexible role that can be remote, with varying pay ranges based on geographic location. For example, if you are based out of Seattle, the estimated base pay range for this role is listed below. $175,500 — $263,300 USD Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice (where applicable) for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

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 Team Our Reinforcement Learning teams are central to advancing our AI systems, contributing to every Claude model and driving the autonomy and coding gains in our latest releases. The work spans computer use, code generation through RL, fundamental RL research for large language models, scalable infrastructure and training methodologies, and model reasoning. Research TPM team supports the full model development lifecycle, from pre-training through post-training, operating at the frontier of AI development. About the Role As a Technical Program Manager on the reinforcement learning team, you will own the systems and programs that determine how fast our research moves: a trustworthy read on the state of RL research, the review and prioritization processes that turn that read into critical decision for production RL runs. Strong candidates should have an ML engineering or research background and have grown into program leadership. You'll need real technical depth: the ability to debug data pipelines, read RL transcripts to spot issues, and make allocation and quality decisions in real time when research or production runs hit problems. You'll need organizational effectiveness in equal measure: the ability to navigate a fast-growing organization, quickly identify the critical people and teams across research, infrastructure, product, and data operations, and coordinate across them without losing velocity. Join us in our mission to build AI systems that are safe, reliable, and beneficial to humanity. Responsibilities Deliver a regular read on the ground truth in RL research, covering performance against baselines, experiment results, day-to-day health, and incidents Work with RL org leads on prioritizing, ranking, and tracking the state of experiments Drive research reviews end to end in partnership with set the agenda, make sure the right context is in the room ahead of time, and close the loop on what gets decided Establish processes and frameworks that bring structure to an unstructured research setting without slowing researchers down Collaborate with research leads, infrastructure engineers, and data operations to identify blockers, prioritize competing needs, and make technical trade-off decisions You May Be a Good Fit If You Have a background in ML engineering or ML research before transitioning to technical program management Have deep, hands-on experience with ML training pipelines, RLHF systems, and large-scale data infrastructure in production Have a track record of building execution plans and inventing high-leverage processes that reduce operational overhead and let researchers focus on research Are a fast learner who builds deep contextual understanding in unfamiliar technical domains and can contribute meaningfully to discussions with researchers Are resourceful, high-agency, and able to navigate ambiguity and shifting priorities to drive progress in a fast-moving research setting Have excellent stakeholder management and communication skills, with the ability to influence senior technical staff through clarity, competence, and consistent delivery Are excited about pushing the frontier of what RL can do at scale The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $365,000 — $435,000 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: Palo Alto Workplace: Hybrid ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE ROLE This role focuses on building and operating the end-to-end execution, training, and data infrastructure that powers Mistral’s agentic models and coding assistants. You will be a core contributor to our agent research stack: designing scalable systems for synthetic data generation, building ultra-fast training and RL execution environments, and maintaining high-throughput execution engines. You will tackle the engineering challenges at every step of the agent lifecycle: from orchestrating 1M+ concurrent and short-lived sandboxes for untrusted code execution to optimizing agent training codebases, distributed trajectory collection pipelines, and dataset processing workflows across massive hybrid and multi-cloud clusters. WHAT YOU WILL DO - Large-Scale Sandboxing Infrastructure: Design, deploy, and operate our high-throughput sandboxing platform, executing LLM-generated untrusted code across over 1 million isolated environments concurrently for model evaluation and interactive RL environments. - Agent Data Generation Pipelines: Architect and scale high-throughput pipelines for synthetic code generation, agent trajectories, rollouts, and self-play data collection to power post-training and RL loops. - Training Codebase & Systems Optimization: Optimize agent training codebases and distributed execution runtimes (PyTorch, Ray, SLURM/Kubernetes) to minimize multi-step rollout overhead, improve GPU utilization, and eliminate scaling bottlenecks. - Low-Latency Orchestration & Warm Pooling: Reduce sandbox cold-start times to sub-second levels using container warm pools, snapshot/restore technology (e.g., CRIU, microVMs), and optimized image delivery layers across hybrid clusters. - Multi-Cluster Queueing & Resource Allocation: Implement Kubernetes-native custom controllers, CRDs, and queuing systems to dynamically route short-lived evaluation, synthetic data, and agent execution tasks across diverse hardware fleets. - Isolation, Security & Security Boundary: Ensure strict multi-tenant network and process isolation for untrusted agent code using container/sandboxing runtimes (e.g., gVisor, Firecracker) and default-deny network postures. - Operational Excellence: Maintain high availability, telemetry, and automated self-healing across millions of transient jobs while participating in on-call rotations for critical agent training and execution pipelines. WHAT WE'RE LOOKING FOR - 4+ years of experience in Systems Engineering, Distributed Systems, Cloud Infrastructure, or MLOps supporting LLM/RL workloads. - Data & Pipeline Engineering: Proven experience building high-throughput data processing and generation pipelines for large-scale datasets (e.g., Ray, Spark, custom distributed queues). - Deep experience with Kubernetes & Container Tech: Strong expertise writing custom K8s operators/controllers, managing Linux cgroups/namespaces, and optimizing Docker image layers and distribution systems. - High-Performance Software Engineering: Advanced proficiency in Python, Go, C++ or Rust, with a track record of profiling and optimizing high-performance ML or backend systems codebases. - Sandboxing & Isolation Technologies: Hands-on experience with lightweight virtualization, container runtimes, or WASM (e.g., Docker, gVisor, Firecracker). - Queueing & Scheduling: Deep familiarity with task queue systems, resource schedulers, and low-latency queuing architectures for high-volume, short-lived workloads. - Comfort with Ambiguity: Passion for working directly alongside AI researchers to rapidly turn frontier agent ideas into scalable, production-grade infrastructure. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: New York; San Francisco Workplace: OnSite ABOUT US: AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. WHAT YOU’LL WORK ON: - Improve existing methods and develop new techniques for large-scale model training, optimization, and inference - Extend models to long-context and long-horizon tasks - Improve inference-time efficiency, reliability, and robustness in high-stakes real-world deployments YOU MAY BE A FIT IF: - You’re currently pursuing a PhD in computer science, machine learning, or a related field - You have a demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas - You have experience developing and evaluating large-scale models or machine learning systems - You’re familiar with distributed training, large-scale inference, or multi-GPU environments - You have publications at leading venues such as NeurIPS, ICML, ICLR, CVPR, CoRL, UAI, JMLR, or TMLR - You have strong programming and engineering skills and can translate research ideas into working implementations - You have a collaborative, mission-driven mindset and can work effectively across research and engineering teams

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a VP of Research (Machine Learning) to define and lead the research direction behind its AI platform. This leadership role will shape how AI systems reason, learn, evaluate, and continuously improve within a product designed for high-frequency, real-world usage. Working closely with product and engineering leadership, this position will drive the long-term intelligence strategy, balancing cutting-edge research with practical production impact. Key Responsibilities Define and evolve the research roadmap for the platform's core AI intelligence, including context representation, memory, reasoning, planning, and orchestration. Evaluate and determine when to develop proprietary model architectures versus leveraging or adapting frontier open-source and commercial AI models. Design evaluation frameworks that measure real-world performance, robustness, safety, and long-term system behavior beyond traditional benchmark metrics. Lead the company's AI alignment, safety, and guardrail strategy as a core component of product development. Drive research and experimentation across advanced machine learning techniques, including: Retrieval-Augmented Training (RAG) Mixture of Experts (MoE) Model Distillation Multi-Agent Orchestration Multimodal AI Systems Partner closely with product and engineering teams to define and execute the intelligence strategy for AI-powered applications. Establish and maintain high standards for research quality, technical judgment, and engineering excellence across the organization. Requirements Extensive experience designing, building, and deploying machine learning systems in production environments. Strong technical expertise in model behavior, failure analysis, system evaluation, and long-term AI performance. Proven ability to translate advanced research into reliable, production-ready AI solutions. Demonstrated experience making high-impact technical decisions in fast-moving and ambiguous environments. Deep understanding of AI evaluation methodologies, model robustness, safety, and system reliability. Strong ownership mindset with the ability to lead technical strategy and execution. Passion for building practical AI systems that deliver measurable real-world impact rather than focusing solely on academic research or benchmark performance. Preferred Technical Skills Experience with the following technologies is preferred: Python PyTorch and/or JAX GPU-based model training and inference systems

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 Anthropic's Safeguards team builds the systems that detect and mitigate misuse of our AI models, from individual policy violations to sophisticated, coordinated attacks. A growing part of that work depends on lightweight detection methods trained on model internals, which let us identify harmful behavior cheaply and at scale. This work feeds directly into Anthropic's Responsible Scaling Policy commitments. We're looking for an engineer to own the infrastructure behind that research. This is the tooling our researchers rely on to run experiments, train detection methods, and select detections for launch. It sits between research and production: researchers depend on it for fast iteration, and our detection systems depend on it for reliable, correct results as our models continue to change. Running machine learning workloads at our scale often requires solving novel systems problems. You'll identify those problems and build the abstractions, pipelines, and tooling that keep the research loop fast as requirements shift underneath you. Strong candidates will have a track record of solving large-scale systems and data problems and will be excited to grow deep machine learning expertise alongside it. Key responsibilities Build and scale the infrastructure and data pipelines behind Safeguards machine learning research Own the training, evaluation, and scoring workflows researchers use, with a focus on cutting the time between an idea and a result Design tooling and interfaces, including libraries and command line tools, that researchers can use directly without needing to understand the systems underneath Build correctness and sanity checking into the stack, so results stay trustworthy as models and workloads evolve Take the highest-value research workflows from experiments to reliable, production-grade jobs Improve the throughput, cost, and reliability of large-scale inference and scoring workloads Partner closely with researchers and engineers across Safeguards to understand their workflows, anticipate how their needs will change, and design for that ahead of time Minimum qualifications Strong software engineering fundamentals and hands-on coding ability, with proficiency in Python Experience building and operating data-intensive or distributed systems in production Experience building tooling or infrastructure that other engineers or researchers use as a dependency Comfort working across the research-to-deployment pipeline, from exploratory experiments to production systems Ability to debug performance and correctness problems across an unfamiliar stack Strong written and verbal communication skills, and a collaborative approach to technical decisions Preferred qualifications Experience with high-performance, large-scale machine learning systems Familiarity with language modeling and transformers, including working with model internals Experience with machine learning framework internals, GPU or accelerator programming, or inference optimization Experience building experiment tracking, caching layers, or evaluation harnesses for research teams Experience with probes, interpretability, or classifier development Interest in the misuse risks of AI systems and a desire to work on mitigating them 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: $350,000 — $500,000 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: Boston, MA Workplace: OnSite At WHOOP, we're on a mission to unlock and inspire performance for life. By providing members with a deep understanding of their bodies, behaviors, and daily lives, WHOOP empowers healthier choices and peak performance. We are seeking a Principal AI Researcher to join our Foundation AI team. This team builds the foundation models that underpin WHOOP’s next generation of intelligent, personalized, and health-enhancing experiences. These models integrate data across wearable sensors, language, biomarkers, clinical information, and self-reported inputs to create scalable AI systems that understand human physiology and behavior. In this role, you'll be one of the senior technical leaders at WHOOP.  You will own the technical execution of our foundation modeling workstreams and your technical judgment will shape a core part of WHOOP’s AI efforts and the products they power. RESPONSIBILITIES: - Set the architectural direction for the development of large-scale models spanning wearable sensor data, text, biomarkers, and behavioral signals, and personally own the highest-stakes, hardest-to-reverse design decisions. - Identify which research bets in self-supervised and representation learning are worth making, and drive the most ambiguous and unproven ones from thesis to validated, production capability. - Contribute to the broader AI  strategy and standards at WHOOP, shaping the platform, compute, and infrastructure choices the team operates on, not just working within them. - Partner across research, product, and engineering to connect technical breakthroughs to product and business outcomes. - Raise the technical bar across the team: grow staff and senior engineers, strengthen design-review and evaluation norms, and act as a force multiplier. - Represent WHOOP's technical work externally, helping with recruiting, partnerships, and visibility in the broader AI research community. QUALIFICATIONS: - Advanced degree (Master’s or Ph.D.) or equivalent depth, with extensive experience in large-scale machine learning and AI research. - A track record of owning technical direction for a significant area and making architectural decisions whose impact outlived any single project. - Demonstrated ability to take ambiguous, high-risk research directions all the way to production impact and the judgment to discontinue those that will not. - Deep expertise in modern deep learning (transformers, state space models), multimodal training, self-supervised and representation learning, RL-based post-training (PPO/DPO/GRPO), and large-scale distributed training (data, model, and context parallelism). - A history of being a technical authority that teams defer to, and evidence of multiplying their output. - Excellent communication and the ability to influence both the engineering bench and senior leadership. - Publications and evidence of community building at top-tier machine learning venues.  - Passion for WHOOP's mission to improve human performance and extend healthspan through science and technology.  This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.  Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply. WHOOP is an Equal Opportunity Employer and participates in E-verify https://www.e-verify.gov/ to determine employment eligibility.  It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values. At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success. The U.S. base salary range for this full-time position is $270,000 - $300,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training.  In addition to the base salary, the successful candidate will also receive benefits and a generous equity package. These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.

Location: United States of America What We Do: Cybercrime is growing, and more businesses are getting hit by threats that used to target only the biggest organizations. That pushes defenders like us to operate at the highest level, and it deepens our need for good people who want to make a meaningful impact. Founded in 2015 by former NSA cyber operators, Huntress is a remote-first team working to make enterprise-grade cybersecurity accessible to businesses of all sizes. We work closely with security teams and service providers protecting complex environments, often without the time or headcount to handle it all. That’s why we build our technology in-house and back it with a 24/7 human-led Security Operations Center (SOC). As a result, our platform is never disconnected from the experts who manage it, ensuring our customers' protection. Huntress now secures more than 6M endpoints and 17M identities worldwide. Those numbers keep growing because more businesses rely on us to help carry the load and operate with more confidence. Every day, you can see that commitment in how we stand with our customers and how we show up for each other. Reports to: Manager, Product Research Location: Remote, US Compensation Range: $200,000 to $230,000 base plus bonus and equity What You’ll Do: The Huntress Product Research team has the unique honor of waking up every morning knowing we’re going to make hackers regret targeting our partners and customers. As a Principal Product Researcher, we’re looking for someone who wants to pour all of their creativity into building and implementing simple solutions that are disproportionately effective at countering these constantly evolving threats. Competitive candidates have experience managing, deploying, and securing SMB environments utilizing a wide variety of security software, best practices, and automation tools. Familiarity with product management, incident response, social engineering, psychology, education, and managed service provider tools is an additional way to differentiate yourself. As you can imagine, success doesn’t happen in a vacuum. An effective Product Researcher fosters highly collaborative environments between the Product, Engineering, and Security teams to accelerate our mission and secure the 99% of businesses that fall below the enterprise poverty line. This collaboration is needed to produce and prioritize a unified technical vision that ultimately delivers our most impactful features and capabilities. We defend over 5M endpoints for 225,000+ mid-sized and small-business customers, and that number continues to grow each month. Given this market’s tighter budget, it’s not financially feasible to assign human analysts to each client. The Security Operations team addresses this challenge head-on by building and scaling highly automated efficiencies—often lightly augmented by our SOC — that make intruders earn every inch of their access while maintaining affordability and healthy gross margins. Roles and Responsibilities: Identify innovative ways to detect Windows OS threats Develop cross-platform features that leverage telemetry from common OS subsystems such as file system, memory, process, and network activity Research and development of sensor capabilities to provide visibility and detection support for attack techniques across supported Windows OS versions Work collaboratively with internal engineering and detection teams to implement detection logic Identify and evaluate new telemetry opportunities Identify and address gaps in product coverage Respond to product escalations Perform False Positive and False Negative investigations Lead product research initiatives to develop and evaluate security product strategies and technologies Coordinate with Product and Engineering teams to integrate and operationalize solutions developed by Threat Operations teams Applies AI as a practical part of day-to-day product research work to improve the speed, quality, and impact of research outputs Uses AI to support information gathering, analysis, prototyping, and production of clear, decision-useful work products while maintaining accountability for the final output Builds and reuses lightweight prompts, templates, or workflows for recurring research tasks rather than relying on one-off use Develop internal and external technical documentation to educate customers and communicate research findings to adjacent teams about security risks and opportunities Mentor and teach technical expertise to advance the broader community Promote Huntress’ reputation through media interaction, public speaking, CFPs, CTFs, and blogs Qualifications: Expert in Windows OS internals, components, APIs, and design Prior experience with Windows OS kernel coding and device drivers Experience testing EDRs, bypasses, and evasion techniques Comfortable with reverse engineering and using debuggers Proficiency in multiple programming/scripting languages, such as C/C++/C#, PowerShell, and Python Applies AI to improve research quality, speed, and outputs in day-to-day work Commitment to clear documentation of research findings Experience with MITRE ATT&CK matrix, SIGMA, Yara, and Elasticsearch/Kibana What We Offer: 100% remote work environment - since our founding in 2015 Generous paid time off policy, including vacation, sick time, and paid holidays 12 weeks of paid parental leave Highly competitive and comprehensive medical, dental, and vision benefits plans 401(k) with a 5% contribution regardless of employee contribution Life and Disability insurance plans Stock options for all full-time employees One-time $500 reimbursement for building/upgrading home office Annual allowance for education and professional development assistance $75 USD/month digital reimbursement Access to the BetterUp platform for coaching, personal, and professional growth Huntress is committed to creating a culture of inclusivity where every single member of our team is valued, has a voice, and is empowered to come to work every day just as they are. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, gender identity, disability, veteran status, genetic information, marital status, or any other legally protected status. We do discriminate against hackers who try to exploit businesses of all sizes. Accommodations: If you require reasonable accommodation to complete this application, interview, or pre-employment testing or participate in the employee selection process, please direct your inquiries to accommodations@huntresslabs.com . Please note that non-accommodation requests to this inbox will not receive a response. Huntress uses artificial intelligence tools to assist in reviewing and evaluating job applications, including resume screening, skills assessment, and candidate matching and comparisons. These AI tools support our human recruiters in the initial review process, but do not make final hiring decisions without human involvement. By submitting your application, you acknowledge this use of AI in our recruitment process. Please review our Candidate Privacy Notice for more details on our practices and your data privacy rights. #BI-Remote

Location: San Francisco, CA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the team Anthropic's Life Sciences team is building a world-class research group focused on making fundamental biological discoveries. The team combines cutting-edge AI with hands-on biological research, positioning Anthropic at the forefront of AI-accelerated scientific discovery. About the role We're seeking an exceptional Research Scientist to join the team. This role combines deep computational biology expertise with frontier AI capabilities, positioning Anthropic at the forefront of AI-driven scientific discovery. As one of the first computational members of this Life Sciences research group, you'll work on a high-impact team that operates at the intersection of computational and experimental biology. You'll bring broad computational biology experience to bear across the team's projects, driving discoveries from large-scale computational analysis of biological data through to results our experimental scientists can test, and moving flexibly between problems as the science demands. You'll have substantial access to Claude and you'll help establish how computational biology operates at Anthropic. This role offers a unique opportunity to shape how AI transforms biological research. You'll work with some of the world's best AI researchers while tackling problems that matter deeply for scientific understanding and biomedicine. If you're excited about using your computational expertise to make fundamental biological discoveries and guide the development of transformative AI systems, we want to hear from you. Key responsibilities Build, run, and maintain the analysis pipelines that back the team's experimental programs: sequence analysis at petabyte scale, structural bioinformatics, phylogenetic and comparative genomics, design and analysis of high-throughput functional screens, biological sequence modeling, etc. Partner directly with experimental biologists to design experiments that produce high-quality data, and turn results around fast enough to immediately inform the next experiment Draw on the literature and curated biological knowledge bases alongside primary data to generate and prioritize hypotheses for experimental follow-up Stand up and maintain the team's computational infrastructure: data ingestion, workflow orchestration, internal databases, and the interfaces that make all of it accessible to both researchers and AI agents Use Claude and our internal agent frameworks heavily in your own work, and feed what you learn back to the model-improvement and product teams as evaluations, datasets, and concrete failure cases Pick up analyses across projects as priorities shift; we're looking for breadth and flexibility over a single deep specialty Minimum qualifications Have a PhD in computational biology, bioinformatics, genomics, biophysics, machine learning, computer science, or a related quantitative or biological field, or equivalent industry research experience Have a track record of computational biology research you have led end to end, from question to result, with evidence of impact (for example publications, preprints, released datasets or tools, or research that changed a program's direction) Have demonstrated breadth across multiple areas of computational biology Are proficient in one or more programming languages used in scientific computing and comfortable working on large datasets in Linux and cloud compute environments Can take an ambiguous biological question, scope the analysis, and produce a result an experimentalist can act on Communicate computational results clearly to both biologists and ML researchers Preferred qualifications Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments Are results-oriented, with a bias towards flexibility and impact Hands-on experience in experimental biology, or a track record of designing experiments side by side with experimentalists Experience building tools, pipelines, or agentic systems on top of LLMs, or training models on biological sequence data Deep expertise in one or two areas of computational biology (for example structural biology, metagenomics, single-cell genomics, or protein design) on top of the required breadth 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: $300,000 — $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: Chicago, New York City Our formula for success is to hire exceptional people, encourage their ideas and reward their results. As a Quantitative Researcher , you will develop mathematical models using advanced statistical learning methods to build automated trading strategies across multiple asset classes. Our research team collaborates on idea generation and strategy development, while encouraging independent exploration and original approaches. You will have access to clean data integrated with compute resources and the support of experienced researchers, traders and dedicated software developers. You will conduct quantitative analysis of market data to uncover relationships and identify historical trends. We do not require familiarity with or prior experience in financial markets for this role. We will give you the training you need to be successful. How you will make an impact Extract predictive signals from financial data through both traditional statistical analysis methods and cutting edge machine learning techniques Assist software developers to translate research strategies into production software Optimize the order execution and risk management of our trading system Create robust solutions to problems presented in the trading environment Formulate and apply mathematical modeling techniques to enhance existing trading strategies and perform innovative new research with the goal of identifying and capturing trading opportunities Automate human-decision based trading strategies; prototype algorithmic trades from trading ideas You will be right at home if you have… A degree in a technical discipline with a focus on statistics, machine learning, signal processing, optimization and control graduating between December 2026 and August 2027 (Bachelor’s, Master’s, PhD) A curiosity for model development and experience handling large data sets Expertise in programming using Python, R, MATLAB, or C++ for conducting research Can advocate for your beliefs in a concise and effective way with the team Significant hands-on experience applying machine learning algorithms to real world problems Strong problem-solving and statistics skills The proactive ability to take the lead on assignments and deliver practical research results in a timely manner DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world.  We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it.  DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus. The annual base salary range for this position is $250,000 to $300,000 depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus.  In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts. For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice . California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice . #LI-SB1

Location: Chicago, New York City Our formula for success is to hire exceptional people, encourage their ideas and reward their results. As a Quantitative Research Intern you will have an opportunity to solve challenging problems arising in a trading environment while utilizing the latest statistical scientific algorithms, machine learning techniques and derivatives pricing theory.  The teams focus on non-latency sensitive investment opportunities and multi-asset class derivatives strategies across geographies. Our teams emphasize cutting-edge innovative scientific research and collaboration, allowing you to gain a deeper understanding of quantitative trading. You will find great minds with diverse backgrounds, who are passionate about cultivating new ideas and exploring ways to bring them to life. You will use the team’s custom research infrastructure for simulation, back-testing, and validation of the proposed models. While your days will have you engrossed with complex technology projects, your evenings will be spent exploring the city with organized social events to truly discover what it’s like to live and work in Chicago or New York City. How you will make an impact… Create practical solutions to problems presented in the trading environment on either a systematic equity trading desk or a fixed income options desk Conduct statistical analysis of market data, historical trends, and relationships across multiple asset classes Formulate and apply mathematical modeling, quantitative methods and machine learning techniques to identify and capture trading opportunities Work closely with traders and researchers to build and refine research infrastructure and tools What you bring to the team… Are pursuing a Bachelor’s, Master’s or PhD in a technical discipline with a focus on Statistics, Optimization, Machine Learning, Artificial Intelligence, Quantitative Finance or related fields graduating between December 2027 and August 2028 Proficiency in Python programming experience using the Python machine learning stack: numpy, pandas, scikit-learn, etc. Proficient programming skills with experience exploring large datasets Strong analytical and problem-solving skills including a solid foundation of statistics knowledge Working knowledge of probability theory, stochastic calculus and numerical algorithms such as finite differences, Monte Carlo simulation, etc. Some exposure to Natural Language Processing and/or High-Performance Computing is a plus Excellent written and verbal communication skills to report research results as well as methodologies Added bonus if you have been published in a top tier journal focusing on Natural Language Processing or High-Performance Computing What to expect during the internship Meaningful projects: Each project, advised by a trader, promotes a comprehensive learning experience and provides you with real-world work experience. Community: Throughout the summer, we host a variety of educational, social and team-building activities to explore the city, foster friendships and camaraderie. Housing: DRW provides fully furnished apartments located close to the office – making your morning commute as easy as possible. Mentorship: You’ll build a professional relationship with an experienced mentor in your field. Mentors and mentees meet to discuss goals, challenges and professional development and explore the city together at our mentor outings. Education: As the trading industry continually evolves, both in terms of new products and transaction methods, the future will present us with unique opportunities and challenges. You’ll complete an options course taught by an experienced trader and participate in a technology immersion course to better understand how technology and trading intersect. DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world.  We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it.  DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus. The annual base salary range for this position is $250,000 to $300,000 depending on the candidate’s experience, qualifications, and relevant skill set. For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice . California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice . #LI-DNI

Location: London Our formula for success is to hire exceptional people, encourage their ideas and reward their results. As a Quantitative Research Intern you will have an opportunity to solve challenging problems arising in a trading environment while utilizing the latest statistical scientific algorithms, machine learning techniques and derivatives pricing theory.  The teams focus on non-latency sensitive investment opportunities and multi-asset class derivatives strategies across geographies. Our teams emphasize cutting-edge innovative scientific research and collaboration, allowing you to gain a deeper understanding of quantitative trading. You will find great minds with diverse backgrounds, who are passionate about cultivating new ideas and exploring ways to bring them to life. You will use the team’s custom research infrastructure for simulation, back-testing, and validation of the proposed models. While your days will have you engrossed with complex technology projects, your evenings will be spent exploring the city with organised social events to truly discover what it’s like to live and work in London. How you will make an impact… Create practical solutions to problems presented in the trading environment on either a systematic equity trading desk or a fixed income options desk Conduct statistical analysis of market data, historical trends, and relationships across multiple asset classes Formulate and apply mathematical modeling, quantitative methods and machine learning techniques to identify and capture trading opportunities Work closely with traders and researchers to build and refine research infrastructure and tools What you bring to the team… Are pursuing a Bachelor’s, Master’s or PhD in a technical discipline with a focus on Statistics, Optimisation, Machine Learning, Artificial Intelligence, Quantitative Finance or related fields graduating between December 2027 and October 2028 Proficiency in Python programming experience using the Python machine learning stack: numpy, pandas, scikit-learn, etc. Proficient programming skills with experience exploring large datasets Strong analytical and problem-solving skills including a solid foundation of statistics knowledge Working knowledge of probability theory, stochastic calculus and numerical algorithms such as finite differences, Monte Carlo simulation, etc. Some exposure to Natural Language Processing and/or High-Performance Computing is a plus Excellent written and verbal communication skills to report research results as well as methodologies Added bonus if you have been published in a top tier journal focusing on Natural Language Processing or High-Performance Computing What to expect during the internship Meaningful projects: Each project, advised by a trader, promotes a comprehensive learning experience and provides you with real-world work experience. Community: Throughout the summer, we host a variety of educational, social and team-building activities to explore the city, foster friendships and camaraderie. Housing: DRW provides fully furnished apartments located close to the office – making your morning commute as easy as possible. Mentorship: You’ll build a professional relationship with an experienced mentor in your field. Mentors and mentees meet to discuss goals, challenges and professional development and explore the city together at our mentor outings. Education: As the trading industry continually evolves, both in terms of new products and transaction methods, the future will present us with unique opportunities and challenges. You’ll complete an options course taught by an experienced trader and participate in a technology immersion course to better understand how technology and trading intersect. DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world.  We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it.  DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus. For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice . California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice .