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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 .

Location: Singapore 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. 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-LT2

Location: Singapore 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 Singapore. 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. 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 . #LI-LT2

Location: Palo Alto; San Francisco 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 next generation of data infrastructure at Mistral AI. You will be a core contributor to our evolution, helping us design and scale massive compute fleets and storage systems designed for high performance and scalability. You will help us move toward a future of decoupled control and data planes, scaling big data compute and storage platforms while ensuring secure and governed data access for MLOps and research. You will take full lifecycle ownership: from architecting the migration away from legacy orchestrators to implementing production-grade pipelines and participating in on-call rotations for critical training jobs.     What You Will Do   • Build & Scale: Help us reach our goal of operating massive distributed compute and storage systems • Global Orchestration: Architect and maintain multi-cluster orchestration layers to optimize workload placement across diverse hardware and regions. • Design Future-Proof Storage: Architect our transition to modern storage formats to handle fine-tuning datasets at a scale that anticipates exabyte growth. • Platform Engineering: Contribute to the development of our internal training platform, ensuring seamless model training and fine-tuning capabilities across Kubernetes and SLURM based environments. • Metadata & Lineage: Implement and manage systems to provide clear visibility and lineage as our data and model pipelines grow in complexity. • Operational Excellence: Use modern deployment workflows to manage cloud-native deployments, ensuring our data platform can scale by orders of magnitude while remaining reliable and efficient.   What We're Looking For   • Have 4+ years of experience in Data Infrastructure, MLOps, or Infrastructure Engineering. • Have experience or a strong interest in supporting foundational compute and storage platforms. • Are proficient in Python and enjoy solving the "brittle data lake" problem with modern, columnar storage standards. • Are well-versed in Kubernetes-native tooling and excited to debug large-scale distributed systems across multi-cluster environments. • Take pride in building and operating scalable, reliable, and secure systems from the ground up. • Are comfortable with ambiguity and the challenges of building high-scale infrastructure in a rapid-growth AI environment.     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: 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 Senior AI Researcher to join our Foundation AI team. This team builds the multimodal 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 serve as a senior individual contributor driving the research, development, and deployment of large-scale multimodal models. You’ll collaborate closely with data scientists, ML engineers, and cross-functional partners to push the boundaries of deep learning and ensure our models deliver measurable value to WHOOP members. RESPONSIBILITIES: - Design, train, and optimize large-scale multimodal foundation models that integrate wearable sensor data, text, biomarkers, and behavioral data. - Conduct applied research in self-supervised learning, representation learning, and downstream task fine tuning to advance WHOOP’s core model capabilities. - Develop scalable, distributed training pipelines for large models on high-performance compute environments. - Collaborate with MLOps, data engineering, and software engineering teams to operationalize models for production deployment, ensuring robustness, reproducibility, and observability. - Partner with product and research teams to translate foundation model capabilities into downstream features that deliver meaningful member value. - Contribute to the technical roadmap and architectural direction for foundation model development at WHOOP. - Ensure models adhere to WHOOP’s standards for ethical, transparent, and privacy-preserving AI. QUALIFICATIONS: - Advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, Electrical Engineering, or a related field, or equivalent professional experience. - 7+ years of experience in applied ML, AI research, or large-scale modeling, with a track record of delivering production systems. - Expertise in modern deep learning (e.g., transformers, state space models), multimodal model training.  - Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).  - Familiarity with training models on  mulit-node, multi-gpu distributed compute environments.  - Familiarity with best practices for data, model, and context parallelisms. - Strong applied experience with representation learning, self-supervised methods, and post-training for downstream applications. - Experience with reinforcement learning for post-training foundation models (PPO, DPO, GRPO etc.).  - Familiarity with MLOps best practices including model versioning, evaluation, CI/CD for ML, and cloud-based compute. - Excellent communication skills and ability to collaborate cross-functionally with engineers, researchers, and product teams. - 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 $190,000 - $230,000. 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: Paris; Zurich; Warsaw; London 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. Role Summary   The Data Infrastructure team at Mistral AI is architecting the backbone of our frontier model training and fine-tuning ecosystem. We are building the specialized compute and data fabrics required to power the development of world-class AI.   Our vision is to operate some of the largest compute fleets in production and build data lakes and metadata systems with a roadmap toward exabyte-scale architecture. We are currently in the process of building a high-performance training platform designed for massive scale across both on-premise and cloud-native Kubernetes environments.   We are leading a strategic transition from legacy scheduling to modern orchestration. With numerous clusters distributed across various regions, we are focussed on implementing sophisticated multi-cluster orchestration and cloud-bursting capabilities to better utilize our global resources and ensure our researchers have seamless access to compute wherever it resides. Our mission is to evolve our current systems into a platform that is as durable as it is flexible.   Location: Paris / Warsaw / Zurich / London (hybrid) or remote EU/UK with one hub visit per month.   About the Role   This role focuses on building and operating the next generation of data infrastructure at Mistral AI. You will be a core contributor to our evolution, helping us design and scale massive compute fleets and storage systems designed for high performance and scalability. You will help us move toward a future of decoupled control and data planes, scaling big data compute and storage platforms while ensuring secure and governed data access for MLOps and research. You will take full lifecycle ownership: from architecting the migration away from legacy orchestrators to implementing production-grade pipelines and participating in on-call rotations for critical training jobs.   In this role, you will: - Build & Scale: Help us reach our goal of operating massive distributed compute and storage systems - Global Orchestration: Architect and maintain multi-cluster orchestration layers to optimize workload placement across diverse hardware and regions. - Design Future-Proof Storage: Architect our transition to modern storage formats to handle fine-tuning datasets at a scale that anticipates exabyte growth. - Platform Engineering: Contribute to the development of our internal training platform, ensuring seamless model training and fine-tuning capabilities across Kubernetes and SLURM based environments. - Metadata & Lineage: Implement and manage systems to provide clear visibility and lineage as our data and model pipelines grow in complexity. - Operational Excellence: Use modern deployment workflows to manage cloud-native deployments, ensuring our data platform can scale by orders of magnitude while remaining reliable and efficient. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have 4+ years of experience in Data Infrastructure, MLOps, or Infrastructure Engineering. - Have experience or a strong interest in supporting foundational compute and storage platforms. - Are proficient in Python and enjoy solving the "brittle data lake" problem with modern, columnar storage standards. - Are well-versed in Kubernetes-native tooling and excited to debug large-scale distributed systems across multi-cluster environments. - Take pride in building and operating scalable, reliable, and secure systems from the ground up. - Are comfortable with ambiguity and the challenges of building high-scale infrastructure in a rapid-growth AI environment. Location and Remote This role is primarily based at one of our European offices (Paris, London, Warsaw and Zurich). We will prioritize candidates who either reside there or are open to relocating. We strongly believe in the value of in-person collaboration to foster strong relationships and seamless communication within our team. In certain specific situations, we will also consider remote candidates based in one of the countries listed in this job posting — currently France, UK, Poland and Switzerland). In that case, we ask all new hires to visit the local hub: • for the first week of their onboarding (accommodation and travelling covered) • then at least 3 days per month 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: Paris; Zurich; Warsaw; London 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. Role Summary   As a Research Engineer on the software side, you will design and harden the codebase, tools and distributed services that let our scientists train and ship frontier-scale models. You do not need prior ML experience; what matters is writing clean, reliable code that scales.   You will join our Research Software Engineering team to build and maintain shared dev-tools, evaluation & data pipelines, training framework, cluster tooling and CI/CD.   Location: Paris / Warsaw / Zurich / London (hybrid) or remote EU/UK with one hub visit per month. What will you do   • Accelerate researchers by owning the complex parts of large-scale pipelines and delivering robust internal tooling. • Interface research with product: expose clean APIs, automate model pushes, surface live metrics. • Write efficient, well-tested Python and systems code; enforce code review, CI, and observability. • Design and optimise distributed services (Kubernetes / SLURM, thousands-of-GPU jobs). • Prototype utilities (CLI, dashboards) and carry them through to stable, shared libraries.   About the Research Engineering team Based in Paris and London, our REs move fluidly along the research ↔ production spectrum. Engineers can rotate between Platform and Embedded tracks as their interests evolve.   About you   • Master’s in Computer Science (or equivalent experience). • 4 + years building and operating large-scale or distributed systems. • Strong software-design instincts: modular code, tests, CI/CD, observability. • Fluency in Python plus one systems language (C++, Rust, Go or Java). • Hands-on with container orchestration and schedulers (Kubernetes / K8s, SLURM, or similar). • Comfortable profiling performance, optimising I/O, and automating workflows. • Self-starter, low-ego, collaborative, high-energy.   Nice-to-haves   • Exposure to ML workloads or data-processing pipelines. • Experience with GPU clusters or CUDA. • Open-source contributions or widely used internal tools. Location and Remote This role is primarily based at one of our European offices (Paris, Warsaw, Zurich and London). We will prioritize candidates who either reside there or are open to relocating. We strongly believe in the value of in-person collaboration to foster strong relationships and seamless communication within our team. In certain specific situations, we will also consider remote candidates based in one of the countries listed in this job posting — currently France & UK. In that case, we ask all new hires to visit our local office: • for the first week of their onboarding (accommodation and travelling covered) • then at least 3 days per month 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: Paris; Warsaw; London 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. Role Summary    We are seeking a talented and experienced platform engineer to join our Research Platform team. You'll work closely with our R&D team to build a cloud agnostic platform that improves the stability, scalability and velocity across the research department. You'll help create and optimize tools that empower researchers to make the most of high performance clusters.   Location: Paris / Warsaw / London (hybrid) or remote EU/UK with one hub visit per month.   What you will do   As a Platform Engineer, your responsibilities will include (but may not be limited to):   • Designing and implementing complex systems (e.g. scale our research CI with a strong focus toward reliability, reproducibility and speed) • Building flexible yet solid and accessible development environment for researchers, so they can focus on core mission. • Designing, implementing and advocating for solutions addressing large amounts of data and maintainable data pipelines. • Optimizing a variety of builds: container images, large libraries compilation times, python environments... • Building strong relationships with researchers, understanding their workflow and enabling them to achieve more by leveraging your expertise. • Communicating and producing documentation or any content that will help them to make the most out of the tools and systems you'll build. • Being part of the team that "platformizes" research and constantly improve the daily experience for researchers while avoiding future roadblocks.   About you   • 5+ years of successful experience in a similar DX / DevOps / SRE role. • Proficiency in software development (Python, Go...) and programming best practices. • Exposure to site reliability engineering: root cause analysis, in-production troubleshooting, on-call rotations...) • Exposure to infrastructure management: CI/CD, containerization, orchestration, infra-as-code, monitoring, logging, alerting, observability...). • Technical product mindset (e.g. understanding how to debug poor adoption). • Excellent problem-solving and communication skills (ability to contextualizing, gauging risks and getting buy-in for high stakes and impactful solutions). • Ownership, high agency and constantly seeking to learn and improving things for others. • Autonomous, self-driven and able to work well in a fast-paced startup environment. • Low ego and team spirit mindset.   Your application will be all the more interesting if you also have: • First hand Bazel (or equivalent) experience. • Strong knowledge of Python's ecosystem. • Familiarity with GPU based workloads and ecosystems. • Experience of full remote environments (you're comfortable with having some of your users on the other side of the globe).   Location and Remote This role is primarily based at one of our European offices (Paris, Warsaw and London). We will prioritize candidates who either reside there or are open to relocating. We strongly believe in the value of in-person collaboration to foster strong relationships and seamless communication within our team. In certain specific situations, we will also consider remote candidates based in one of the countries listed in this job posting — currently France & UK. In that case, we ask all new hires to visit our local office: •  for the first week of their onboarding (accommodation and travelling covered) •  then at least 3 days per month WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: Hybrid About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: Austin, TX | New York, NY | San Francisco, CA or London, UK (Hybrid) About the Role You’ll help define how machine learning models run across Cloudflare’s global network, from frontier open LLMs and real-time voice models to customer-deployed models served on heterogeneous GPUs and next-generation accelerators. You’ll work with systems engineers, product teams, hardware partners, and AI/ML engineers to bring models into production with low latency, strong reliability, and efficient resource use. This role combines applied ML, inference optimization, evaluation, and production engineering, with a focus on benchmarking models, improving serving performance, validating quality, and building tooling that helps Cloudflare and its customers ship AI applications at Internet scale. Responsibilities Develop, optimize, and productionize machine learning models for Cloudflare’s serverless inference platform, with a focus on performance, reliability, and model quality. Build benchmarking and evaluation frameworks to measure latency, throughput, cost efficiency, and model behavior across LLMs, speech, vision, and other model families. Improve inference performance through quantization, batching, caching, model compilation, runtime tuning, and accelerator-aware optimization. Partner with systems engineers to integrate models into Cloudflare’s distributed inference infrastructure across a heterogeneous fleet of GPUs and next-generation accelerators. Drive improvements to model deployment workflows, including validation, rollout safety, observability, regression testing, and operational readiness. Collaborate with product and engineering teams to translate customer requirements into scalable ML capabilities for Workers AI. Mentor engineers, contribute to technical direction, and raise the quality bar for production ML engineering practices across the team. Desirable Skills, Knowledge, and Experience Experience building, optimizing, and operating machine learning models in production environments. Strong proficiency with Python and modern ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent. Hands-on experience with inference optimization techniques for large-scale models, including quantization, batching, caching, compilation, and serving runtime tuning. Experience with large-scale inference serving frameworks or runtimes such as SGLang, vLLM, TensorRT-LLM, ONNX Runtime, Triton, llama.cpp, or similar. Familiarity with LLMs, speech models, vision models, embeddings, multimodal models, retrieval-augmented generation, or other modern deep learning architectures. Experience optimizing models for GPUs or specialized accelerators. Strong understanding of production ML concerns, including evaluation, monitoring, model regressions, rollout safety, and reliability. Ability to work across ML and systems boundaries, including familiarity with distributed systems, networking, or serverless platforms. Track record of leading complex technical projects and mentoring other engineers. Bonus Points Experience contributing to open source ML tooling, model serving frameworks, or inference runtimes. Compensation Compensation may be adjusted depending on work location. F or Bay Area based hires: Estimated annual salary of $194,000 - $266,000 For New York City, New Jersey, Washington, Washington DC, and California (excluding Bay Area) based hires: Estimated annual salary of $185,000 - $254,000 Equity This role is eligible to participate in Cloudflare’s equity plan. Benefits Cloudflare offers a complete package of benefits and programs to support you and your family.  Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun!  The below is a description of our benefits for employees in the United States, and benefits may vary for employees based outside the U.S. Health & Welfare Benefits Medical/Rx Insurance Dental Insurance Vision Insurance Flexible Spending Accounts Commuter Spending Accounts Fertility & Family Forming Benefits On-demand mental health support and Employee Assistance Program Global Travel Medical Insurance Financial Benefits Short and Long Term Disability Insurance Life & Accident Insurance 401(k) Retirement Savings Plan Employee Stock Participation Plan Time Off Flexible paid time off covering vacation and sick leave Leave programs, including parental, pregnancy health, medical, and bereavement leave What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.

Location: Worldwide We are looking for a Principal Research Scientist to lead the development of foundation models for Risk & Decisioning, the team that powers credit decisions for Plata’s 3M+ customers. You will define the technical direction of our modeling work and take models from research all the way to production, partnering closely with Risk, Product, and Data teams on priorities and roadmap. This is a hands-on principal role: you will set the engineering standard for the team, build production-grade models yourself, and see their impact directly in cost of risk, approval rates, and portfolio NPV. Challenges that await you: Build an end-to-end foundation model over financial event sequences - transactions, credit bureau data, and in-app behavioral events  with subsequent fine-tuning for downstream business tasks: underwriting (PD), credit limit strategy, fraud detection, collections, and propensity models Drive technical decisions end-to-end: methodology → implementation → performance and latency → robustness, interpretability, and regulatory compliance Take models from research to production: training infrastructure, evaluation frameworks, model serving, latency/cost optimization, and monitoring Research state-of-the-art approaches in the industry, publish your own work, and speak at leading conferences Mentor senior engineers and scientists; own technical standards for model development across the team (design reviews, evaluation methodology, deployment practices). Communicate results clearly to cross-functional stakeholders: product, risk, business, and leadership What makes you a great fit: Proven experience applying deep learning to sequential data - transformer architectures on event/transaction sequences strongly preferred, but not required Strong foundation in mathematical statistics and probability theory Deep understanding of machine learning algorithms (GBM, MLP, CNN, RNN, Transformers, etc.) Experience taking large models to production: distributed training, model serving, latency/cost trade-offs Ability to strike a reasonable balance between solution complexity and practical applicability Strong mathematical or technical education - degree in mathematics, physics, or CS from a top technical university Kaggle Competitions Master/Grandmaster or equivalent (a plus); experience developing models in banking or consumer lending (a plus) Strong communication skills Our ways of working: Innovative Spirit: a commitment to creativity and groundbreaking solutions Honest Feedback: valuing open, transparent communication Supportive Team: a strong, collaborative community Celebrating Achievements: recognizing our wins together High-Tech Environment: a team full of smart and revolutionary people who date to challenge the status quo of incumbent finances Our benefits: Relocation support to one of our hubs - Mexico, Cyprus, Spain with assistance for the employee and their family Flexible work from one of our offices or remote Healthcare coverage Education budget: language lessons, professional training and certifications Wellness budget : mental health and fitness activity reimbursements Vacation Policy: Annual leave and paid sick leave as per applicable policies

Location: Austin; Yerevan; London Workplace: OnSite We are looking for exceptional quantitative researchers to develop systematic trading strategies based on market microstructure. This role is focused on extracting predictive signals from high-frequency market data and turning them into robust, scalable trading models. You will explore large datasets, develop new features, test hypotheses, and work closely with researchers and engineers to deploy ideas into production. Location Austin, TX, Yerevan, AM and London, UK (5 days in-office requirement) Key Responsibilities - Research predictive signals from market microstructure data. - Design and evaluate new features using the most granular market data. - Develop statistical and machine learning models for systematic trading. - Build robust research infrastructure and analytical tools. - Work directly with experienced researchers and Portfolio Managers to take ideas from hypothesis to live trading. Basic Requirements - PhD in Mathematics, Statistics, Physics, Computer Science, Electrical Engineering, or a related quantitative discipline. - Outstanding mathematical and statistical skills. - Strong programming ability in Python and C++/Java. - Experience working with quantitative models. - Curiosity, creativity, and proven academic track record. What you’ll get - On-site presence of experienced Quantitative Researchers and Portfolio Managers to learn from - Build Strategies while becoming the best at what you do - Professional guidance from experienced mentors Benefits - Health insurance - Flexible sick time policy - Office Lunches

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 researchers generate and depend on enormous amounts of data — training runs, evaluations, RL transcripts, annotations etc... The Research Data Platform team builds the systems that make that data easy to produce, find, query, and trust. We work in two modes: we build platform components that other systems plug into (for example, a metrics library that training frameworks integrate to record and retrieve run data), and we own core datasets end to end (for example, the data pipeline behind RL transcripts). As the team's tech lead, your job starts with our users. You'll work directly with researchers — and with the engineers who support them — to understand how they actually work, where managing data slows them down, and where a well-built platform component or a well-curated dataset would change what's possible. You'll turn what you learn into technical direction for the team, in partnership with the team's manager, who owns priorities and people. A central ambition you'll drive: a small set of canonical, well-documented datasets — starting with the core data model for RL — that researchers trust and standardize on, rather than every team managing its own copies. You'll spend your first few months close to the code and close to users: shipping improvements in our core systems, embedding with research teams, and building your own map of their workflows. As the team grows, this role has a natural path into formal people leadership for someone who wants it. Responsibilities Work directly with researchers and the engineers supporting them to understand their workflows, identify the highest-leverage opportunities, and shape what the team builds next Set the technical direction for the team across our platform and our datasets Design and build platform components that other teams plug into — libraries, services, and interfaces such as the metrics library used by training frameworks Own core datasets end to end: the pipelines that produce them, the schemas that define them, and the documentation and guarantees that make researchers trust them Drive convergence toward canonical datasets — including the core data model for RL transcripts — that research teams standardize on Lead complex, multi-quarter projects that span several systems and teams, staying hands-on in the code Raise the team's technical bar through design reviews, mentorship, and the quality of your own work You may be a good fit if you: Have built and operated data-intensive systems at scale — pipelines, storage layers, query systems — with strong instincts for data modeling and schema design that hold up as usage grows Have set technical direction for a team, or owned the architecture of a data platform that other teams build on Treat internal users as customers: you do the discovery work, iterate with users, and measure success by adoption rather than by shipping Understand that researchers aren’t typical internal customers — the work is exploratory by nature, workflows differ from team to team, and requirements are discovered through experiments rather than specified up front Can build for that motion — keeping interfaces stable and data trustworthy while use cases change underneath you, and judging when a quick, disposable solution serves research better than a durable one Lead through influence — aligning engineers and stakeholders without relying on formal authority Are results-oriented and pragmatic, willing to do unglamorous work when it's the highest-leverage thing Are excited about learning the fundamentals of machine learning research (deep ML expertise is not required) Care about the societal impacts of your work Strong candidates may also have Experience with large-scale ETL and columnar or analytical storage (e.g., Spark, BigQuery, ClickHouse, DuckDB, Parquet) Experience with metrics or experiment-tracking systems, or high-volume time-series data Experience with dataset management, cataloging, or lineage tooling Built developer tooling or internal data platforms for demanding technical users — including in domains like quantitative trading, where fast-moving, exploratory data work looks a lot like research A working knowledge of machine learning Worked in, or closely with, an ML research lab Interest in — or experience with — people management and growing engineers 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: $405,000 — $850,000 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: 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 Research Productivity organization builds the infrastructure and systems that accelerate research across Anthropic, including the tools that help our research process get faster and more effective over time. We're looking for an experienced software engineer to join us as we scale this infrastructure at a moment when both demand and scope are growing extremely quickly. This team operates like a startup within a startup. You'll be a strong fit if you like thinking from first principles, iterating fast on infrastructure, and tackling reliability and scalability challenges head-on as the products built on top of your systems evolve underneath you. You'll independently scope complex, multi-month projects, drive cross-org alignment through ambiguous problem spaces, and make the architectural decisions that shape how the infrastructure behind our research tooling gets built. You'll partner directly with research teams to understand their workflows, anticipate how their requirements will change, and design and scale infrastructure that can keep pace. Responsibilities Design, build, and scale infrastructure and systems that support rapidly increasing usage, where requirements and workload continue to evolve as the products built on top of them evolve Independently scope and lead complex, multi-month engineering projects, from an ambiguous starting point through to a production system Drive cross-organizational alignment on technical direction, working through ambiguous problem spaces with multiple stakeholders and teams Make architectural decisions that shape the foundation of research infrastructure and tooling across Anthropic Partner directly with researchers to deeply understand their workflows, then anticipate and design for how those needs will change Iterate quickly, favoring pragmatic, first-principles solutions and fast feedback loops over heavy upfront design Take ownership of the reliability and scalability of critical systems as load, usage, and complexity increase Help set technical standards and best practices for the team, and mentor other engineers Minimum Qualifications Experience designing, building, and operating large-scale distributed systems or infrastructure in production A track record of independently scoping and delivering complex, ambiguous, multi-month technical projects Strong software engineering fundamentals and hands-on coding ability Experience making architectural decisions that other engineers and teams build on top of Strong written and verbal communication skills, with experience driving alignment across multiple teams or stakeholders Demonstrated ability to operate effectively in ambiguous, fast-changing environments Strong candidates may also have Experience building infrastructure or platforms specifically for research or machine learning workflows Direct experience navigating the reliability and architectural challenges that come with rapidly scaling systems Experience with distributed systems, cloud infrastructure, and infrastructure-as-code Familiarity with the compute, tooling, and workflow needs of large-scale machine learning research Experience operating in a startup or startup-like environment, i.e. a small, fast-moving team with high autonomy Prior experience as a technical lead or mentor for other engineers 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: $405,000 — $625,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: New York; San Francisco 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’re building a platform that covers the whole life of an LLM: training it, deploying it, and observing it in production. We already run multi-node training, elastic inference, sandboxes, and distributed volumes, and we control the infrastructure underneath. We’re looking for research depth in post-training to sit alongside our systems and product work. We are looking for research scientists with a strong track record 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 have a PhD in computer science, machine learning, or a related field - You have a demonstrated record of research accomplishments in reinforcement learning, machine learning, foundation models, or related fields - You have experience with large-scale training and inference infrastructure, including distributed systems and multi-node GPU clusters - You’ve developed, trained, optimized, or deployed state-of-the-art large-scale models - You have first-author publications at leading venues such as NeurIPS, ICML, ICLR, CoRL, CVPR, UAI, JMLR, or TMLR - You have a mission-driven mindset and a strong desire to translate research advances into meaningful product impact - You have a collaborative spirit and can work effectively across research and engineering teams Candidates with a master’s degree and significant research or industry experience will also be considered.

Location: New York; San Francisco 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: Most of the value of owning a model shows up at serving time. We’re building a platform that covers the whole life of an LLM (train it, deploy it, observe it), and inference is where teams feel the difference every day. We already run elastic inference, sandboxes, distributed volumes, and multi-node training, and we control the infrastructure underneath, so the serving stack is ours to shape rather than something we resell. You will do hands-on inference research at Modal, working with the research lead to pick high-impact bets and owning them end to end. The bets that matter most are the ones that move cost per token and tail latency on the workloads our customers actually run. WHAT YOU’LL WORK ON: - Own end-to-end inference research bets: speculative decoding, disaggregated prefill/decode, quantization (FP8, INT4), KV-cache and memory management, autoscaling for spiky serverless traffic, and whatever else the research agenda calls for - Train custom speculators against real production traffic and feed what you learn back into target models; acceptance length is the metric that decides the win - Work directly with customers alongside our Forward Deployed Engineers to deploy and tune models, and bring what you learn back into the research - Carry and expand collaborations with outside research labs, for example: - our work with ZLab on DFlash https://modal.com/blog/spec-is-all-u-need, a speculator design built on KV injection and blockwise parallel drafting - our work with SGLang on specdec https://modal.com/blog/host-overhead-inference-efficiency and multimodal inference https://modal.com/blog/boosting-multimodal-inference-performance-by-greater-than-10-with-a-single-python-dictionary performance - our work on Flash Attention 4 kernels https://modal.com/blog/flash-attention-4-faster - Work with engineering to turn frontier serving techniques into products: primitives for disaggregation, fast weight refresh for models that keep training after deployment, observability for quality and latency in production, or even a next-generation inference engine - Help shape the research agenda None of the above is prescriptive; your work will help guide our future. YOU MAY BE A FIT IF: - You have a research-leaning or systems background in LLM inference, with work you can point to - You’re fluent in the LLM serving stack, from kernels and quantization up to schedulers and autoscaling - You have a record of shipping research or systems that other people build on, whether in a lab or in industry - You have the drive to independently take a research bet from idea to result, working in the open with the rest of the team - You’re able to work in person in our NYC or San Francisco office

Location: San Francisco, CA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role We're seeking an exceptional Research Engineer to join our Life Sciences team at Anthropic. Our team is organized around the north star goal of accelerating progress in the life sciences, from early discovery through translation, by an order of magnitude. Our team likes to think across the whole model stack. In this role, you'll combine your deep expertise in machine learning engineering to develop novel evaluation frameworks and training strategies that push the frontier of what AI can achieve in biology. You'll work at the intersection of cutting-edge AI and the biological sciences, developing rigorous methods to measure and improve model performance on complex scientific tasks. You'll collaborate closely with world-class researchers and engineers to build AI systems that can engage in all phases of research and development, while maintaining our commitment to safety and beneficial impact. Previous experience in life sciences is welcome, but not required for this role. Minimum Qualifications Demonstrated experience training and evaluating large language models Proficiency in Python and familiarity with modern ML development practices Experience building and managing data pipelines for large-scale datasets Comfortable navigating ambiguity and developing solutions in rapidly evolving research environments Strong written and verbal communication skills, with the ability to work independently while collaborating effectively across cross-functional teams Preferred Qualifications 8+ years of machine learning experience Prior work experience in AI and biology, including graduate studies (molecular biology, biochemistry, computational biology, or related fields) Experience working with large-scale biological datasets Published research or practical experience in scientific AI applications or long-horizon reasoning Background in reinforcement learning and/or pretraining Knowledge of containerization technologies (e.g., Docker, Kubernetes) and cloud deployment at scale Demonstrated ability to work across multiple domains, such as language modeling, systems engineering, and scientific computing Contributions to open-source scientific software or databases 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.