Вакансии

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: Worldwide About Plata Plata is one of the fastest-growing fintech companies in the world. In just 3 years, we've grown to 3M+ customers and reached a $5B+ valuation.  We're now strengthening our Risk & Decisioning core team and are looking for a Principal AI Engineer to set the technical bar and build best-in-class, production-grade models that materially move business metrics. Why this role You will own our foundation model program end to end - from research direction to models running in production and driving real credit decisions. Your models directly move the numbers that matter: cost of risk, approval rates, and portfolio NPV across a fast-scaling credit portfolio. You'll work with one of the richest financial datasets in LATAM billions of transactions and behavioral events across 3M+ customers, growing daily. 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: 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.

Location: US / Remote Workplace: remote About the Company Born from groundbreaking research at Columbia University and Yale University, CertiK is a leading Web3 security company focused on securing blockchain protocols, smart contracts, and decentralized applications through cutting-edge security research, formal verification, and AI-powered technology. Founded in 2017 and headquartered in New York City, CertiK provides end-to-end security solutions including smart contract audits, penetration testing, on-chain monitoring, incident response, and compliance services for some of the largest projects in the digital asset ecosystem. Today, CertiK supports thousands of enterprise clients and Web3 projects globally, with a distributed international team spanning North America, Asia, and Europe. The company is backed by leading investors including Coatue, Goldman Sachs, Insight Partners, and Sequoia Capital, and has been recognized by organizations such as the World Economic Forum and CB Insights for its contributions to blockchain security innovation.   About This Role This role is for a Web3 security researcher who can act as a public face for CertiK. This means having a strong social media presence, speaking at conferences, and being openly helpful to the wider security community. This doesn't mean you have to have tens of thousands of followers on X, but you do need to be comfortable interacting with the general public. The specific type of security researcher is flexible - contract/chain auditors, pen testers, engineers, and multi-talented individuals can apply. This is a great role for a generalist. Responsibilities Work closely with the Web3 Task Force Team to create stronger connections to the blockchain ecosystem. Speak at conferences about your area of expertise on behalf of CertiK. Complete interviews about various Web3 security topics. Perform tasks cross-functionally among the various teams at CertiK. Support the marketing and feedback collection of CertiK’s products and services. Requirements 3+ years of experience in the Web3 space. Strong social media presence and high visibility. Experience speaking at conferences or in front of large audiences. Strong technical skills in the areas of web3 security or related fields. Bonus Points Large number of social media followers. Previous experience being the public face of an organization.

Location: San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The Computer Use team focuses on teaching Claude to see, use, and understand computer interfaces. As a Research Engineer on the team, you'll work on advancing our models' ability to reliably and safely operate real software. We're looking for someone who's genuinely excited about both the research and the product sides of computer use. Your work will translate directly into model improvements in our own and our customers' products. You can try Claude's computer use capabilities today through the Claude in Chrome extension and Claude Cowork. Key Responsibilities: Design and run experiments to improve Claude's perception and agentic capabilities Develop robust, reliable evaluation frameworks for measuring our models' ability to complete complex computer tasks Build and improve computer use and vision reinforcement learning training environments Create pipelines and tools to test and validate complex RL environments Collaborate with teams across the model training and infrastructure stack to improve our production training setup Partner with product teams to bring research advances into production Minimum Qualifications: Software engineering experience and proficiency in Python Experience training, fine-tuning, or evaluating machine learning models Strong communication skills and a collaborative working style Care about the societal impacts and safety of your work Preferred Qualifications: Experience training models for computer use or other agentic capabilities Experience with reinforcement learning, particularly in long-horizon or sparse-reward settings Familiarity with multimodal model training Experience building evaluations or benchmarks for agentic systems Experience building reinforcement learning environments, simulation systems, or large-scale ML infrastructure Experience working closely with product teams to drive model improvements 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: $500,000 — $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: New York WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: As a Senior Research Engineer in our Safety team, you will play a key role in helping develop safer, more secure, and more reliable models. Your primary focus will be on building tools to enable easy data synthesis, analysis, and management, for complex combinations of real and synthetic data that is used in both model training and evaluation. You will own the cohesive vision of these tooling repositories. You will work closely with a team of research scientists and engineers to create tooling that enables tighter experimentation cycles, better data coverage of the real world, and more scientific rigour. You will have a lot of autonomy and need to be opinionated about what areas of the codebase need elegance and standards, and where that would be overengineering. You will be given high level experimental problems that need to be solved with efficient pipelines, and design and implement the solutions. Your data analysis will collaboratively feed into modelling decisions and experimentation. This role combines expertise in software engineering, statistics, and data science. If any of these topics sound interesting to you, we encourage you to apply. You will be working on the Modelling Safety and Trust team, so interest in these areas is a plus, but is not at all required. Please Note: We have offices in London, Edinburgh, Paris, Toronto, Montreal and New York, but we also embrace being remote-friendly! This role has timezone restrictions (UK, Europe, or ET) but no location restrictions. Key Responsibilities: - Design and implement robust data pipeline tooling that enables frequent, low-friction data generation and annotation - Create cohesive data infrastructure that supports continuous parallel operation with model experimentation - Establish standardized processes for data validation, analysis, and improvement of data both training and evaluation - Collaborate with the ML modeling team to align data capabilities with experimental needs - Maintain opinionated, well-documented solutions that become team standards - Develop systematic analysis frameworks to identify incoming data sources and benchmark coverage gaps Qualifications: - Extremely strong software engineering skills. - Strong statistical skills and experience evaluating scientific experiments related to data collection and model performance. - Proficiency in programming languages such as Python and ML frameworks (e.g., PyTorch) and Big Data Analytics (e.g. BigQuery, SQL) - Demonstrated ability to own complex technical projects from conception to deployment - Opinionated approach to technical architecture with ability to make principled decisions - Understanding of ML data requirements and the intersection of data engineering with modeling workflows Working Location: This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.   FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Location: London, UK 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 RL Scaling Science team studies how reinforcement learning behaves as we scale it (across model size, compute, and task horizon) and turns that understanding into the training recipes behind our frontier models. As a Research Engineer on this team, you'll design and run large-scale experiments to understand and resolve bottlenecks, build the benchmarks that make long-horizon progress measurable, and ship validated findings directly into production training. This role lives at the boundary between research and engineering. The problems are open, the experiments run at frontier scale, and the path from a robust result to production is short. Key responsibilities Design, run, and interpret large-scale RL experiments, reasoning rigorously about what the data does and doesn't show Investigate how RL improves as horizon, compute, and model size grow Build and maintain benchmarks for long-horizon RL so progress is measurable and reproducible Translate validated findings into production training recipes, exercising judgment about when a result is robust enough to ship Debug complex issues at the seam where research meets infrastructure - failures that only appear at scale Partner closely with adjacent RL teams across research and engineering and advance our overall RL stack Minimum qualifications Strong empirical research skills in Reinforcement Learning, large-scale ML training, or a closely adjacent area Demonstrated ability to own large experiments end-to-end, from design through interpretation Proficiency in Python and experience working with large-scale or distributed ML systems Comfort operating at the research/systems boundary, including debugging where the two meet Care about the societal impacts of AI and responsible scaling Preferred qualifications Published or shipped work in long-horizon RL or RL fundamentals Experience translating research findings into production training recipes Demonstrated large scale industry impact via RL interventions Experience working on frontier-scale training runs with long trajectories Representative projects Design a benchmark suite for long-horizon RL that distinguishes genuine capability gains from artifacts of evaluation setup Take a promising experimental finding, stress-test it across model scales, and work with training teams to land it in a production recipe Investigate an unexpected scaling trend in an RL run and trace it to a root cause spanning algorithm, data, and infrastructure 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: £375,000 — £640,000 GBP 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: Bangalore, India 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 training and optimizing vision-language models with a focus on real-world deployment. Your work will span the full model development lifecycle - from data curation and training pipeline design to model evaluation and optimization - with the goal of building models that are both highly capable and practical to deploy at scale. You will work across a wide spectrum of multimodal architectures integrating text and vision, applying state-of-the-art research to improve model quality, efficiency, and domain-specific performance. We expect you to bring a research-driven mindset combined with strong engineering discipline - someone who can identify the right technique for a given problem, implement it rigorously, and measure its impact clearly. You will work closely with a small, high-caliber team where your contributions will have direct and meaningful impact. If you are passionate about pushing the boundaries of what multimodal AI can achieve in production environments, this is your opportunity. Responsibilities Conduct end-to-end research and engineering on vision-language models, covering training, evaluation, and optimization across the full model development lifecycle. Design and implement post-training pipelines including supervised fine-tuning, knowledge distillation, and reinforcement learning from human feedback. Develop and maintain high-quality multimodal datasets, including data curation, filtering, and balancing for domain-specific tasks. Drive model efficiency and deployability, adapting models for resource-constrained environments using compression and optimization techniques. Design and implement evaluation frameworks and benchmarks to measure model performance, robustness, and real-world task success. Build and scale training workflows across distributed GPU infrastructure. Identify and resolve bottlenecks in training pipelines to achieve state-of-the-art model quality on target benchmarks. Contribute to and leverage open-source ecosystems including models, datasets, and tooling to accelerate development. Stay current with the latest research in multimodal learning and vision-language systems, translating relevant findings into practical improvements. Publish research findings in top-tier AI conferences and journals where applicable. Degree in Computer Science, Machine Learning, or a related field; MS/PhD preferred. Strong experience with multimodal post-training workflows including supervised fine-tuning, knowledge distillation, and reinforcement learning from feedback. Hands-on experience with parameter-efficient fine-tuning and distributed training frameworks. Demonstrated ability to build and improve vision-language models with measurable results on standard benchmarks or real-world tasks. Experience adapting models for resource-constrained environments. Proven open-source contributions in multimodal AI on GitHub or HuggingFace. Publications at top AI conferences (NeurIPS, ICML, ICLR, CVPR, ECCV etc.) 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.