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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 The Anthropic Residency is our paid, fixed-term research program, with placements lasting between six weeks and six months. We are looking for people who have led ambitious work in a variety of technical fields and want to transition to full-time work focused on making sure AI systems are safe and beneficial for all of humanity. If you are a professor in a quantitative discipline, a technical founder or a staff-level engineer and want to transition to work on transformative AI, we want to hear from you. Background in machine learning is not required. What to expect A concrete, scoped project matched to your background, with direct mentorship from Anthropic researchers Access to Claude models and GPU compute Connection to the broader AI safety and security research community Interview process The interview process will include an initial application, recruiter screen, technical assessment, research discussion and a reference check. Applications for this round close on November 22, 2026 (11:59 PM PT). We expect to make offers in January 2027. You may be a good fit if you Are motivated by making sure AI is safe and beneficial for society as a whole Are excited to transition into working on empirical AI research full-time Have led ambitious work in a variety of technical fields (professor in a quantitative discipline, a technical founder or a staff-level engineer) Thrive in fast-paced, collaborative environments Can implement ideas quickly and communicate clearly Strong candidates may also Have experience authoring research papers in machine learning, NLP, or AI safety Have experience with LLMs, reinforcement learning or Kubernetes clusters and complex shared codebases Mentors and research areas Potential Anthropic mentors include: Sara Price : leads the Alignment Training team; background in data science Ethan Perez : leads the Alignment Science team; background in machine learning Evan Hubinger : leads the Alignment Stress-Testing team; formerly at The Machine Intelligence Research Institute (MIRI) Samuel Marks : leads the Scalable Oversight team; background in mathematics Elana Simon : researches the interpretability of language models and biological models; background in biomedical informatics Belinda Li : researches how language models represent the world, users, and themselves; background in natural language processing (PhD, MIT) Jack Lindsey : leads the Model Psychology team; background in theoretical neuroscience Nicholas Carlini : researches adversarial machine learning and AI security; previously at DeepMind and Google Brain Jascha Sohl-Dickstein : works at the intersection of safety and pretraining; background in physics and neuroscience, formerly Principal Scientist at Google Brain Residents will work on empirical research questions in collaboration with Anthropic's research teams. Areas we are particularly interested in include: AI safety & alignment Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains. Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios. Model Organisms: Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise. Model Internals and Interpretability: Advancing our understanding of the internal workings of large language models to enable more targeted interventions and safety measures. Safeguards Research: Developing robust defenses against adversarial attacks, comprehensive evaluation frameworks for model safety, and automated systems to detect and mitigate potential risks before deployment. AI Welfare: Improving our understanding of potential AI welfare and developing related evaluations and mitigations. AI security & frontier red-teaming Stress-testing frontier models to understand the full extent of their current capabilities in fields such as cybersecurity, robotics or cryptography. ML systems & reinforcement learning Improving how frontier models are trained and building the infrastructure behind that research, from model architecture, optimizers and training data to accelerator simulators, hardware backends and RL environment pipelines. For a fuller picture of the questions we care about, see our Recommendations for Technical AI Safety Research Directions and recent publications from our Alignment , Interpretability and Frontier Red teams. Logistics Duration: Placements are six weeks, three months or six months, depending on team fit and candidate preferences. Other lengths between three and six months are possible by exception. Location: This application is for a remote UK position, with the option to work from our London office. However, we prefer that Residents work from our San Francisco office, where most of our research staff is based, but remote participation is possible, especially for shorter placements. Most full-time research roles at Anthropic require relocation to San Francisco. Remote participants need to be based in a country where Anthropic can hire. Commitment: Full-time role for the duration of the residency. Compensation: £265,000 to £375,000/year pro-rated for the duration of the residency + benefits Visa sponsorship: We sponsor visas for placements of three months or longer. For six-week placements we encourage remote participation from a country where Anthropic can hire. Applicants may also be considered for the Anthropic Fellows Program . 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: £265,000 — £375,000 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: 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 The Anthropic Residency is our paid, fixed-term research program, with placements lasting between six weeks and six months. We are looking for people who have led ambitious work in a variety of technical fields and want to transition to full-time work focused on making sure AI systems are safe and beneficial for all of humanity. If you are a professor in a quantitative discipline, a technical founder or a staff-level engineer and want to transition to work on transformative AI, we want to hear from you. Background in machine learning is not required. What to expect A concrete, scoped project matched to your background, with direct mentorship from Anthropic researchers Access to Claude models and GPU compute Connection to the broader AI safety and security research community Interview process The interview process will include an initial application, recruiter screen, technical assessment, research discussion and a reference check. Applications for this round close on November 22, 2026 (11:59 PM PT). We expect to make offers in January 2027. You may be a good fit if you Are motivated by making sure AI is safe and beneficial for society as a whole Are excited to transition into working on empirical AI research full-time Have led ambitious work in a variety of technical fields (professor in a quantitative discipline, a technical founder or a staff-level engineer) Thrive in fast-paced, collaborative environments Can implement ideas quickly and communicate clearly Strong candidates may also Have experience authoring research papers in machine learning, NLP, or AI safety Have experience with LLMs, reinforcement learning or Kubernetes clusters and complex shared codebases Mentors and research areas Potential Anthropic mentors include: Sara Price : leads the Alignment Training team; background in data science Ethan Perez : leads the Alignment Science team; background in machine learning Evan Hubinger : leads the Alignment Stress-Testing team; formerly at The Machine Intelligence Research Institute (MIRI) Samuel Marks : leads the Scalable Oversight team; background in mathematics Elana Simon : researches the interpretability of language models and biological models; background in biomedical informatics Belinda Li : researches how language models represent the world, users, and themselves; background in natural language processing (PhD, MIT) Jack Lindsey : leads the Model Psychology team; background in theoretical neuroscience Nicholas Carlini : researches adversarial machine learning and AI security; previously at DeepMind and Google Brain Jascha Sohl-Dickstein : works at the intersection of safety and pretraining; background in physics and neuroscience, formerly Principal Scientist at Google Brain Residents will work on empirical research questions in collaboration with Anthropic's research teams. Areas we are particularly interested in include: AI safety & alignment Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains. Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios. Model Organisms: Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise. Model Internals and Interpretability: Advancing our understanding of the internal workings of large language models to enable more targeted interventions and safety measures. Safeguards Research: Developing robust defenses against adversarial attacks, comprehensive evaluation frameworks for model safety, and automated systems to detect and mitigate potential risks before deployment. AI Welfare: Improving our understanding of potential AI welfare and developing related evaluations and mitigations. AI security & frontier red-teaming Stress-testing frontier models to understand the full extent of their current capabilities in fields such as cybersecurity, robotics or cryptography. ML systems & reinforcement learning Improving how frontier models are trained and building the infrastructure behind that research, from model architecture, optimizers and training data to accelerator simulators, hardware backends and RL environment pipelines. For a fuller picture of the questions we care about, see our Recommendations for Technical AI Safety Research Directions and recent publications from our Alignment , Interpretability and Frontier Red teams. Logistics Duration: Placements are six weeks, three months or six months, depending on team fit and candidate preferences. Other lengths between three and six months are possible by exception. Location: We prefer that Residents work from our San Francisco office, where most of our research staff is based, but remote participation is possible, especially for shorter placements. Most full-time research roles at Anthropic require relocation to San Francisco. Remote participants need to be based in a country where Anthropic can hire. Commitment: Full-time role for the duration of the residency. Compensation: $350,000 to $500,000/year pro-rated for the duration of the residency + benefits Visa sponsorship: We sponsor visas for placements of three months or longer. For six-week placements we encourage remote participation from a country where Anthropic can hire. Applicants may also be considered for the Anthropic Fellows Program . 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: Remote (US) About the company Hi, we're Ondo Finance. Our mission is to provide institutional-grade, blockchain-enabled investment products and services. We have both a technology arm that develops decentralized finance technology, and an asset management arm that creates and manages tokenized funds. We are the global leader in tokenized treasuries, tokenized stocks and ETFs, and are building the future of institutional-grade financial services onchain. Founded by folks from Goldman Sachs Digital Assets Team, we’re backed by some of the best investors in the world including Founders Fund, Coinbase Ventures, Pantera Capital, Tiger Global, and more. We are currently the leaders in the space in terms of AUM and are well capitalized to continue growing the firm. We're fully remote, with team members across the U.S. About the role Ondo's user research team is small but mighty, and works horizontally across the whole product suite, not inside a single pod. We're hiring a senior quantitative user researcher to build and lead quantitative research across the portfolio. You'll set your own UXR strategy with minimal guidance, take on the hardest decision-shaping questions whichever team they come from, and answer them with evidence that holds up at scale. What You'll Do Own the research intake and quant roadmap. Field research questions from product, design, GTM and leadership. Turn vague asks into answerable questions, decide which need quantitative evidence (and which don't), and prioritize where evidence most reduces risk on our biggest bets. Answer the big questions before launch. Use MaxDiff and conjoint to show which features, benefits and messages actually drive decisions. Design pre-launch experiments (stimulus tests, matched cohorts, vignettes, threshold studies) with decision rules set before fielding, and pretest instruments with cognitive interviews. Build measurement and segments that last. Adapt or develop validated scales for trust, perceived security and comprehension, track them across products and markets, and turn our user profiles into statistically validated, sized segments. Get data you can trust, and connect it to behavior. Design sampling, translation and fraud controls for a global, crypto-native audience, and partner with data science to link survey data with product data. Evaluate AI-assisted experiences. Design human evaluation for AI-assisted finance features: rubrics, rater reliability, and whether user trust matches how reliable the system actually is. Be leadership's partner for evidence. Present findings as recommendations with effect sizes, confidence and what would change the call. Give quick, right-sized answers to ad-hoc leadership questions, whether that's existing data, a fast pulse survey or a full study. Set the standards that let PMs and designers run sound lightweight surveys or data-driven validation themselves. Requirements 7+ years of UX or applied research experience, including at least 5 years leading quantitative studies end to end, from study design through recommendation. Experience leading quantitative research studies for financial products (e.g., brokerage, trading, banking, payments, wealth or lending). A self-starter who operates with minimal guidance: you scope ambiguous problems, set your own priorities, and drive studies to a decision. Deep survey science expertise: sampling and weighting, instrument design, bias control, data-quality screening, and programming in Qualtrics or similar. Hands-on experience designing and analyzing MaxDiff and/or conjoint studies. Strong applied statistics: regression, power analysis, and experimental and quasi-experimental design. Fluency in R or Python for analysis and visualization. You have an existing AI-assisted research setup, from drafting survey instruments to writing and reviewing analysis code, and you proofread/validate every output with rigor. A track record of quantitative work that changed a product or business decision, and the ability to explain uncertainty clearly to executives and non-researchers. Nice to Have Experience with crypto, onchain or tokenized-asset products. A master’s or PhD in a quantitative field such as statistics, survey methodology, behavioral economics, or marketing science. Depth in advanced methods, such as hierarchical Bayes and simulators, psychometric scale development, segmentation, pricing research, or SQL and multilevel modeling. Experience evaluating AI or LLM-powered products, such as human evaluation design, rubrics or rater reliability. Research across markets in LATAM, APAC or EMEA, including translating survey instruments. Responsible AI-native research workflows, such as using LLM tools to speed up instrument design, coding of open-ended responses or analysis. What we offer Competitive compensation including but not limited to salary, future token rights, and/or equity (according to your preferences) — We are well-funded and believe that great talent deserves great compensation. Full benefits (medical, vision, and dental) and flexible vacation policy (PTO). Remote-first team across many countries — You will be an early team member helping shape our vision, culture, and design practices. A+ colleagues — Our team includes alumni from: Goldman Sachs, Blackrock, Two Sigma, Bridgewater, SpaceX, AWS, Meta, Google, McKinsey, Circle, Uniswap. Best-in-class investors — We are proud to be backed by leading crypto experts and VCs, including Pantera Capital, Founders Fund and Coinbase Ventures.

SpAItial is looking for PhD Research Interns to explore 3D generation and reconstruction, develop and evaluate novel methods, and publish research findings.

Isomorphic Labs is looking for a Research Leader in Toxicology to lead safety assessment strategies, regulatory toxicology work, CRO partnerships, and AI-driven toxicology platform development.

— Обладатели исследовательской докторской степени в профильной области не допускаются. — Как правило, нужна магистратура или эквивалентная квалификация в естественных науках или науках о жизни и исследовательский опыт.

Location: San Francisco, CA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's Life Sciences team is building a research group focused on biological discoveries by combining AI with hands-on biological research. We're seeking a research scientist who combines deep chemistry knowledge with machine learning expertise to join the team. Our goal is to speed up progress in the life sciences by an order of magnitude, and this role puts that mission into practice. You'll be a computational chemist who knows reactions, synthesis, and medicinal chemistry in depth and has hands-on experience training and evaluating machine learning models. Above all, you'll build the reinforcement learning environments, datasets, and evaluations that teach Claude chemistry. Working alongside our bench chemists and AI researchers, you'll turn real chemistry problems and experimental data into training tasks and measure whether the models get better at them. This role is fully on-site in the San Francisco Bay Area. Key responsibilities Design and build reinforcement learning environments for chemistry end to end, including task design, data curation, automated grading, and checks against shortcuts and data leakage Design and source new experimental datasets, environments, and evaluations with our bench chemists, and external research and academic partners Develop evaluations that measure model performance on real chemistry tasks, such as reaction outcome prediction, synthesis planning, structure elucidation, and compound design, and feed results back to research teams Run and analyze training experiments with AI research teams, diagnose where models fail at chemistry, and prioritize what to build next Ensure datasets and environments are high quality and well documented, including sources, train and test splits, and failure modes Communicate findings clearly to chemistry, AI research, and life sciences audiences Minimum qualifications PhD in chemistry, computational chemistry, or a related field Deep knowledge of organic and medicinal chemistry, including reactions, synthesis, and SAR, with experimental data as your ground truth Strong Python skills and solid machine learning fundamentals, with hands-on experience training and evaluating models Comfort navigating ambiguity and defining problems in a rapidly evolving research environment Ability to work independently while collaborating effectively across cross-functional teams, with strong written and verbal communication skills Preferred qualifications Experience with reinforcement learning, large language models, or building evaluations and benchmarks Track record of supporting drug discovery programs alongside medicinal and synthetic chemists Experience curating chemistry datasets from the literature, patents, or lab notebooks, including reaction, ADMET, and spectroscopic data Cheminformatics and molecular modeling skills, for example RDKit, docking, free energy calculations, or quantum chemistry Hands-on synthetic or medicinal chemistry experience at the bench Publications, patents, or open-source software demonstrating scientific impact The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $300,000 — $320,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Singapore Workplace: Hybrid About Cantina: Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning. About the Role: Cantina is expanding, and we're looking for a Research Scientist to join our growing Singapore team! In this role, you will drive foundational research on video generation models, taking ownership across the full research cycle and driving post-training research. Furthermore, you'll collaborate closely with data, infrastructure, and adjacent modeling teams to translate research findings into durable model improvements. What You’ll Do: - Build and maintain scalable systems for ingesting, preprocessing, and delivering large-scale video data for model training - Design and scale distributed data pipelines for preprocessing, dataset generation, and repeated dataset refreshes - Own workflow orchestration, job scheduling, monitoring, and failure recovery for large-scale data processing jobs - Implement and maintain containerized pipeline infrastructure using Kubernetes or equivalent orchestration systems - Optimize cloud-based data storage and movement across providers (AWS, GCS, or Azure) for cost, throughput, and operational efficiency - Define and implement best practices for dataset storage layout, versioning, caching, retention, and access patterns - Build tooling to support deduplication workflows at scale, including near-dedup pipelines over large video corpora - Research and develop distillation methods for large-scale diffusion and flow-based video generation models, including guidance distillation and adversarial distillation, with a focus on preserving or improving generation quality while reducing inference cost - Develop reward models and preference-based fine-tuning pipelines that align video generation quality with human judgments across dimensions such as aesthetics, motion quality, and prompt adherence - Analyze the relationship between base model behavior and post-training outcomes, and work with the foundation model team to inform pretraining decisions accordingly What You’ll Bring: - Strong hands-on experience building or scaling large-scale data systems or pipelines for machine learning workflows - Experience with distributed data processing frameworks such as PySpark or Ray, and orchestration tools such as Airflow or equivalent - Familiarity with containerization and container orchestration, including Docker and Kubernetes - Experience working with cloud-based data storage and compute (AWS, GCS, and/or Azure), including tradeoffs around cost, throughput, storage layout, and access patterns - Familiarity with video and media processing tools such as FFmpeg, PyAV, DALI, or OpenCV - Familiarity with multimodal or media data, including video, image, text, and audio - Strong research background in post-training methods for large-scale diffusion or flow-based generative models, with deep hands-on experience in distillation across both inference efficiency and quality preservation - Experience with reward modeling or preference-based fine-tuning for generative models, including RLHF, DPO or equivalent alignment approaches - Solid understanding of the interplay between pretraining and post-training, and how base model properties affect distillation and fine-tuning outcomes - Proficiency in Python and modern machine learning frameworks, with a strong preference for PyTorch or JAX - Track record of independent research, with the ability to drive projects from initial idea through experimental validation - Publications at top-tier venues (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV) preferred - Good understanding of the practical challenges involved in building reliable, scalable, and reproducible data workflows for machine learning systems Benefits We Offer: - Competitive salary and generous company equity - Personal time off and paid holidays - Health insurance - Global travel insurance: Covers you when traveling internationally - Monthly spending stipend: $500 (~S$635) - Equipment: All equipment needed for your home 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 RL teams Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Fable 5.1 and Claude Opus 5.5. Our work spans several key areas: Developing systems that enable models to use computers effectively Advancing code generation through reinforcement learning Pioneering fundamental RL research for large language models Building scalable RL infrastructure and training methodologies Enhancing model reasoning capabilities We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implementing our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish. About the role We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to design silicon. Hardware design is difficult and unforgiving – exactly the sort of domain we want Claude to excel at. You'll leverage your chip design expertise and turn it into tasks and signals for models to learn from. Specifically, you will: Invent, design, and implement RL environments and evaluations for agentic RTL generation, design (including formal) verification, physical design optimization. Work on cross-cutting RL considerations such as EDA-tool latency optimization and proxy rewards. Conduct experiments and shape our roadmap. Deliver your work into research and production training runs. Collaborate with other researchers and engineers across and outside Anthropic. You may be a good fit if you: Have expertise in ASIC or FPGA design: RTL, design verification (UVM, formal methods, coverage-driven), physical design (synthesis, place-and-route, timing closure), PPA optimization, DFT, ECOs. Are fluent with industry EDA tools and processes. Have taped out chips and have experience going from spec to silicon. Know how to balance research exploration with engineering implementation. Are passionate about AI's potential and committed to developing safe and beneficial systems. Strong candidates may also have: Experience with reinforcement learning, evaluations or environments. Built tooling or automation around chip design flows. Worked on ML accelerators or high-performance compute hardware. Familiarity with high-level synthesis or architecture simulators. 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.

Задачи: — Проводить глубокие исследования в конкретных областях для улучшения понимания влияния AGI. — Создавать карту потенциальных исходов. — Строить и совершенствовать инфраструктуру измерений и оценки.

What To Do: Conduct deep research into specific areas to improve our understanding of the impact of AGI, and create the map of potential outcomes; Build and refine measurement infrastructure and evaluation frameworks for a systematic evaluation of AGI.....

Location: United States; Germany; The Netherlands Workplace: Remote PROGRAM OVERVIEW The ClickHouse PhD Research Internship Program offers doctoral students the opportunity to pursue ambitious research alongside ClickHouse Labs researchers and engineers working on some of the most interesting challenges in databases, systems, and data infrastructure. Research interns join ClickHouse Labs for approximately 10-12 weeks to explore meaningful research questions, develop new ideas, and investigate technologies that have the potential to advance both the broader research community and the future of ClickHouse. The program is intentionally designed around research collaboration. Interns are paired with experienced researchers and have the opportunity to pursue work that aligns with their academic interests, ongoing research, and areas of interest within ClickHouse Labs. In some cases, an internship may build on research, a prototype, or a system the student has already begun exploring at their university. We believe the strongest research relationships extend beyond a single summer. Interns may continue their work after returning to their university through academic research, open-source contributions, publications, or other forms of collaboration. Where there is a strong mutual fit, an internship may become the beginning of a longer-term relationship between the researcher, ClickHouse Labs, and the broader academic community. During their internship, researchers will have the opportunity to work closely with ClickHouse Labs, engage with ClickHouse's open-source technology and engineering community, share their findings, and contribute to the intellectual life of the broader research community. Program length: 10-12 weeks, with flexibility based on academic calendars and research needs Typical timing: Summer, beginning approximately June 1 Locations: Opportunities available across the US and Europe, subject to role and employment requirements. Remote first. Research environment: ClickHouse Labs Internship format: Research-focused, mentor-supported, and shaped around areas of mutual research interest Compensation: commensurate with experience, a monthly stipend will be paid RESEARCH AT CLICKHOUSE LABS ClickHouse Labs explores ambitious ideas at the intersection of databases, distributed systems, data infrastructure, and the broader systems research community. We believe some of the most interesting advances in these areas happen through close collaboration between industry and academia. ClickHouse Labs brings together researchers and engineers to explore new ideas, investigate challenging technical problems, and connect academic research with real-world systems at scale. Research interns are an important part of that community. Interns have the opportunity to bring their own research interests and perspectives into ClickHouse Labs while working alongside researchers and engineers who are deeply engaged with both the practical challenges and open research questions shaping the future of data systems. PROGRAM GOALS 1. Identify exceptional PhD researchers working in areas directly relevant to ClickHouse. 2. Give researchers the opportunity to work on meaningful, open-ended problems with ClickHouse Labs. 3. Build long-term relationships with researchers and academic institutions. 4. Encourage continued research and open-source contributions after the internship. 5. Create opportunities for co-authored papers, thesis advising, or other academic collaboration. 6. Increase ClickHouse's visibility within the database and systems research community. 7. Publish and share meaningful research outcomes through the ClickHouse website and other appropriate channels. WHAT MAKES THE PROGRAM DIFFERENT This is intentionally not a highly structured engineering internship and should not detailed planning. ****But the high-level problem area and expectations should defined several months before the internship begins. This flexibility is intentional and allows ClickHouse Labs to respond to: - Emerging research questions - Current ClickHouse priorities - New academic research - Interesting systems or database prototypes - Work candidates have already completed - Opportunities to apply new research ideas to ClickHouse The goal is to identify researchers whose work is already closely aligned with areas ClickHouse cares about and then determine the most valuable research problem to pursue together. CANDIDATE PROFILE We are looking for exceptional PhD students conducting research directly relevant to ClickHouse and the broader database/systems space. IDEAL CANDIDATES - Currently pursuing a PhD in computer science or a closely related field - Conducting research in areas relevant to ClickHouse - Demonstrated research excellence - Strong database systems, distributed systems, hardware, or related technical background - Has demonstrated ability to independently pursue complex research questions - Ideally has prior experience with ClickHouse open source but not required**.** - Has research, prototypes, or publications that could translate into meaningful work with ClickHouse - Interested in continuing research beyond the internship - Potential interest in open-source contribution and academic collaboration CANDIDATE SELECTION PHILOSOPHY The strongest candidates will be researchers whose work is specifically relevant to what ClickHouse Labs wants to explore. A candidate who has already: - Worked with ClickHouse - Built a relevant database or systems prototype - Published research in an area relevant to ClickHouse - Explored a technical problem ClickHouse is interested in may be significantly more valuable than a candidate with an otherwise impressive academic profile but no meaningful connection to our research areas. RESEARCH AREAS & PROJECT SCOPE Unlike an engineering internship program, specific projects do not need to be defined when recruiting begins. Research projects may emerge from: - A candidate's existing research - A recent academic paper - A prototype or system the candidate has developed - An emerging ClickHouse Labs research priority - An opportunity to test or extend an existing research concept within ClickHouse IF THIS IS RELEVANT AND EXCITING TO YOU, PLEASE APPLY! PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new eligible team member who joins our company receives equity. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

- Планировать и проводить UX-исследования: глубинные интервью, опросы, юзабилити-тестирования и другие методы; - Самостоятельно организовывать исследования: искать респондентов, готовить сценарии и проводить интервью;

Arc Institute is looking for a Senior Research Associate Viral Production to produce, quality-control, schedule, and optimize lentivirus supplies for screening and cell-engineering workflows.

Arc Institute is looking for a Research Associate to drive high-throughput antibody discovery, perform molecular biology workflows, and validate discoveries for neurological disease diagnostics.

Задачи: — Продвигать и оценивать методы 3D реконструкции и синтеза новых видов. Требования: — Опыт и знания в области novel view synthesis, optimization, radiance field, 3D reconstruction, multi-view geometry, neural rendering, Deep learning, rendering, camera model, Gaussian splat. Условия: — Работа в офисе в Лондоне.

Задачи: — разработка, оценка и внедрение передовых методов 3D-реконструкции.

Location: Europe; United States Workplace: Remote ABOUT CANTINA Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning. If you're excited about the potential AI has to shape human creativity and social interactions, join us in building the future! ABOUT THE ROLE We’re working on real-time video generation for interactive characters, spanning realistic humans, stylized characters, and non-human identities. We want these characters to feel present: to speak, react, and express themselves through their face, gestures, and body language. This brings together a set of closely connected research problems. A character needs a consistent appearance, expressive and coherent motion, and a natural relationship between speech and movement. The model also needs to respond quickly and keep generating reliably as an interaction unfolds. Quality, controllability, and efficiency all matter. You’ll work across streaming generation, fine-tuning, distillation, inference optimization, and data. Some problems will call for a new training approach; others will come down to better examples, a more useful evaluation, or understanding a bottleneck in the implementation. Part of the job is figuring out which intervention is worth making. You’ll have substantial freedom to propose directions, investigate unfamiliar ideas, and shape what we work on next. We’re looking for someone who enjoys that freedom and can turn it into a steady sequence of useful experiments. WHAT YOU’LL WORK ON - Real-time and streaming generation. Develop models that support responsive interaction while maintaining visual quality and consistency over time. Investigate latency, continuity, and how model behavior changes during longer generation. - Character expression and motion. Explore how appearance, facial expression, gesture, and body movement come together into a coherent character. Improve the relationship between speech and motion across different identities and visual styles. - Fine-tuning and post-training. Adapt video models to new data, behaviors, and capabilities. Investigate training objectives and conditioning methods, and follow promising ideas through implementation and evaluation. - Distillation and inference optimization. Make generation faster and more efficient. Work across model-level approaches and runtime improvements, measuring their effects on quality, latency, and memory. - Data development. Spend meaningful time with the actual training examples. Identify missing coverage and recurring failure modes, improve curation and sampling, and develop data that helps models learn the behavior we want. - Evaluation. Build comparisons that make progress visible and failures understandable. Combine quantitative measurements with careful inspection of generated video and the experience of interacting with the model. - Research and collaboration. Read relevant work, reproduce and adapt useful ideas, and share early experiments, demos, and findings. Help the team decide what deserves more time and compute. WHAT YOU’LL BRING - Experience training or fine-tuning video or related generative models. - Ability to learn unfamiliar methods quickly and turn ideas into useful experiments. - Good experimental judgment, ownership, and clear communication. You’ll have freedom to explore new approaches. We expect fast iteration and early sharing of results. COMPENSATION When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.   Benefits for U.S.-based roles: - Competitive salary and generous company equity - Medical, dental, and vision insurance – 99.99% of premiums covered by Cantina - 42 days of paid time off, including: - 15 PTO days - 10 sick days - 15 company holidays - 2 floating holidays - Generous parental leave & fertility support - 401(k) retirement savings plan - Lifestyle spending account – $500/month to use however you’d like - Complimentary lunch and snacks for in-office employees - One Medical membership, and more!