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Prime Intellect is looking for an Applied Researcher to lead customer-embedded reinforcement-learning programs from discovery through deployment.

Location: Remote - US Founded in 2012, Bugcrowd is the preemptive security platform that unifies exposure discovery and assessment, offensive testing, and intelligence shaped by AI and human insight to help organizations avoid, discover, and validate real-world risk. Bugcrowd helps security teams move faster by identifying the exposures that matter most so they can act first and stay ahead of attackers. By combining the power of humans and AI, teams can preempt attack paths and prevent breaches. Based in San Francisco and New Hampshire, Bugcrowd is supported by General Catalyst, Rally Ventures, Costanoa Ventures, and others. Visit www.bugcrowd.com . Job Summary The Bugcrowd RL and Reasoning Team focuses on pushing the boundaries of autonomous cybersecurity by building authentic, verifiable reinforcement learning environments for world-leading foundational AI companies. As a Reinforcement Learning Engineer specializing in Reinforcement Learning from Verifiable Rewards (RLVR), you will design and scale automated verification pipelines that transform real-world software vulnerabilities into deterministic reward functions. In this role, you will bridge the gap between low-level security analysis and modern LLM reasoning models, engineering environments where AI agents learn to discover, exploit, and remediate software vulnerabilities with mathematical certainty. Instead of relying on subjective human feedback, your work directly powers the rigorous, verifiable reward signals that teach next-generation frontier AI models how to master complex cybersecurity domain logic. You will work at the intersection of fuzzing, dynamic program analysis, system exploitation, and scalable ML infrastructure to shape the safety and offensive/defensive capabilities of future artificial intelligence. Essential Duties and Responsibilities Design, build, and deploy high-throughput RLVR (Reinforcement Learning from Verifiable Rewards) environments that evaluate LLM action sequences against deterministic execution outcomes. Develop automated test harnesses, sandboxes, and verification engines that convert complex vulnerability research (e.g., memory corruption, web security, logic bugs) into binary pass/fail reward signals. Integrate Bugcrowd’s Mayhem automated analysis platform and real-world vulnerability feeds into continuous, scalable RL environment generation pipelines. Architect safe, isolated, and highly reproducible execution environments (using Docker, BuildKit, or Nix) capable of running thousands of simultaneous agent-driven exploitation and patching trajectories. Collaborate directly with researchers at frontier AI labs including Anthropic, OpenAI, and Cohere to define standard benchmark formats, observation spaces, and verifiable evaluation metrics for cybersecurity tasks. Implement precise telemetry, ground-truth verification algorithms, and trajectory logging to analyze agent reasoning paths and prevent reward hacking or false positives. Build low-level instrumentation and debugging tools to monitor memory states, process executions, and network behaviors during agent interaction cycles. Optimize infrastructure performance and environment reset latency to support massive-scale parallel sampling and distributed RL training workflows. Benchmark and evaluate frontier AI model performance across diverse offensive and defensive security challenges, such as automated fuzzing, exploit payload generation, and patch validation. Education, Experience, Knowledge, Skills, and Abilities Understanding of RL training workflows used by modern LLM systems, specifically execution-based feedback or Reinforcement Learning from Verifiable Rewards (RLVR). Proficiency developing applications in Python and low-level systems programming in C, with Rust experience being a strong plus. Solid understanding of software vulnerabilities, binary exploitation, fuzzing methodologies, or program analysis. Experience with DevOps pipelines (e.g., GitHub Actions), reproducible builds (Docker, BuildKit, Nix), and comfort working with Linux systems and low-level debugging. Experience working with or building benchmark environments (e.g., CTFs, SWE-bench, security challenges, or execution sandboxes). Preferred Experience Experience designing custom reward functions, ground-truth verifiers, or automated grading engines for AI safety and reasoning models. Background in low-level program analysis tools, sanitizers (e.g., ASan/MSan), compiler instrumentation, or automated exploit generation tools. Proven track record of participating in or developing competitive cybersecurity benchmarks, CTFs, or open-source AI evaluation frameworks. Working Conditions and Physical Requirements The ideal candidate must be able to complete all physical requirements of the job with or without reasonable accommodation. Sitting and / or standing - Must be able to remain in a stationary position 50% of the time Carrying and / or lifting - Must be able to carry / move laptop as needed throughout the work day. Environment - remote, work-from-home 100% of the time. Pay Range Disclosure At Bugcrowd, we strive for fairness, equality and to create an environment that allows our people to perform at their very best. Our compensation philosophy is to foster a collaborative community that rewards, attracts and retains the best possible talent. The provided salary details are based on US national averages and we retain the flexibility to tailor to the needs of the business. The national estimate for the current base range for the position of $176,400 - $242,550. This position may also be eligible to participate in a discretionary bonus program or commission plan, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance. Culture At Bugcrowd, we understand that diversity in the workplace is vital to a company’s success and growth. We strive to make sure that people are included and have a sense of being part of making Bugcrowd not only a great product but a great place to work. We regularly hear from both customers and researchers that Bugcrowd feels like a family, and we strive to maintain that internally as well. Our team consists of a broad range of people: musicians, adventure sports junkies, nature lovers, parents, cereal enthusiasts, night owls, cyclists, artists—you get the point. At Bugcrowd, we are solving security threats and vulnerabilities that are relevant to everyone, therefore we believe solving these problems takes all kinds of backgrounds. We value the perspectives and experiences people from underrepresented backgrounds bring. Disclaimer This position has access to highly confidential, sensitive information relating to the technologies of Bugcrowd. It is essential that the applicant possess the requisite integrity to maintain the information in the strictest confidence. The company is authorized to obtain background checks for employment purposes under state and federal law. Background checks will be conducted for positions that involve access to confidential or proprietary information (including trade secrets). Background checks may include Social Security verification, prior employment verification, personal and professional references, educational verification, and criminal history. Applicants with conviction histories will not be excluded from consideration to the extent required by law. Any personal data you submit in connection with your application will be processed in compliance with Bugcrowd's Privacy Policy, which you may review here: https://www.bugcrowd.com/privacy . Equal Employment Opportunity: Bugcrowd is EOE, Disability/Age Employer. Individuals seeking employment at Bugcrowd are considered without regards to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, or sexual orientation. Bugcrowd is committed to the full inclusion of all qualified individuals. In keeping with our commitment, Bugcrowd will take the steps to assure that people with disabilities are provided reasonable accommodations. Accordingly, if reasonable accommodation is required to fully participate in the job application or interview process, to perform the essential functions of the position, and/or to receive all other benefits and privileges of employment, please contact HR at ADA at bugcrowd.com. Apply at: https://www.bugcrowd.com/about/careers/

Location: Europe; New York; Toronto Workplace: Remote 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! Key Overview: As a Member of Technical Staff in Post-Training, you will train our large language models (LLMs) with reinforcement learning and develop methods that help them generalize beyond the distribution they were trained on. You will design the training recipes, environments, and evaluations that tell us whether a model has truly learned a skill or has only fitted its training tasks, and turn what works into the recipes behind our production models. You will work closely with the post-training data infrastructure, evaluation, and pre-training teams. Key Responsibilities - Design, run, and analyse large-scale RL post-training experiments (e.g., RLHF, RL from verifiable rewards). - Research and develop methods that improve out-of-distribution generalization, such as reward design, environment and task diversity, curricula, and regularization. - Build held-out evaluations that measure generalization to unseen tasks, domains, and formats, and that catch reward hacking and benchmark overfitting. - Diagnose training dynamics and failure modes, including instability, reward over-optimization, and capability regressions. - Turn successful research into robust, reproducible training recipes for production models. Qualifications - Strong research and software engineering skills, with hands-on experience training LLMs. - Experience applying reinforcement learning to LLMs (e.g., PPO, GRPO, RLHF, RLVR), ideally at scale. - Track record of work on generalization, robustness, or distribution shift, through publications or shipped models. - Rigorous experimental practice: clean ablations, careful controls, and sound conclusions from noisy results. - Experience with distributed training (e.g., JAX, PyTorch, FSDP, Megatron) is a plus. - Excellent communication skills. - Deep understanding of data quality principles and best practices: demonstrates willingness to dive deep into data while possessing strong intuition for assessing the quality of datasets and anticipating how different data characteristics will impact model training outcomes. 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: 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 Frontier research runs on more than research alone. Behind every model launch, review, and cross-team program is a web of decisions, priorities, and follow-through that keeps hundreds of researchers moving in the same direction. Research Operations owns that work: we bring clarity to ambiguity, provide stability during change, and continually deepen our domain knowledge so that researchers can spend their time on the problems only they can solve. It is one of the highest-leverage functions at Anthropic. We are hiring an experienced operator to embed with the leadership of our reinforcement learning (RL) organization, the team responsible for training Claude to be more capable, more reliable, and more aligned. You will shape what leadership spends its time on, make sure priority decisions reach the right decision-makers and get made quickly, and clear the path so leaders can focus on the work that matters most. You will report to the Manager of Research Operations and work daily with RL org leaders and their teams. Key responsibilities Own leadership's time. Shape the week around the leaders' specific, most timely priorities, set agendas so meetings are spent on decisions that move the org forward, and cut what isn't critical so leaders stay focused on the areas that are highest-leverage for them. Triage what reaches leadership. Resolve what you can, route what belongs elsewhere, and thoughtfully escalate the rest. Enable wise decision-making and follow through. Give leaders what they need to decide quickly, communicate those decisions to collaborators, and track commitments through to completion. See problems coming. Surface resourcing gaps, technical bottlenecks, and stalled action-items before they become blockers. Scale leadership voice. Meetings are ephemeral; docs are forever. Draft materials for org-wide announcements, programming, and planning sessions. Protect leadership’s time for creating durable comms that set direction in turbulent times. Make the org run better. Find the friction that keeps recurring and find the people who can fix the process behind it. Be a thought partner. Offer honest, candid counsel to leaders, including crisp and kind disagreement. Minimum qualifications Care deeply about Anthropic's mission and let it underpin all judgment calls you make across the org. Technical fluency to engage substantively with researchers: you can follow a discussion about an RL training run, read a set of eval results and form reasonable hypotheses about what they mean, and hold your own in a technical conversation. You don't need to have trained a model yourself, but you should understand the model training process and have the depth to work closely with leading researchers. Clear writing: you turn dense, jargon-heavy material into summaries and plans that technical collaborators across the RL org and beyond can act on An eye for great dashboards: you can look at data visualizations and scope improvements that make lessons at-a-glance clearer and grounded. Strong follow-through and attention to detail; nothing falls through the cracks on your watch. Ability to push back on senior stakeholders to offer strategic guidance as needed, disagree without causing discord, and influence without direct authority. Comfort with ambiguity and a fast-changing environment; you stay steady when the pace is relentless and the stakes are as high as they can possibly be. Experience in an operations, chief of staff, program management, or comparable role supporting senior leaders in a fast-moving technical organization High integrity. This role sits close to decisions that execute on Anthropic’s mission, and you care about getting them right. Preferred qualifications Experience supporting a senior leader directly in a chief of staff or related function Time in a research institution, lab, or other research-heavy organization Track record of anticipating what colleagues and leaders will need and acting on it before being asked Comfort in an environment where the right answer changes hour to hour Experience moving routine operational work to AI agents, or the ability to script and build small tools Familiarity with AI safety or other existential risks. 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: $210,000 — $260,000 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. 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.

Anthropic Fellows Program is designed to foster AI research and engineering talent by providing funding and mentorship to promising technical talent regardless of previous experience. Fellows primarily use external infrastructure such as open-source models and public APIs to work on empirical projects aligned with Anthropic's research priorities, aiming to produce public outputs like paper submissions. The program runs multiple cohorts yearly with rolling application reviews, expecting a start in January 2027 but flexible start dates can be requested. The program includes 4 months of full-time research, direct mentorship from Anthropic researchers, access to a shared workspace, connection to the AI safety and security research community, a weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD plus benefits varying by country, and funding for compute (~$15k/month) and other research expenses. The interview process involves an initial application and reference check, technical assessments and interviews, and a research discussion. Applicants are encouraged to apply even if they do not meet every qualification, with emphasis on diversity and inclusion. There are two main fellow tracks: ML Systems & Performance Fellows: - Mentors include Alwin Peng and Zygi Straznickas. - Projects may involve building CPU simulators for accelerator workloads, adding backends for accelerators on open source projects, building on-demand infrastructure, and creating complex synthetic data or environment pipelines. - Candidates should have strong software engineering skills, experience with complex ML systems, ability to balance research and engineering rigor, comfort with large-scale distributed systems and high-performance computing, experience with training or evaluating large language models, and skills in analyzing and debugging model training. - Fluency in Python and full-time availability are required. Reinforcement Learning Fellows: - Mentors include Ruhua Jiang, Kaidi Cao, Sunny Duan, David Brandfonbrener, Colt Steele, Dino Distefano, and Will Williams. - Projects may include building model-based tools to understand and improve AI training data, research on generalization, creating RL environments to improve Claude models and safety-related tasks, and research and implementation of RL algorithms. - Candidate criteria are similar to ML Systems Fellows. Logistics: - Participants must have work authorization and be located in the US, UK, or Canada during the program. - Shared workspaces are available in San Francisco; remote participation is allowed in the UK, US, or Canada. - Visa sponsorship is not provided; candidates must have or obtain full-time work authorization independently. - The program duration is 4 months full-time, with possible extensions and flexibility for partial commitments. Additional notes: - No guarantee of full-time offers post-program, though strong performers may receive offers or continue impactful AI safety work elsewhere. - Anthropic's full-time role policies do not apply to the Fellows Program. - The company emphasizes collaborative, large-scale AI research with a focus on safety, interpretability, and steerability. - Candidates must be fluent in Python and able to commit full-time. - The program values diversity and encourages applications from underrepresented groups. - Safety advice is provided regarding recruitment communications. Apply at the provided link.

Location: Zürich, CH About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Team The Security team protects Anthropic's AI systems and maintains the trust of our users and society. About the Role At Anthropic, we are pioneering new frontiers in AI that have the potential to greatly benefit society. However, developing advanced AI also comes with risks if not properly safeguarded. As a Research Engineer, you'll help to safely advance the capabilities of our models in incident response, security analysis, vulnerability remediation, and other areas of defensive cybersecurity. This role blends research and engineering, requiring you to both develop novel approaches and realize them in code. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers, engineers, and cybersecurity specialists across and outside Anthropic. This role requires ML experience combined with domain expertise in cybersecurity. You might be an ML researcher with a history of cybersecurity work, or a security professional with a background in ML. Responsibilities Partner with researchers and safety teams across Anthropic to understand their analytical needs and build solutions Develop agentic integrations that allow AI systems to autonomously investigate and act on analytical findings Contribute to the strategic direction of the team, including decisions about what to build, what to partner on, and where to invest This role may require participation in an on-call rotation. You may be a good fit if you Have experience with machine learning. Have experience in cybersecurity research. Have strong software engineering skills. Can 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 Professional experience in security engineering, fuzzing, detection and response, or other applied defensive work. Experience participating in or building CTF competitions and cyber ranges. Academic research experience in cybersecurity or other experimental & research background. Familiarity with RL techniques and environments. Familiarity with LLM training methodologies. 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. 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: UK, London Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ABOUT THE ROLE We are looking for a Senior or Staff Reinforcement Learning Engineer to develop learning-based control policies for humanoid robots. You will design and train reinforcement learning policies that enable dynamic locomotion and loco-manipulation behaviors on real robots. Your work will focus on building scalable training pipelines, designing reward functions and environments, and improving sim-to-real transfer for reliable deployment on hardware. You will work closely with controls and robotics engineers to integrate learned policies into the robot control stack, ensuring stable and robust behavior in real-world conditions. Development will involve continuous iteration between large-scale simulation and hardware experiments. The problems you will work on include dynamic locomotion, balance recovery, contact-rich manipulation, and multi-behavior policy learning. WHAT YOU’LL DO - Design and train reinforcement learning policies for humanoid robot control. - Build scalable simulation and training pipelines (e.g., Isaac Lab, MuJoCo). - Design reward functions, observation spaces, and curricula for complex behaviors. - Improve robustness and sim-to-real transfer of learned policies. - Deploy and evaluate policies on real robotic systems. - Integrate policies into the control stack. WHAT WE'RE LOOKING FOR - MS or PhD in Robotics, Machine Learning, Computer Science, or related field. - Strong experience with reinforcement learning (e.g., PPO, SAC, offline RL). - Experience applying RL to robotics or physical systems. - Experience deploying learned policies on real robotic systems. - Experience with physics-based simulation environments (e.g., Isaac Lab, MuJoCo). - Strong programming skills in Python and/or C++. NICE TO HAVE: - Experience with RL for locomotion or legged robots. - Experience with sim-to-real transfer. - Familiarity with robot dynamics, control, or whole-body control. WHAT WE OFFER - Competitive equity: stock options with meaningful upside as we scale. - 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown). - Private healthcare, including virtual and in-person care. - Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in-office. - Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics. - Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

Location: UK, London Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ABOUT THE ROLE We're hiring a Reinforcement Learning Engineer to join our Autonomy team based in London. In this role you will leverage reinforcement learning in both simulation and physical reality to build highly performant and robust manipulation policies. WHAT YOU'LL DO - Train language-vision conditioned manipulation policies via reinforcement learning (RL) in simulation and in the real world. - Construct challenging and diverse suites of manipulation tasks in simulation. - Partner with teleoperations to collect trajectories in simulation for behavior cloning. - Partner with testing and operations to establish real-world RL training pipelines. - Experiment with various ways of bringing policies trained in simulation to the real world. WHAT WE'RE LOOKING FOR - 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it. - Hands‑on with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference. - Experience solving real problems using reinforcement learning with deep neural networks in any domain. - Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code. - You are self-driven, pro-active, communicate efficiently, document experiments clearly and communicate trade‑offs crisply. NICE TO HAVE - Experience with simulators for robotics (Isaac Sim, MuJoCo etc.) - Experience in RL for robotics. - Experience building infrastructure for large-scale RL (e.g. using ray). - Publications at ICLR/ICML/NeurIPS or equivalent open‑source contributions. - Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks. WHAT WE OFFER - Competitive equity: stock options with meaningful upside as we scale. - 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown). - Private healthcare, including virtual and in-person care. - Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in-office. - Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics. - Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

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 RL Teams Our Reinforcement Learning teams play 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 our latest Claude models. 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 implement 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 write, edit, test, debug, and ship real software — end to end, on real codebases, with real tools — and to do it correctly, fast, and safely. This role blends research and engineering. You'll design RL environments and coding tasks, build the reward signals and verifiers that capture what "good code" means, run training experiments on frontier models, diagnose why a model does (or doesn't) get better at a class of software-engineering work, and improve the speed and reliability of the pipelines that make all of that iterate fast. Code RL spans several focus areas — from agentic coding behaviors and code correctness, to long-horizon autonomous engineering, to high-performance code for accelerators — and we'll match you to the area where you'll have the most impact. You may be a good fit if you: Have strong software-engineering skills and deep Python expertise, including async/concurrent programming Are comfortable owning systems end to end and debugging across the stack Can balance research exploration with engineering implementation, and engage rigorously in shaping experimental design and interpreting results Care about code quality, testing, and performance Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems Strong candidates may also have: Experience with reinforcement learning, RLHF, post-training, or LLM finetuning Built coding agents, code-execution sandboxes, eval harnesses, verifiers, or developer tooling Background in program analysis, testing, verification, compilers, or formal methods Experience with PyTorch and large-scale distributed training; performance profiling and optimization of ML systems CUDA / GPU or TPU kernel experience and accelerator-performance intuition Experience with virtualization and sandboxed code execution environments Related roles If your background leans toward one of these areas specifically, you may also want to look at these postings: Research Engineer, Performance RL (Reinforcement Learning) — teaching Claude to write correct, fast code for accelerators Research Engineer, Universes — long-horizon, ultra-realistic agentic training environments Research Engineer, Cybersecurity RL (Reinforcement Learning) — RL for security-relevant coding capabilities 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. 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: United Kindom, United Kingdom; Buenos Aires, Argentina; Bogotá, Colombia; Sao Paulo, Brazil; Montevideo, Uruguay; Bangalore, India; Chittagong, Bangladesh; Karachi, Pakistan; Tokyo, Japan; Hanoi, Vietnam; Taipei, Taiwan; Bangkok, Thailand; London, United Kingdom; Lugano, Switzerland; Barcelona, Spain; Dubai, United Arab Emirates; Sofia, Bulgaria; Prague, Czechia; Belgrade, Serbia; Copenhagen, Denmark; Tallinn, Estonia; Athens, Greece; Tbilisi, Georgia; Budapest, Hungary; Dublin, Ireland; Sliema, Malta; Oslo, Norway; Amsterdam, Netherlands; Warsaw, Poland; Lisbon, Portugal; Bucharest, Romania; Stockholm, Sweden; Brussels, Belgium; Tel Aviv, Israel; Istambul, Türkiye; Nicosia, Cyprus Workplace: remote Join Tether and Shape the Future of Digital Finance At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction. Innovate with Tether Tether Finance: Our innovative product suite features the world’s most trusted stablecoin, USDT , relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services. But that’s just the beginning: Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities. Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET , our flagship app that redefines secure and private data sharing. Tether Education : Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity. Tether Evolution : At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways. Why Join Us? Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry. If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you. Are you ready to be part of the future? About the job As a member of the AI model team, you will drive innovation in multi-modal reinforcement learning to advance next-generation intelligent systems. Your work will focus on optimizing decision-making and adaptive behavior across integrated data modalities such as text, images and audio to deliver enhanced intelligence, robust performance, and domain-specific capabilities for real-world challenges. You will develop and scale reinforcement learning techniques within complex multi-modal architectures, including diffusion-based generative models and autoregressive models for multimodal understanding, as well as resource-efficient models designed for constrained hardware environments. This includes conducting research on reinforcement learning algorithms for multimodal models, spanning diffusion models for image autoregressive models for multimodal reasoning, and unified multimodal frameworks. You are expected to have deep expertise in designing multi-modal reinforcement learning systems and a strong background in advanced model architectures, with a hands-on, research-driven approach to building and deploying novel algorithms and training frameworks. You will design and develop RL infrastructure and reward modeling strategies to enable efficient large-scale training, improve training stability, and mitigate reward hacking and related failure modes. Your responsibilities also include curating multi-modal simulation environments and training datasets, improving baseline policy performance across modalities, and identifying and resolving bottlenecks in multi-modal learning and reward optimization. In addition, you will explore next-generation reinforcement learning paradigms that more directly and effectively learn from environment feedback, with the goal of unlocking superior, domain-adapted AI performance in dynamic, real-world environments. Responsibilities Conduct research on reinforcement learning algorithms for multimodal models, including diffusion-based approaches for image autoregressive models for multimodal understanding, and unified frameworks that integrate multiple modalities. Design and build reinforcement learning infrastructure that supports scalable, distributed training across multimodal systems while maintaining efficiency and reliability. Develop and refine reward modeling strategies that improve training stability, align model behavior with desired outcomes, and mitigate reward hacking and related failure modes. Create and curate multimodal simulation environments and datasets to support robust training, evaluation, and benchmarking of reinforcement learning systems. Design and conduct rigorous benchmarking and evaluation protocols to measure model performance, track progress against baselines, and validate improvements across multimodal tasks. Analyze and optimize policy performance across modalities by identifying bottlenecks in training, credit assignment, and cross-modal alignment. Investigate and develop next-generation reinforcement learning paradigms that more effectively learn from environment feedback, with the goal of achieving superior state-of-the-art (SOTA) performance. Publish research findings in top-tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV etc. A Master's degree in Computer Science or a related field is required; a PhD in Machine Learning, NLP, Computer Vision, or a closely related discipline is preferred, along with a strong track record of AI research and publications in top-tier conferences. Proven experience running large-scale reinforcement learning experiments in multimodal and vision-centric systems, including online RL settings, with demonstrated impact on domain-specific decision-making and measurable improvements in policy performance. Deep understanding of reinforcement learning algorithms and optimization methods applied to vision and multimodal learning problems, with a focus on improving policy stability, exploration, and sample efficiency in complex, high-dimensional environments involving images, video, and other modalities. Strong proficiency in PyTorch and deep learning frameworks for vision and multimodal AI, with hands-on experience building end-to-end RL pipelines covering simulation, training, evaluation, and deployment in production-grade systems. Demonstrated ability to apply empirical research to solve core RL challenges in multimodal and vision tasks, such as sample inefficiency, exploration-exploitation tradeoffs, and training instability, along with experience designing robust evaluation frameworks and iterating on algorithmic improvements to advance agent performance. Proven track record of research publications in top-tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, 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.

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 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 Sonnet 4.5 and Opus 4.5. 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 implement 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 As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation. Representative projects: Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows. Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models. Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows. Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research. You may be a good fit if you: Are proficient in Python and async/concurrent programming with frameworks like Trio Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX) Have industry experience in machine learning research Can balance research exploration with engineering implementation Enjoy pair programming (we love to pair!) Care about code quality, testing, and performance Have strong systems design and communication skills Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems Strong candidates may have: Familiarity with LLM architectures and training methodologies Experience with reinforcement learning techniques and environments Experience with virtualization and sandboxed code execution environments Experience with Kubernetes Experience with distributed systems or high-performance computing Experience with Rust and/or C++ Strong candidates need not have: Formal certifications or education credentials Academic research experience or publication history Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: £260,000 — £630,000 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. 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 Horizons The Horizons team leads Anthropic's reinforcement learning (RL) research and development, playing a critical role in advancing our AI systems. We've contributed to every Claude release, with significant impact on the autonomy, coding, and reasoning capabilities of Anthropic's models. About the role We're hiring for the Cybersecurity RL team within Horizons. As a Research Engineer, you'll help to safely advance the capabilities of our models in secure coding, vulnerability remediation, and other areas of defensive cybersecurity. This role blends research and engineering, requiring you to both develop novel approaches and realize them in code. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers, engineers, and cybersecurity specialists across and outside Anthropic. The role requires domain expertise in cybersecurity paired with interest or experience in training safe AI models. For example, you might be a white hat hacker who's curious about how LLMs could augment or transform your work, a security engineer interested in how AI could help harden systems at scale, or a detection and response professional wondering how models could enhance defensive workflows. You may be a good fit if you: Have experience in cybersecurity research. Have experience with machine learning. Have strong software engineering skills. Can 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: Professional experience in security engineering, fuzzing, detection and response, or other applied defensive work. Experience participating in or building CTF competitions and cyber ranges. Academic research experience in cybersecurity. Familiarity with RL techniques and environments. Familiarity with LLM training methodologies. 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. 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 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 Sonnet 4.5 and Opus 4.5. 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 implement 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 As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation. Representative projects: Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows. Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models. Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows. Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research. You may be a good fit if you: Are proficient in Python and async/concurrent programming with frameworks like Trio Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX) Have industry experience in machine learning research Can balance research exploration with engineering implementation Enjoy pair programming (we love to pair!) Care about code quality, testing, and performance Have strong systems design and communication skills Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems Strong candidates may have: Familiarity with LLM architectures and training methodologies Experience with reinforcement learning techniques and environments Experience with virtualization and sandboxed code execution environments Experience with Kubernetes Experience with distributed systems or high-performance computing Experience with Rust and/or C++ Strong candidates need not have: Formal certifications or education credentials Academic research experience or publication history Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $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. 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.

Prime Intellect is looking for an Applied Researcher to build post-training and evaluation systems for multimodal computer-use agents.

Prime Intellect is looking for an Applied Researcher to build reinforcement-learning systems for quantitative research and systematic decision-making.

Prime Intellect is looking for an Applied Researcher to build and evaluate reinforcement-learning agents with external robotics teams.

Location: Pasig, Philippines Neolytix is a healthcare services and technology organization that supports medical practices and healthcare businesses through specialized operational solutions. We operate with globally distributed teams and are committed to delivering operational excellence, maintaining strong compliance standards, and creating an engaging employee experience across all locations. We are seeking an experienced, commercially minded Human Resources Business Partner (HRBP) to support leaders and employees across the Philippines, Latin America, and the United States. This individual will act as a strategic advisor and hands-on HR partner, helping business leaders build high-performing teams while ensuring fair, consistent, compliant, and people-centered HR practices. The HRBP will partner with functional and operational leaders across the Philippines, Latin America, and the United States to align people strategy with business goals. The role is responsible for advising on employee relations, performance management, workforce planning, engagement, talent development, organizational change, and policy implementation. This position requires a practical understanding of the challenges of managing distributed, multicultural teams. The successful candidate will be able to navigate regional employment practices, work effectively across time zones, maintain high levels of confidentiality, and balance employee advocacy with Neolytix’s operational and client-service requirements. Key ResponsibilitiesRegional Business Partnership Partner closely with business leaders across the Philippines, Latin America, and the United States to understand workforce priorities, operational needs, organizational challenges, and people-related risks. Translate business objectives into regional people plans covering headcount planning, hiring, retention, performance, talent development, and employee engagement. Provide proactive HR guidance to leaders on team structure, role clarity, manager effectiveness, internal mobility, succession planning, and organizational change. Act as a trusted advisor and coach to people managers, supporting them in making fair, timely, and business-aligned people decisions. Collaborate with HR, Operations, Finance, Talent Acquisition, Payroll, and Legal stakeholders to ensure coordinated people support across regions. Identify regional trends, recurring employee concerns, and organizational risks; recommend practical and scalable improvements. Employee Relations and Workplace Investigations Manage and advise on sensitive employee-relations matters, including conduct concerns, attendance, performance issues, conflicts, grievances, workplace investigations, corrective action, and separations. Ensure employee-relations matters are addressed promptly, objectively, confidentially, and with appropriate documentation. Guide managers through performance improvement plans, coaching plans, written warnings, final notices, and other corrective-action processes. Conduct or support workplace investigations in accordance with company policy, local employment requirements, and applicable escalation protocols. Partner with HR leadership and legal counsel, as appropriate, on complex employee-relations matters and high-risk decisions. Maintain consistency in the application of policies and employment practices while accounting for country-specific legal and cultural considerations. Regional Compliance and Policy Administration Support implementation and consistent application of Neolytix HR policies across the Philippines, Latin America, and the United States. Maintain working knowledge of key employment-practice requirements applicable to supported regions, including employment classification, working hours, leave administration, wage-and-hour considerations, disciplinary practices, employee documentation, and separation processes. Partner with local HR, Payroll, Finance, and external advisors to ensure appropriate country-specific compliance and documentation. Identify policy gaps, process risks, and inconsistent practices; recommend updates that balance regional requirements with a cohesive global employee experience. Support audit readiness and provide HR documentation, reports, and records as needed. Escalate legal, regulatory, and employee-relations risks to HR leadership and appropriate internal or external counsel. Performance, Talent, and Leadership Development Support the design and execution of performance-management processes, including goal setting, performance reviews, calibration discussions, development planning, and performance-improvement processes. Coach managers to give clear feedback, set measurable expectations, document performance concerns, and address issues early. Partner with business leaders and Talent Acquisition on workforce planning, job design, hiring priorities, interview quality, and onboarding readiness. Support talent reviews, succession planning, and identification of high-potential employees and critical roles. Help implement leadership-development, manager-training, and employee-learning initiatives that build capability across regions. Ensure career development and internal-mobility practices are equitable, transparent, and aligned with business needs. Employee Engagement and Culture Use employee-engagement surveys, pulse checks, retention data, exit-interview feedback, and manager input to identify cultural strengths and opportunities. Partner with leaders to develop clear and measurable action plans in response to engagement and employee-experience findings. Support initiatives that promote inclusion, recognition, effective communication, wellness, and connection among remote and distributed employees. Facilitate or support manager conversations, employee listening sessions, and culture-building activities across regional teams. Promote Neolytix values and reinforce a consistent, respectful, and accountable employee experience across the employee lifecycle. HR Operations, Data, and Reporting Maintain accurate, timely, and confidential HR documentation for supported employee populations. Partner with HR Operations, Payroll, Benefits, and HRIS teams to resolve employee concerns and ensure smooth people processes. Analyze regional people metrics, including attrition, retention, absenteeism, time-to-fill, engagement, internal mobility, performance outcomes, and employee-relations trends. Develop reports and concise insights for business leaders and HR leadership, highlighting risks, root causes, and recommended actions. Identify opportunities to streamline workflows, improve service delivery, standardize documentation, and automate recurring HR processes. Ensure employee data is handled in accordance with company standards, access controls, privacy requirements, and applicable data-protection obligations.

Location: Quezon City, Metro Manila, Philippines Neolytix is a healthcare services and technology organization that supports medical practices and healthcare businesses through specialized operational solutions. We operate with globally distributed teams and are committed to delivering operational excellence, maintaining strong compliance standards, and creating an engaging employee experience across all locations. We are seeking an experienced, commercially minded Human Resources Business Partner (HRBP) to support leaders and employees across the Philippines, Latin America, and the United States. This individual will act as a strategic advisor and hands-on HR partner, helping business leaders build high-performing teams while ensuring fair, consistent, compliant, and people-centered HR practices. The HRBP will partner with functional and operational leaders across the Philippines, Latin America, and the United States to align people strategy with business goals. The role is responsible for advising on employee relations, performance management, workforce planning, engagement, talent development, organizational change, and policy implementation. This position requires a practical understanding of the challenges of managing distributed, multicultural teams. The successful candidate will be able to navigate regional employment practices, work effectively across time zones, maintain high levels of confidentiality, and balance employee advocacy with Neolytix’s operational and client-service requirements. Key ResponsibilitiesRegional Business Partnership Partner closely with business leaders across the Philippines, Latin America, and the United States to understand workforce priorities, operational needs, organizational challenges, and people-related risks. Translate business objectives into regional people plans covering headcount planning, hiring, retention, performance, talent development, and employee engagement. Provide proactive HR guidance to leaders on team structure, role clarity, manager effectiveness, internal mobility, succession planning, and organizational change. Act as a trusted advisor and coach to people managers, supporting them in making fair, timely, and business-aligned people decisions. Collaborate with HR, Operations, Finance, Talent Acquisition, Payroll, and Legal stakeholders to ensure coordinated people support across regions. Identify regional trends, recurring employee concerns, and organizational risks; recommend practical and scalable improvements. Employee Relations and Workplace Investigations Manage and advise on sensitive employee-relations matters, including conduct concerns, attendance, performance issues, conflicts, grievances, workplace investigations, corrective action, and separations. Ensure employee-relations matters are addressed promptly, objectively, confidentially, and with appropriate documentation. Guide managers through performance improvement plans, coaching plans, written warnings, final notices, and other corrective-action processes. Conduct or support workplace investigations in accordance with company policy, local employment requirements, and applicable escalation protocols. Partner with HR leadership and legal counsel, as appropriate, on complex employee-relations matters and high-risk decisions. Maintain consistency in the application of policies and employment practices while accounting for country-specific legal and cultural considerations. Regional Compliance and Policy Administration Support implementation and consistent application of Neolytix HR policies across the Philippines, Latin America, and the United States. Maintain working knowledge of key employment-practice requirements applicable to supported regions, including employment classification, working hours, leave administration, wage-and-hour considerations, disciplinary practices, employee documentation, and separation processes. Partner with local HR, Payroll, Finance, and external advisors to ensure appropriate country-specific compliance and documentation. Identify policy gaps, process risks, and inconsistent practices; recommend updates that balance regional requirements with a cohesive global employee experience. Support audit readiness and provide HR documentation, reports, and records as needed. Escalate legal, regulatory, and employee-relations risks to HR leadership and appropriate internal or external counsel. Performance, Talent, and Leadership Development Support the design and execution of performance-management processes, including goal setting, performance reviews, calibration discussions, development planning, and performance-improvement processes. Coach managers to give clear feedback, set measurable expectations, document performance concerns, and address issues early. Partner with business leaders and Talent Acquisition on workforce planning, job design, hiring priorities, interview quality, and onboarding readiness. Support talent reviews, succession planning, and identification of high-potential employees and critical roles. Help implement leadership-development, manager-training, and employee-learning initiatives that build capability across regions. Ensure career development and internal-mobility practices are equitable, transparent, and aligned with business needs. Employee Engagement and Culture Use employee-engagement surveys, pulse checks, retention data, exit-interview feedback, and manager input to identify cultural strengths and opportunities. Partner with leaders to develop clear and measurable action plans in response to engagement and employee-experience findings. Support initiatives that promote inclusion, recognition, effective communication, wellness, and connection among remote and distributed employees. Facilitate or support manager conversations, employee listening sessions, and culture-building activities across regional teams. Promote Neolytix values and reinforce a consistent, respectful, and accountable employee experience across the employee lifecycle. HR Operations, Data, and Reporting Maintain accurate, timely, and confidential HR documentation for supported employee populations. Partner with HR Operations, Payroll, Benefits, and HRIS teams to resolve employee concerns and ensure smooth people processes. Analyze regional people metrics, including attrition, retention, absenteeism, time-to-fill, engagement, internal mobility, performance outcomes, and employee-relations trends. Develop reports and concise insights for business leaders and HR leadership, highlighting risks, root causes, and recommended actions. Identify opportunities to streamline workflows, improve service delivery, standardize documentation, and automate recurring HR processes. Ensure employee data is handled in accordance with company standards, access controls, privacy requirements, and applicable data-protection obligations.