{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/reinforcement-learning"},"x-facet":{"type":"skill","slug":"reinforcement-learning","display":"Reinforcement Learning","count":100},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. 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We build evaluation frameworks that ensure these models operate reliably, safely, and effectively under real-world constraints.</p>\n<p>As an ML Engineer, you will design, implement, and scale automated evaluation pipelines that help customers trust and operationalize advanced AI systems across defense, intelligence, and federal missions.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Developing and maintaining automated evaluation pipelines for ML models across functional, performance, robustness, and safety metrics, including LLM-judge–based evaluations.</li>\n</ul>\n<ul>\n<li>Designing test datasets and benchmarks to measure generalization, bias, explainability, and failure modes.</li>\n</ul>\n<ul>\n<li>Building evaluation frameworks for LLM agents, including infrastructure for scenario-based and environment-based testing.</li>\n</ul>\n<ul>\n<li>Conducting comparative analyses of model architectures, training procedures, and evaluation outcomes.</li>\n</ul>\n<ul>\n<li>Implementing tools for continuous monitoring, regression testing, and quality assurance for ML systems.</li>\n</ul>\n<ul>\n<li>Designing and executing stress tests and red-teaming workflows to uncover vulnerabilities and edge cases.</li>\n</ul>\n<ul>\n<li>Collaborating with operations teams and subject matter experts to produce high-quality evaluation datasets.</li>\n</ul>\n<p>This role requires an active security clearance or the ability to obtain a security clearance.</p>\n<p>Ideal candidates will have experience in computer vision, deep learning, reinforcement learning, or NLP in production settings, strong programming skills in Python, and background in algorithms, data structures, and object-oriented programming.</p>\n<p>Nice to have qualifications include graduate degree in CS, ML, or AI, cloud experience (AWS, GCP), and model deployment experience.</p>\n<p>Compensation packages at Scale for eligible roles include base salary, equity, and benefits. 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The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity-based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.</p>\n<p><strong>About Us</strong></p>\n<p>At Scale, our mission is to develop reliable AI systems for the world&#39;s most important decisions. Our products provide the high-quality data and full-stack technologies that power the world&#39;s leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. 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As a Research Scientist/Research Engineer, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. 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Since 2018, we&#39;ve been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.</p>\n<p>We&#39;re the only company offering three integrated solutions for frontier AI development:</p>\n<p>Enterprise Platform &amp; Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale</p>\n<p>Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models</p>\n<p>Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling</p>\n<p>Why Join Us</p>\n<p>High-Impact Environment: We operate like an early-stage startup, focusing on impact over process. You&#39;ll take on expanded responsibilities quickly, with career growth directly tied to your contributions.</p>\n<p>Technical Excellence: Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.</p>\n<p>Innovation at Speed: We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.</p>\n<p>Continuous Growth: Every role requires continuous learning and evolution. You&#39;ll be surrounded by curious minds solving complex problems at the frontier of AI.</p>\n<p>Clear Ownership: You&#39;ll know exactly what you&#39;re responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.</p>\n<p>The role</p>\n<p>We’re hiring a Forward Deployed Engineering Manager to lead the design, development, and delivery of reinforcement learning environments for agentic AI systems.</p>\n<p>You’ll manage a team responsible for building sandboxed, reproducible environments,terminal-based workflows, browser automation, and computer-use simulations,that power both model training and human-in-the-loop evaluation. This is a hands-on leadership role where you’ll set technical direction, guide execution, and stay close to architecture and critical systems.</p>\n<p>What You’ll Do</p>\n<p>Lead, hire, and develop a high-performing team of Forward Deployed Engineers, setting a high bar for ownership, velocity, and technical quality</p>\n<p>Own the RL environment roadmap, aligning team execution with customer needs and evolving model capabilities</p>\n<p>Oversee development of sandboxed environments (terminal, browser, tool-augmented workspaces) that support deterministic execution and multi-step agent interaction</p>\n<p>Ensure reliability, observability, and data integrity through strong instrumentation (logging, trajectory capture, state snapshotting)</p>\n<p>Drive infrastructure excellence across containerization, sandboxing, CI/CD, automated testing, and monitoring</p>\n<p>Partner cross-functionally with data operations, product, and leading AI labs to define task design, evaluation protocols, and environment requirements</p>\n<p>Enable rapid prototyping and iteration, helping the team move from ambiguous requirements to production-ready systems quickly</p>\n<p>Stay close to the technical details,reviewing architecture, unblocking complex issues, and guiding design decisions</p>\n<p>What We’re Looking For</p>\n<p>5+ years of software engineering experience (Python)</p>\n<p>2+ years of experience managing or leading engineers in fast-paced environments</p>\n<p>Strong experience with containerization and sandboxing (Docker, Firecracker, or similar)</p>\n<p>Solid understanding of reinforcement learning fundamentals (MDPs, reward design, episode structure, observation/action spaces)</p>\n<p>Background in infrastructure, developer tooling, or distributed systems</p>\n<p>Strong debugging skills and systems thinking across layered, containerized environments</p>\n<p>Ability to operate in ambiguity and translate loosely defined problems into clear execution plans</p>\n<p>Excellent communication and stakeholder management skills</p>\n<p>Preferred</p>\n<p>Experience building or working with RL environments (Gym, PettingZoo) or agent benchmarks (SWE-bench, WebArena, OSWorld, TerminalBench)</p>\n<p>Familiarity with cloud infrastructure (GCP or AWS)</p>\n<p>Prior experience in AI/ML platforms, data companies, or research environments</p>\n<p>Contributions to open-source projects in RL, agents, or developer tooling</p>\n<p>Why This Role Matters</p>\n<p>RL environment quality is a critical bottleneck in advancing agentic AI. Poorly designed or unreliable environments introduce noise into training loops and directly impact model performance.</p>\n<p>In this role, you’ll lead the team building the environments that define how models learn,working across a range of cutting-edge projects with leading AI labs. Alignerr offers the speed and ownership of a startup with the scale and resources of Labelbox, giving you the opportunity to have outsized impact on the future of AI.</p>\n<p>About Alignerr</p>\n<p>Alignerr is Labelbox’s human data organization, powering next-generation AI through high-quality training data, reinforcement learning environments, and evaluation systems. We partner directly with leading AI labs to build the data and infrastructure that push model capabilities forward.</p>\n<p>Life at Labelbox</p>\n<p>Location: Join our dedicated tech hubs in San Francisco or Wrocław, Poland</p>\n<p>Work Style: Hybrid model with 2 days per week in office, combining collaboration and flexibility</p>\n<p>Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making</p>\n<p>Growth: Career advancement opportunities directly tied to your impact</p>\n<p>Vision: Be part of building the foundation for humanity&#39;s most transformative technology</p>\n<p>Our Vision</p>\n<p>We believe data will remain crucial in achieving artificial general intelligence. As AI models become more sophisticated, the need for high-quality, specialized training data will only grow. Join us in developing new products and services that enable the next generation of AI breakthroughs.</p>\n<p>Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs.</p>\n<p>Any emails from Labelbox team members will originate from a @labelbox.com email address. 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Specifically, you will:</p>\n<ul>\n<li>Invent, design and implement RL environments and evaluations.</li>\n<li>Conduct experiments and shape our research roadmap.</li>\n<li>Deliver your work into training runs.</li>\n<li>Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.</li>\n</ul>\n<p>We&#39;re looking for someone with expertise in accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch), and experience with balancing research exploration with engineering implementation.</p>\n<p>Strong candidates may also have experience with reinforcement learning, porting ML workloads between different types of accelerators, and familiarity with LLM training methodologies.</p>\n<p>The annual compensation range for this role is $350,000-$850,000 USD.</p>\n<p>Please note that we&#39;re an extremely collaborative group, and we value communication skills. The easiest way to understand our research directions is to read our recent research.</p>\n<p>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.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c9ab5cbc-dd6","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000-$850,000 USD","x-skills-required":["accelerator performance","ML framework programming","reinforcement learning","RL environments and evaluations","experiments and research roadmap","training runs","collaboration with researchers and engineers"],"x-skills-preferred":["CUDA","ROCm","Triton","Pallas","JAX","PyTorch","LLM training methodologies"],"datePosted":"2026-04-18T15:54:02.762Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"accelerator performance, ML framework programming, reinforcement learning, RL environments and evaluations, experiments and research roadmap, training runs, collaboration with researchers and engineers, CUDA, ROCm, Triton, Pallas, JAX, PyTorch, LLM training methodologies","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_01819c10-867"},"title":"PhD Machine Learning Engineer, Intern","description":"<p><strong>Job Description</strong></p>\n<p>We&#39;re excited to offer PhD machine learning engineering internships for the summer of 2026. As an intern, you&#39;ll contribute to critical projects that directly enhance Stripe&#39;s suite of products, focusing on areas such as foundation models used for dozens of tasks e.g. fraud detection, enhanced support, and predicting user behavior.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Develop and deploy large-scale machine learning systems that drive significant business value across various domains.</li>\n<li>Engage in the end-to-end process of designing, training, improving, and launching machine learning models.</li>\n<li>Write production-scale ML models that will be deployed to help Stripe enable economic infrastructure access for a diverse range of businesses globally.</li>\n<li>Collaborate across teams to incorporate feedback and proactively seek solutions to challenges.</li>\n<li>Rapidly learn new technologies and approaches, demonstrating a strong ability to ask insightful questions and communicate the status of your work effectively.</li>\n</ul>\n<p><strong>Who We&#39;re Looking For</strong></p>\n<ul>\n<li>A deep understanding of computer science, obtained through the pursuit of a PhD in Computer Science, Machine Learning, or a closely related field, with the expectation of graduating in winter 2026 or spring/summer 2027.</li>\n<li>Practical experience with programming and machine learning, evidenced by projects, classwork, or research. 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We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.</p>\n<p>Life at Labelbox</p>\n<p>Location: Join our dedicated tech hubs in San Francisco or Wrocław, Poland</p>\n<p>Work Style: Hybrid model with 2 days per week in office, combining collaboration and flexibility</p>\n<p>Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making</p>\n<p>Growth: Career advancement opportunities directly tied to your impact</p>\n<p>Vision: Be part of building the foundation for humanity&#39;s most transformative technology</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_ed5725bb-311","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Labelbox","sameAs":"https://www.labelbox.com/","logo":"https://logos.yubhub.co/labelbox.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/labelbox/jobs/4829775007","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$250,000-$300,000 USD","x-skills-required":["Python","data science libraries","deep learning frameworks","PyTorch","JAX","TensorFlow","supervised fine-tuning","reinforcement learning","agent libraries","benchmarks","proprietary datasets","human-AI interaction","AI ethics"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:52:38.777Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco Bay Area"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, data science libraries, deep learning frameworks, PyTorch, JAX, TensorFlow, supervised fine-tuning, reinforcement learning, agent libraries, benchmarks, proprietary datasets, human-AI interaction, AI ethics","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":250000,"maxValue":300000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_272bd1ad-99d"},"title":"Software Engineer, Sandboxing","description":"<p><strong>About the Role</strong></p>\n<p>Anthropic&#39;s sandboxing infrastructure enables Claude to safely execute code and interact with external systems. As we expand Claude&#39;s capabilities, the reliability, security, and developer experience of this infrastructure becomes increasingly critical. We&#39;re looking for an engineer to join the sandboxing team and help shape both the client-side library/API and the underlying infrastructure.</p>\n<p>In this role, you&#39;ll combine deep infrastructure expertise with an obsession for developer experience. You&#39;ll help maintain and evolve a system that must be correct, performant, and intuitive to use. You&#39;ll work closely with internal teams to understand their needs, burn down errors and edge cases, and build a roadmap that anticipates where the product needs to go. 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However, some roles may require more time in our offices.</li>\n<li>Visa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. 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In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission.</p>\n<p>To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies.</p>\n<p>Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.</p>\n<p>The base salary range for this position is $230,000-$322,000 USD.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_d4a6ec69-e81","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/7377109","x-work-arrangement":"remote","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$230,000-$322,000 USD","x-skills-required":["Machine Learning","Personalized Feed Ranking","Applied Machine Learning Models","Programming Languages","Statistical Analysis"],"x-skills-preferred":["Sequence Modeling","Reinforcement Learning","Transformer Architecture","Bayesian Methodology","Experimentation"],"datePosted":"2026-04-18T15:48:57.691Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, Personalized Feed Ranking, Applied Machine Learning Models, Programming Languages, Statistical Analysis, Sequence Modeling, Reinforcement Learning, Transformer Architecture, Bayesian Methodology, Experimentation","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":230000,"maxValue":322000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_5038042b-80a"},"title":"Modeling and Simulation Engineer, Space","description":"<p>As a Modeling and Simulation Engineer, you will own the understanding and design of the mission solution which includes designing mission trajectories, building models to help craft mission solutions, and developing simulations to solve key mission needs.</p>\n<p>You will carefully listen to stakeholder needs and then design rigorous math and physics analyses leading to clear and compelling value propositions.</p>\n<p>You will work closely with related teams, including Systems Engineering, GNC, Propulsion, Communications, Flight Software, Mission Operations, and others.</p>\n<p>This role is directly tied to ongoing, funded programs within Anduril’s Space Business Line. 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If applying ML / AI in production to improve the relevance of Reddit Notifications excites you, then you’ve found the right place.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Lead the team that architects and designs notifications relevance at Reddit.</li>\n<li>Guide team on holistic, adaptive systems covering budgeting optimization, candidate retrieval, and ranking.</li>\n<li>Work with ML engineers to design, implement, and optimize machine-learning models that drive personalization and user re-engagement.</li>\n<li>Participate in the full development cycle: design, develop, QA, experiment, analyze, and deploy.</li>\n<li>Build and maintain a diverse team that can collaborate across disciplines to find technical solutions to complex challenges.</li>\n<li>Serve as a thought partner to product and upper management to ensure your team’s plans align with company goals.</li>\n<li>Communicate your team’s work and set expectations with cross-functional stakeholders.</li>\n<li>Help your engineers identify career goals and create development plans to achieve them.</li>\n<li>Constantly seek opportunities to push your engineers &amp; managers outside their comfort zone and turn followers into leaders.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>2+ years of experience building and managing engineering teams.</li>\n<li>5+ years of experience as a Machine Learning Engineer or Software Engineer working on large-scale machine learning systems.</li>\n<li>Deep understanding of building and deploying large-scale recommender systems (retrieval + ranking) in production.</li>\n<li>Hands-on experience working with deep learning models, sequential features and real-time systems.</li>\n<li>Experience with distributed training and inference using tools like Ray, PyTorch Distributed, or similar.</li>\n<li>Familiarity with reinforcement learning or multi-objective optimization in recommendation systems.</li>\n<li>Entrepreneurial and self-directed, innovative, results-oriented, biased towards action in fast-paced environments.</li>\n<li>Able to communicate and discuss complex topics with technical and non-technical audiences.</li>\n<li>Able to tackle ambiguous and undefined problems.</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Comprehensive Healthcare Benefits and Income Replacement Programs</li>\n<li>401k with Employer Match</li>\n<li>Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support</li>\n<li>Family Planning Support</li>\n<li>Gender-Affirming Care</li>\n<li>Mental Health &amp; Coaching Benefits</li>\n<li>Flexible Vacation &amp; Paid Volunteer Time Off</li>\n<li>Generous Paid Parental Leave</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_f723a069-05a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/7340793","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$230,000-$322,000 USD","x-skills-required":["Machine Learning Engineer","Software Engineer","Deep Learning Models","Sequential Features","Real-Time Systems","Distributed Training","Inference","Reinforcement Learning","Multi-Objective Optimization"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:46:22.742Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning Engineer, Software Engineer, Deep Learning Models, Sequential Features, Real-Time Systems, Distributed Training, Inference, Reinforcement Learning, Multi-Objective Optimization","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":230000,"maxValue":322000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_a0355e9d-a71"},"title":"Research Lead, Training Insights","description":"<p>As a Research Lead on the Training Insights team, you&#39;ll develop the strategy for, and lead execution on, how we measure and characterise model capabilities across training and deployment. 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work to directly influence how capable AI systems are developed and deployed</li>\n</ul>\n<p>Strong candidates may also have:</p>\n<ul>\n<li>Experience building evaluations for long-horizon or agentic tasks</li>\n<li>Deep familiarity with Reinforcement Learning training dynamics and how model behaviour changes during training</li>\n<li>Published research in machine learning evaluation, benchmarking, or related areas</li>\n<li>Experience with safety evaluation frameworks and red teaming methodologies</li>\n<li>Background in psychometrics, experimental psychology, or other measurement-focused disciplines</li>\n<li>A track record of communicating evaluation results to inform high-stakes decisions about model development or deployment</li>\n<li>Experience managing or mentoring researchers and engineers</li>\n</ul>\n<p>Representative projects:</p>\n<ul>\n<li>Designing and implementing a suite of long-horizon evaluations that test model capabilities on tasks requiring sustained reasoning, planning, and tool use over extended interactions</li>\n<li>Building systems to track capability development across RL training checkpoints, surfacing insights about when and how specific capabilities emerge</li>\n<li>Conducting a cross-org audit of evaluation coverage, identifying blind spots, and prioritising new evaluations to fill critical gaps across Pretraining, RL, Inference, and Product</li>\n<li>Developing the evaluation methodology and narrative for a major model release, working with research leads and communications to clearly characterise model capabilities and limitations</li>\n<li>Researching and prototyping novel evaluation approaches for capabilities that are difficult to measure with existing benchmarks</li>\n<li>Leading a team effort to build reusable evaluation infrastructure that serves multiple teams across the research organisation</li>\n</ul>\n<p>The annual compensation range for this role is $850,000.</p>\n<p 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If you clear the initial phone interview, you will enter the main process, which consists of four technical interviews:</p>\n<ul>\n<li>Coding assessment in a language of your choice.</li>\n<li>Systems hands-on: Demonstrate practical skills in a live problem-solving session.</li>\n<li>Project deep-dive: Present your past exceptional work to a small audience.</li>\n<li>Meet and greet with the wider team.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_01ff2381-5c4","directApply":true,"hiringOrganization":{"@type":"Organization","name":"xAI","sameAs":"https://www.xai.com/","logo":"https://logos.yubhub.co/xai.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/xai/jobs/5073866007","x-work-arrangement":"onsite","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["large-scale reinforcement learning systems","distributed systems","state-of-the-art RL and inference time compute algorithms"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:44:29.806Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"large-scale reinforcement learning systems, distributed systems, state-of-the-art RL and inference time compute algorithms"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c3599ca5-5e7"},"title":"Research Engineer, Environment Scaling","description":"<p>About the role</p>\n<p>The Environment Scaling team is a team of researchers and engineers whose goal is to improve the intelligence of our public models for novel verticals and use cases. 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This is a unique role that combines executing directly on ML research, data operations, and project management to improve our models.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Improve and execute our fine-tuning strategies for adapting Claude to new domains and tasks</li>\n<li>Manage technical relationships with external data vendors, including evaluation of data quality and reward design</li>\n<li>Collaborate with domain experts to design data pipelines and evaluations</li>\n<li>Explore novel ways of creating RL environments for high value tasks</li>\n<li>Develop and improve QA frameworks to catch reward hacking and ensure environment quality</li>\n<li>Partner with other RL research teams and product teams to translate capability goals into training environments and evals</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have experience with fine-tuning large language models for specific domains or real-world use cases and/or domain expertise in an area where we would like to make our models more useful.</li>\n<li>Have experience with reinforcement learning, reward design, or training data curation for LLMs</li>\n<li>Are comfortable managing technical vendor relationships and iterating quickly on feedback</li>\n<li>Find value in reading through datasets to understand them and spot issues</li>\n<li>Have strong project management and interpersonal skills</li>\n<li>Are passionate about making AI more useful and accessible across different industries</li>\n<li>Are excited about a role that includes a combination of ML research, data operations, and project management</li>\n</ul>\n<p>Strong candidates may also:</p>\n<ul>\n<li>Have experience training production ML systems</li>\n<li>Be familiar with distributed systems and cloud infrastructure</li>\n<li>Have domain expertise in an area where we would like to make our models more useful</li>\n<li>Have experience working with external vendors or technical partners</li>\n</ul>\n<p>The annual compensation range for this role is $350,000-$850,000 USD.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c3599ca5-5e7","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4951064008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$350,000-$850,000 USD","x-skills-required":["fine-tuning large language models","reinforcement learning","reward design","training data curation","project management","interpersonal skills"],"x-skills-preferred":["distributed systems","cloud infrastructure","domain expertise","external vendors","technical partners"],"datePosted":"2026-04-18T15:44:26.621Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote-Friendly (Travel Required) | San Francisco, CA"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"fine-tuning large language models, reinforcement learning, reward design, training data curation, project management, interpersonal skills, distributed systems, cloud infrastructure, domain expertise, external vendors, technical partners","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_51eda545-3f5"},"title":"AI Chief Engineering Lead","description":"<p>We are seeking a Generative AI Chief Engineering Lead to drive innovations in autonomous vehicle technology using deep learning and reinforcement learning.</p>\n<p>In this dynamic role, you will design state-of-the-art algorithms and systems that enable safe, efficient, and intelligent autonomous capabilities.</p>\n<p>Today, employing mass quantities of autonomous robots requires heavy human oversight and execution. Anduril is leveraging AI approaches to improve effectiveness of autonomous missions, offload operator burden, and speed up execution via realtime monitoring, recommendations to users, and multi-modal interaction patterns.</p>\n<p>You will apply proven and un-proven approaches to create prototypes for expanding the capability of autonomous systems.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Develop Advanced Agentic Software</li>\n<li>Design and implement novel agent-based software systems to improve sensor perception, prediction, and decision-making for autonomous vehicles</li>\n<li>Apply Agentic Reasoning</li>\n<li>Design and implement integrated agents and AI models to solve for end-user autonomous systems workflows.</li>\n<li>End-to-End System Integration</li>\n<li>Collaborate with cross-functional teams to integrate research prototypes into robust, production-ready systems including simulation environments and real-world platforms.</li>\n<li>Research &amp; Experimentation</li>\n<li>Conduct research into reinforcement learning strategies and deep architectures, iterate on experimental designs, and evaluate performance using rigorous quantitative metrics.</li>\n<li>Data-Driven Innovation</li>\n<li>Utilize real-world and synthetic data to enhance model robustness and generalization, leveraging scalable training pipelines on distributed systems.</li>\n</ul>\n<p><strong>Required Qualifications</strong></p>\n<ul>\n<li>Sophisticated knowledge of LLM&#39;s with an understanding of how they work and how they&#39;re applied</li>\n<li>Solid experience with reinforcement learning methods and their application to autonomous systems.</li>\n<li>Proven experience of shipping products end to end</li>\n<li>Experience with simulation or real-world validation for autonomous vehicles is highly desirable.</li>\n<li>A degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience</li>\n<li>Eligible to obtain and maintain an active U.S. Top Secret security clearance</li>\n</ul>\n<p><strong>Preferred Qualifications</strong></p>\n<ul>\n<li>PhD or Master’s degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience</li>\n<li>Novel application track record and experience including first author publications, participation in peer reviewed conferences, contribution to open source projects, and demonstrated contribution to the ML and AI community.</li>\n<li>Proven experience in deep learning research and development, particularly in generative AI. This includes diffusion models and autoregressive generative models.</li>\n<li>Experience in multi-modal sensor data processing (e.g., cameras, LiDAR, radar).</li>\n<li>Familiarity with ML Ops best practices, including model versioning and reproducible research pipelines.</li>\n<li>Strong programming skills in Python and familiarity with C/C++ is a plus.</li>\n<li>General software engineering experience solving motion planning or related robotics problems.</li>\n</ul>\n<p><strong>Salary and Benefits</strong></p>\n<p>The salary range for this role is $254,000-$336,000 USD. Highly competitive equity grants are included in the majority of full-time offers; and are considered part of Anduril&#39;s total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including:</p>\n<ul>\n<li>Healthcare Benefits - US Roles: Comprehensive medical, dental, and vision plans at little to no cost to you.</li>\n<li>UK &amp; AUS Roles: We cover full cost of medical insurance premiums for you and your dependents.</li>\n<li>IE Roles: We offer an annual contribution toward your private health insurance for you and your dependents.</li>\n<li>Income Protection: Anduril covers life and disability insurance for all employees.</li>\n<li>Generous time off: Highly competitive PTO plans with a holiday hiatus in December.</li>\n<li>Caregiver &amp; Wellness Leave is available to care for family members, bond with a new baby, or address your own medical needs.</li>\n<li>Family Planning &amp; Parenting Support: Coverage for fertility treatments (e.g., IVF, preservation), adoption, and gestational carriers, along with resources to support you and your partner from planning to parenting.</li>\n<li>Mental Health Resources: Access free mental health resources 24/7, including therapy and life coaching.</li>\n<li>Additional work-life services, such as legal and financial support, are also available.</li>\n<li>Professional Development: Annual reimbursement for professional development.</li>\n<li>Commuter Benefits: Company-funded commuter benefits based on your region.</li>\n<li>Relocation Assistance: Available depending on role eligibility.</li>\n<li>Retirement Savings Plan - US Roles: Traditional 401(k), Roth, and after-tax (mega backdoor Roth) options.</li>\n<li>UK &amp; IE Roles: Pension plan with employer match.</li>\n<li>AUS Roles: Superannuation plan.</li>\n</ul>\n<p><strong>Protecting Yourself from Recruitment Scams</strong></p>\n<p>Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We&#39;ve observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information.</p>\n<p>To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind:</p>\n<ul>\n<li>No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates.</li>\n<li>Please always verify communications:</li>\n</ul>\n<p>Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency&#39;s authenticity by reaching out to contact@anduril.com. Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender&#39;s email domain is @anduril.com before providing any personal information or clicking on links.</p>\n<p>What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com.</p>\n<p>Your proactive approach in protecting yourself from recruitment scams is greatly appreciated.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_51eda545-3f5","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anduril Industries","sameAs":"https://www.anduril.com/","logo":"https://logos.yubhub.co/anduril.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/andurilindustries/jobs/5102282007","x-work-arrangement":"remote","x-experience-level":"executive","x-job-type":"full-time","x-salary-range":"$254,000-$336,000 USD","x-skills-required":["Sophisticated knowledge of LLM's","Reinforcement learning methods","Autonomous systems","Simulation or real-world validation for autonomous vehicles","Top Secret security clearance"],"x-skills-preferred":["PhD or Master’s degree in Computer Science, Robotics, Machine Learning, or a related field","Deep learning research and development","Generative AI","Multi-modal sensor data processing","ML Ops best practices"],"datePosted":"2026-04-18T15:44:21.864Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Sophisticated knowledge of LLM's, Reinforcement learning methods, Autonomous systems, Simulation or real-world validation for autonomous vehicles, Top Secret security clearance, PhD or Master’s degree in Computer Science, Robotics, Machine Learning, or a related field, Deep learning research and development, Generative AI, Multi-modal sensor data processing, ML Ops best practices","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":254000,"maxValue":336000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_95c5ac3a-e98"},"title":"Research Engineer / Scientist, Alignment Science","description":"<p>You will contribute to exploratory experimental research on AI safety, with a focus on risks from powerful future systems. Your work will involve building and running elegant and thorough machine learning experiments to help us understand and steer the behavior of powerful AI systems.</p>\n<p>As a Research Engineer on Alignment Science, you&#39;ll collaborate with other teams including Interpretability, Fine-Tuning, and the Frontier Red Team. Your responsibilities will include testing the robustness of our safety techniques, running multi-agent reinforcement learning experiments, building tooling to efficiently evaluate the effectiveness of novel LLM-generated jailbreaks, and contributing ideas, figures, and writing to research papers, blog posts, and talks.</p>\n<p>You may be a good fit if you have significant software, ML, or research engineering experience, have some experience contributing to empirical AI research projects, and have some familiarity with technical AI safety research. 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Your work will focus on advancing the safety and fairness behavior of state-of-the-art AI models, driving the development of foundational technology adopted by numerous product areas, including Gemini App, Cloud API, and Search.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Post-training/instruction tuning state-of-the-art LLMs, focusing on text-to-text, image/video/audio-to-text modalities and agentic capabilities</li>\n<li>Exploring data, reasoning, and algorithmic solutions to ensure Gemini Models are safe, maximally helpful, and work for everyone</li>\n<li>Improve Gemini&#39;s adversarial robustness, with a focus on high-stakes abuse risks</li>\n<li>Design and maintain high-quality evaluation protocols to assess model behavior gaps and headroom related to safety and fairness</li>\n<li>Develop and execute experimental plans to address known gaps, or construct entirely new capabilities</li>\n<li>Drive innovation and enhance understanding of Supervised Fine Tuning and Reinforcement Learning fine-tuning at scale</li>\n</ul>\n<p>To succeed as a Research Scientist in the Gemini Safety team, we look for the following skills and experience:</p>\n<ul>\n<li>PhD in Computer Science, a related field, or equivalent practical experience</li>\n<li>Significant LLM post-training experience</li>\n<li>Experience in Reward modeling and Reinforcement Learning for LLMs Instruction tuning</li>\n<li>Experience with Long-range Reinforcement learning</li>\n<li>Experience in areas such as Safety, Fairness, and Alignment</li>\n<li>Track record of publications at NeurIPS, ICLR, ICML</li>\n<li>Experience taking research from concept to product</li>\n<li>Experience with collaborating or leading an applied research project</li>\n<li>Strong experimental taste: Good judgment regarding baselines, ablations, and what is worth testing</li>\n<li>Experience with JAX</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_d2f5b1e5-545","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7731944","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["PhD in Computer Science","LLM post-training experience","Reward modeling and Reinforcement Learning for LLMs Instruction tuning","Long-range Reinforcement learning","Safety, Fairness, and Alignment","NeurIPS, ICLR, ICML publications","Research from concept to product","Collaborating or leading an applied research project","JAX"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:40:08.109Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Zurich, Switzerland"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PhD in Computer Science, LLM post-training experience, Reward modeling and Reinforcement Learning for LLMs Instruction tuning, Long-range Reinforcement learning, Safety, Fairness, and Alignment, NeurIPS, ICLR, ICML publications, Research from concept to product, Collaborating or leading an applied research project, JAX"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c6c0a57f-a27"},"title":"Research Scientist, Gemini Information Tasks","description":"<p>We are seeking a research scientist to precisely improve Gemini&#39;s information-seeking capabilities. 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This role offers an opportunity to explore fundamental issues in modelling and data interventions for information-seeking scenarios, with significant opportunities in shaping Google&#39;s products in this space.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Conduct research on post-training methods for information-seeking scenarios in Gemini, including reinforcement learning and self-supervised training.</li>\n<li>Develop novel evaluation methods for improving model quality, grounding, and factuality.</li>\n<li>Investigate orchestration of tool calls and improved retrieval methods for information-seeking scenarios.</li>\n</ul>\n<p><strong>Requirements:</strong></p>\n<ul>\n<li>PhD in a relevant area, or an equivalent research/publication record.</li>\n<li>Strong software-engineering skills in addition to a research background.</li>\n</ul>\n<p><strong>Preferred Qualifications:</strong></p>\n<ul>\n<li>Experience in reinforcement learning.</li>\n<li>Experience in post-training methods.</li>\n<li>Experience in Large Language Models for information-seeking scenarios.</li>\n</ul>\n<p>The US base salary range for this full-time position is between $147,000 USD - 211,000 + bonus + equity + benefits.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c6c0a57f-a27","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7669124","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$147,000 USD - 211,000 + bonus + equity + benefits","x-skills-required":["PhD in a relevant area","Strong software-engineering skills","Reinforcement learning","Post-training methods","Large Language Models"],"x-skills-preferred":["Experience in reinforcement learning","Experience in post-training methods","Experience in LLMs for information-seeking scenarios"],"datePosted":"2026-04-18T15:39:59.926Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View, California, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PhD in a relevant area, Strong software-engineering skills, Reinforcement learning, Post-training methods, Large Language Models, Experience in reinforcement learning, Experience in post-training methods, Experience in LLMs for information-seeking scenarios","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":147000,"maxValue":211000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d94b43ab-0e0"},"title":"Research Scientist, Information Quality","description":"<p><strong>Job Title</strong></p>\n<p>Research Scientist, Information Quality</p>\n<p><strong>Job Description</strong></p>\n<p>This role requires a passion for advancing information literacy through AI &amp; machine learning, focusing on assessing media trustworthiness (images, audio, and video) and exploring concepts like authenticity, provenance, and context.</p>\n<p>Key responsibilities include formulating metrics, simulations, rapid prototyping of ML techniques, exploratory data analysis, collaborating with product teams to drive research, and developing tools and frameworks to accelerate research. A public example of research work is Backstory.</p>\n<p><strong>About Us</strong></p>\n<p>Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence.</p>\n<p><strong>The Role</strong></p>\n<p>To succeed in this role, you will need to be passionate about advancing information literacy using machine learning and other computational techniques. You&#39;ll join an interdisciplinary team of domain experts, ML researchers, and engineers to conduct cutting-edge research and advance the next generation of multimodal AI assistants that help co-investigation and deliberation.</p>\n<p>Relevant domains may include, but are not limited to, determining media authenticity, context discovery, and open source intelligence investigations. A public example of recent work is Backstory.</p>\n<p>Key responsibilities:</p>\n<ul>\n<li>Drive the projects by defining key research questions.</li>\n<li>Design, implement, and evaluate experiments to provide clear answers</li>\n<li>Contribute to real world impact, by landing your research in Google products and services.</li>\n<li>Publish research findings in top academic conferences and journals</li>\n<li>Stay up-to-date with the latest advancements in the field</li>\n<li>Collaborate with internal and external scientific domain experts.</li>\n</ul>\n<p><strong>About You</strong></p>\n<p>In order to set you up for success as a Research Scientist at Google DeepMind, we look for the following skills and experience:</p>\n<ul>\n<li>PhD in Computer Science, Statistics, or a related field.</li>\n<li>Strong publication record in top machine learning and/or computer vision conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV).</li>\n<li>Expertise in one or more of the following areas: social impact of AI, reinforcement learning, multimodal agents, computer vision, natural language understanding.</li>\n</ul>\n<p>In addition, the following would be an advantage:</p>\n<ul>\n<li>Passion for research on societal benefits and implications of the internet and AI with focus in information literacy.</li>\n<li>Experience with training, evaluating, and interpreting large language models.</li>\n<li>Experience working with large and noisy datasets.</li>\n<li>Experience collaborating across fields.</li>\n<li>Proven ability to design and execute independent research projects.</li>\n</ul>\n<p>When assessing technical background we will take a holistic view of the mix of scientific, ML and computational experience. We do not expect you to be an expert in all fields simultaneously. At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact.</p>\n<p>We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law.</p>\n<p>If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.</p>\n<p>The US base salary range for this full-time position is between $174,000 USD - $252,000 USD + bonus + equity + benefits.</p>\n<p>Your recruiter can share more about the specific salary range for your targeted location during the hiring process.</p>\n<p>Application deadline: April 28th, 2026</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_d94b43ab-0e0","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7408812","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$174,000 USD - $252,000 USD + bonus + equity + benefits","x-skills-required":["PhD in Computer Science, Statistics, or a related field","Strong publication record in top machine learning and/or computer vision conferences or journals","Expertise in one or more of the following areas: social impact of AI, reinforcement learning, multimodal agents, computer vision, natural language understanding"],"x-skills-preferred":["Passion for research on societal benefits and implications of the internet and AI with focus in information literacy","Experience with training, evaluating, and interpreting large language models","Experience working with large and noisy datasets","Experience collaborating across fields","Proven ability to design and execute independent research projects"],"datePosted":"2026-04-18T15:39:36.602Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View, California, US; San Francisco, California, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PhD in Computer Science, Statistics, or a related field, Strong publication record in top machine learning and/or computer vision conferences or journals, Expertise in one or more of the following areas: social impact of AI, reinforcement learning, multimodal agents, computer vision, natural language understanding, Passion for research on societal benefits and implications of the internet and AI with focus in information literacy, Experience with training, evaluating, and interpreting large language models, Experience working with large and noisy datasets, Experience collaborating across fields, Proven ability to design and execute independent research projects","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":174000,"maxValue":252000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_e121da52-304"},"title":"Research Engineer, Human Understanding","description":"<p>We are seeking a highly motivated Research Engineer with a strong background in multi-modal modelling for humans and a focus on speech &amp; audio/visual to join the effort within Google DeepMind&#39;s Frontier AI unit.</p>\n<p>This role is pivotal in developing foundational multimodal AI capabilities to understand, generate, and protect human likeness. As a key contributor, you will design and implement cutting-edge models and frameworks, pushing the boundaries of AI to enable foundational capabilities for human-centric understanding and generation.</p>\n<p>This is a unique opportunity to contribute to impactful research and advance Google DeepMind&#39;s mission towards Artificial General Intelligence (AGI).</p>\n<p><strong>Key Responsibilities</strong></p>\n<ul>\n<li>Advance multimodal human representations &amp; understanding: Research and implement novel models and other multimodal techniques for a more holistic understanding of humans across visual, audio, and textual data.</li>\n<li>Conduct applied research: Conduct experimental research cycles from hypothesis to deployment.</li>\n<li>Drive technical projects: Take ownership of substantial technical projects within the effort, from ideation and design to implementation and evaluation, often involving cross-functional collaboration.</li>\n<li>Contribute to Infrastructure: Inform and contribute to the development of scalable and efficient research infrastructure for multimodal human understanding models and datasets.</li>\n<li>Design and execute strategies for tuning and adapting VLMs and other foundation models for specific tasks</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>PhD degree in Computer Science, Machine Learning, or a related technical field with 3+ years of relevant experience.</li>\n<li>Experience in developing machine learning models, such as audio &amp; speech-visual models.</li>\n<li>Experience in working with and tuning large-scale vision language models.</li>\n<li>Strong programming skills in Python and experience with at least one major deep learning framework (e.g., JAX)</li>\n<li>Experience conducting independent research and development, including experimental design, implementation, and analysis.</li>\n</ul>\n<p><strong>Salary</strong></p>\n<p>The US base salary range for this full-time position is between $174,000 USD - $252,000 USD + bonus + equity + benefits.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_e121da52-304","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7669433","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$174,000 USD - $252,000 USD","x-skills-required":["Python","JAX","Machine Learning","Deep Learning","Vision Language Models","Audio & Speech-Visual Models"],"x-skills-preferred":["Generative AI","Reinforcement Learning","Alignment Methods","Multimodal Learning","Privacy-Preserving Machine Learning"],"datePosted":"2026-04-18T15:38:13.994Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Los Angeles, California, US; Mountain View, California, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, JAX, Machine Learning, Deep Learning, Vision Language Models, Audio & Speech-Visual Models, Generative AI, Reinforcement Learning, Alignment Methods, Multimodal Learning, Privacy-Preserving Machine Learning","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":174000,"maxValue":252000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_306a6a6f-98c"},"title":"AI Tutor - Crypto","description":"<p>As a Crypto Expert, you will be vital in enhancing xAI&#39;s frontier AI models by supplying high-quality annotations, evaluations, and expert reasoning using proprietary labeling tools. You will work closely with technical teams to support the creation and refinement of new AI tasks, focusing especially on cryptocurrency and digital asset markets.</p>\n<p>Your deep domain knowledge will guide the selection and rigorous solving of complex problems in quantitative crypto strategies , including on-chain analysis, DeFi protocols, perpetual futures &amp; derivatives trading, cross-exchange arbitrage, market microstructure in fragmented venues, MEV-aware execution, machine learning for crypto alpha signals, and portfolio/risk management in high-volatility 24/7 markets.</p>\n<p>This role demands sharp quantitative thinking, quick adaptation to evolving instructions, and the ability to deliver precise, technically robust critiques and solutions in a dynamic environment.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Utilize proprietary software to deliver accurate labels, rankings, critiques, and in-depth solutions on assigned projects</li>\n<li>Consistently produce high-quality, curated data adhering to rigorous technical and domain standards</li>\n<li>Partner with engineers and researchers to iterate on new training tasks, evaluation frameworks, and crypto-specific benchmarks</li>\n<li>Offer actionable feedback to enhance the efficiency, accuracy, and usability of annotation and data-collection interfaces</li>\n<li>Identify and solve challenging problems from crypto &amp; digital asset domains where you have strong expertise , examples include:</li>\n</ul>\n<ul>\n<li>On-chain metrics analysis and wallet/flow clustering for alpha generation</li>\n<li>DeFi yield farming, liquidity provision, and impermanent loss modeling</li>\n<li>Cross-exchange / CEX-DEX arbitrage and triangular opportunities</li>\n<li>Perpetual futures funding rate strategies and basis trading</li>\n<li>Market microstructure in crypto order books (fragmented liquidity, MEV, sandwich attacks)</li>\n<li>Machine learning models for price prediction, sentiment from social/on-chain, volatility forecasting</li>\n<li>Tokenomics evaluation, airdrop/IDO quantitative assessment, and risk premia in altcoins</li>\n<li>Portfolio optimization and risk management in 24/7 high-volatility environments</li>\n</ul>\n<ul>\n<li>Provide rigorous critiques of model outputs, alternative quantitative approaches, mathematical derivations, code snippets, and step-by-step crypto reasoning</li>\n<li>Efficiently interpret, analyze, and complete tasks based on detailed (and evolving) guidelines</li>\n</ul>\n<p>Basic Qualifications:</p>\n<ul>\n<li>Master’s or PhD in a quantitative discipline: Quantitative Finance, Financial Engineering, Computer Science (with crypto/blockchain focus), Statistics, Applied Mathematics, Economics (quantitative), Physics, Operations Research, Data Science, or closely related field or equivalent professional experience as a quantitative crypto trader, systematic strategist, or on-chain analyst</li>\n<li>Superior written and verbal English communication (technical papers, explanatory breakdowns, professional correspondence)</li>\n<li>Extensive hands-on familiarity with crypto data sources and tools (CoinGecko, CoinMarketCap, Dune Analytics, Glassnode, Nansen, Chainalysis, Messari, DefiLlama, The Graph, blockchain explorers, CEX APIs, on-chain datasets, etc.)</li>\n<li>Outstanding analytical skills, attention to detail, and sound judgment under partial information</li>\n</ul>\n<p>Preferred Skills and Experience:</p>\n<ul>\n<li>Professional experience in quantitative crypto trading, systematic strategies, or on-chain research at a crypto hedge fund, prop desk, market-making firm, DeFi protocol, or digital asset investment firm</li>\n<li>Publications or public analyses in crypto quant topics (e.g., journals, conferences, reputable blogs, GitHub repos with notable traction)</li>\n<li>Teaching, mentoring, or content-creation experience in crypto/quant finance (university, bootcamps, Twitter threads, newsletters)</li>\n<li>Proficiency in Python for crypto analysis (pandas, NumPy, ccxt, web3.py, etherscan APIs, polars, scikit-learn, PyTorch/TensorFlow for ML models, etc.) and/or Rust/Solidity familiarity</li>\n<li>Experience with backtesting crypto strategies, handling tick-level or on-chain data, managing API rate limits, and dealing with 24/7 market quirks</li>\n<li>Knowledge of MEV, flash loans, oracle manipulation risks, liquidation cascades, or other crypto-native phenomena</li>\n<li>CFA, FRM, CQF, or blockchain-specific certifications (e.g., Certified Blockchain Expert)</li>\n<li>Prior involvement with LLMs, reinforcement learning, or AI evaluation in financial/crypto contexts (strong plus)</li>\n</ul>\n<p>Location and Other Expectations:</p>\n<ul>\n<li>Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit.</li>\n<li>For contractor positions, hours will vary widely based on project scope and contractor availability, with no fixed commitments required. On average most projects may involve at least 10 hours per week to achieve deliverables effectively though this is not a fixed commitment and depends on the scope of work.</li>\n<li>Tutor roles may be performed remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role specific needs.</li>\n<li>For US based candidates, please note we are unable to hire in the states of Wyoming and Illinois at this time.</li>\n<li>We are unable to provide visa sponsorship.</li>\n<li>For those who will be working from a personal device, your computer must be a Chromebook, Mac with MacOS 11.0 or later, or Windows 10 or later.</li>\n</ul>\n<p>Compensation and Benefits:</p>\n<p>US based candidates: $45/hour - $100/hour depending on factors including relevant experience, skills, education, geographic location, and qualifications. 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We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. 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You have a collaborative mindset and are excited to work as part of a team to tackle ambitious research challenges.</p>\n<p>Requirements include:</p>\n<ul>\n<li>PhD in Computer Science, Artificial Intelligence, or a related field.</li>\n<li>Strong publication record in top-tier machine learning conferences or journals.</li>\n<li>Solid understanding of deep learning, natural language processing, computer vision, and/or speech processing.</li>\n<li>Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.</li>\n</ul>\n<p>Preferred qualifications include experience with multimodal learning, large language models, and/or assistive AI agents.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_d68ddd2d-5bf","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7640947","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Reinforcement Learning","Machine Learning Optimization","Large Language Models","Autonomous Agents","Deep Learning","Natural Language Processing","Computer Vision","Speech Processing","JAX","TensorFlow","PyTorch"],"x-skills-preferred":["Multimodal Learning","Assistive AI Agents","Prompt Engineering","Few-Shot Learning","Post-Training Techniques","Evaluations"],"datePosted":"2026-03-16T14:38:30.668Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Bangalore, India"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Reinforcement Learning, Machine Learning Optimization, Large Language Models, Autonomous Agents, Deep Learning, Natural Language Processing, Computer Vision, Speech Processing, JAX, TensorFlow, PyTorch, Multimodal Learning, Assistive AI Agents, Prompt Engineering, Few-Shot Learning, Post-Training Techniques, Evaluations"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_b1e8058a-2ea"},"title":"Data Science Manager","description":"<p>As the Manager of Data Science, Games Tech, you will be a transformational leader, responsible for guiding and inspiring a dedicated team of data scientists and machine learning engineers. In this role, you’ll drive the creation of groundbreaking data solutions that enhance gameplay, improve user engagement, and optimise business outcomes.</p>\n<p><strong>Key Leadership Responsibilities</strong></p>\n<ul>\n<li>Mentorship &amp; Development: Provide ongoing mentorship, coaching, and professional development opportunities to foster growth and enhance team performance.</li>\n<li>Partnerships: Act as a trusted partner across the organisation, advocating for data-driven decision-making and empowering business units to adopt data products.</li>\n<li>Ownership &amp; Accountability: Assume full accountability for the data science project execution to final integration and outcome assessment, ensuring that your team delivers impactful results on time and within scope.</li>\n<li>Insight Communication: Translate sophisticated analytical insights into actionable recommendations, communicating them to the senior leadership team to advise critical business decisions, with the ability to encourage and influence stakeholders.</li>\n</ul>\n<p><strong>Key Technical Responsibilities</strong></p>\n<ul>\n<li>Data Science Best Practices: Drive best practices in A/B-testing, predictive modelling, user clustering and reinforcement learning, to continually set the standard on data science benefit.</li>\n<li>Engineering Best Practices: Be responsible for the implementation of the best software engineering practices for internal tools and ML/RL model development, define software architecture standards, implement code review practices, auto-tests, improve observability, reproducibility and monitoring of ML/RL solutions.</li>\n<li>Infrastructure Ownership: Own the development of analytical frameworks, including A/B testing (using Bayesian Inference and contextual multi-armed bandits techniques) and other data science tooling. Ensuring scalability, accuracy, and reliability across projects.</li>\n<li>Product &amp; Engineering Collaboration: Coordinate integration of analytical solutions into games and platforms, partnering closely with product and engineering to ensure end-to-end solution success.</li>\n</ul>\n<p><strong>What we need from you</strong></p>\n<ul>\n<li>Expertise in clustering, predictive modelling, reinforcement learning, and Bayesian statistics.</li>\n<li>PHD or MSc or equivalent experience in Data Science, Computer Science, Statistics, Physics or related field</li>\n<li>5+ years of Data Science experience with a minimum of 2 years in a leadership role</li>\n<li>Practical experience in software engineering, proven track record in design and development of the customer-facing products</li>\n<li>Experience in ML Ops and deploying machine learning models at scale.</li>\n<li>Proficiency in Python, and familiarity with data processing technologies (e.g., Kafka, Spark) and/or cloud platforms (e.g., GCP, AWS, or Azure).</li>\n<li>Ability to work on a hybrid work basis requiring at least 3 days a week in our central London office</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_b1e8058a-2ea","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Product Madness","sameAs":"https://www.productmadness.com/","logo":"https://logos.yubhub.co/productmadness.com.png"},"x-apply-url":"https://aristocrat.wd3.myworkdayjobs.com/en-US/AristocratExternalCareersSite/job/London-United-Kingdom/Data-Science-Manager_R0020843-1","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["clustering","predictive modelling","reinforcement learning","Bayesian statistics","Python","Kafka","Spark","GCP","AWS","Azure"],"x-skills-preferred":[],"datePosted":"2026-03-10T12:14:20.145Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"clustering, predictive modelling, reinforcement learning, Bayesian statistics, Python, Kafka, Spark, GCP, AWS, Azure"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d4dabbbc-b6f"},"title":"Principal Data Scientist","description":"<p>Are you ready to join a world-class team and make a significant impact on the gaming industry? At Aristocrat, we aim to bring happiness to life through the power of play. We seek a Principal Data Scientist to help us reach our ambitious goals. You will have a vital role in enhancing gameplay, boosting player engagement, and improving business outcomes with your advanced data expertise. This opportunity allows you to work on innovative projects, collaborate with diverse teams, and guide critical initiatives that will develop the future of our leading games.</p>\n<p>Key Responsibilities:</p>\n<ul>\n<li>Lead high-impact data science initiatives end-to-end, including problem framing, methodology selection, experiment development, implementation partnership, and impact measurement.</li>\n<li>Build and deliver machine learning and reinforcement learning solutions to improve player engagement, retention, monetization, and operational outcomes.</li>\n<li>Lead the modeling framework for complex systems, guaranteeing comprehensive evaluation and monitoring of causal inference, uplift modeling, sequential decisioning, bandits/reinforcement learning, and forecasting.</li>\n<li>Partner with game teams to define success metrics, guardrails, and decision frameworks, translating analytical results into actionable product and operational actions.</li>\n<li>Define and uphold engineering standards and guidelines for model development, including validation, uncertainty, reproducibility, and bias/quality checks.</li>\n<li>Drive scalable experimentation with A/B and Multi-armed bandit testing frameworks, power analysis, variance reduction, and online-offline alignment.</li>\n<li>Work together with Data Engineering, MLOps, and Game Tech teams to guarantee dependable data foundations, feature accessibility, and model deployment pathways.</li>\n<li>Build internal data products to improve the speed and quality of decision-making, such as AB-test calculators, decision tools, and automated insights.</li>\n<li>Provide technical leadership through building and code reviews, mentoring, and coaching, improving the standard of data science craft across the organization.</li>\n<li>Serve as a reliable collaborator throughout the organization, promoting data-informed decision-making and enabling business units to embrace data products.</li>\n<li>Translate complex analytical insights into actionable recommendations, presenting them to senior leadership to inform critical business decisions and encourage collaborators.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_d4dabbbc-b6f","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Aristocrat","sameAs":"https://www.aristocrat.com/","logo":"https://logos.yubhub.co/aristocrat.com.png"},"x-apply-url":"https://aristocrat.wd3.myworkdayjobs.com/en-US/AristocratExternalCareersSite/job/London-United-Kingdom/Principal-Data-Scientist_R0020855","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["PhD or MSc in Data Science, Computer Science, Statistics, Physics, Mathematics, or a related quantitative field","5+ years of professional data science experience","Demonstrated proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics","Hands-on experience in software engineering, MLOps, and deploying machine learning models at scale","Proficiency in SQL, Python, and familiarity with big data technologies (e.g., Kafka, Spark) and/or cloud platforms (e.g., GCP, AWS, or Azure)","Industry knowledge: Experience in gaming or digital entertainment is a strong plus"],"x-skills-preferred":[],"datePosted":"2026-03-10T12:11:57.933Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PhD or MSc in Data Science, Computer Science, Statistics, Physics, Mathematics, or a related quantitative field, 5+ years of professional data science experience, Demonstrated proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics, Hands-on experience in software engineering, MLOps, and deploying machine learning models at scale, Proficiency in SQL, Python, and familiarity with big data technologies (e.g., Kafka, Spark) and/or cloud platforms (e.g., GCP, AWS, or Azure), Industry knowledge: Experience in gaming or digital entertainment is a strong plus"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_4bd6468f-bc0"},"title":"Senior Applied Scientist","description":"<p>We&#39;re building the next-generation Grounding Service that powers the latest AI applications—chat assistants, copilots, and autonomous agents—with factual, cited, and trustworthy responses. Our platform stitches together retrieval, reasoning, and real-time data so that large language models stay anchored to enterprise knowledge, the public web, and proprietary tools.</p>\n<p>We&#39;re looking for a Senior Applied Scientist to lead end-to-end science for grounding: inventing retrieval and attribution methods, defining factuality/faithfulness metrics, and shipping production models and APIs that scale to billions of queries. You&#39;ll partner closely with engineering, product, research, and customers to deliver fast, reliable, and explainable answers with source citations across a diverse set of domains and modalities.</p>\n<p>As a team, we value curiosity, pragmatic rigor, and inclusive collaboration. We believe great systems emerge when scientists and engineers co-design metrics, models, and infrastructure—and when we obsess over customer impact, privacy, and safety.</p>\n<p>Responsibilities\n Owns the science roadmap for grounding—including retrieval, re-ranking, attribution, and reasoning—driving initiatives from problem framing to production impact.\n Designs and evolves state-of-the-art retrieval and RAG orchestration across documents, tables, code, and images.\n Builds citation and provenance systems (e.g., passage highlighting, quote-level alignment, confidence scoring) to reduce hallucinations and increase user trust.\n Leads experimentation and evaluation using A/B testing, interleaving, NDCG, MRR, precision/recall, and calibration curves to guide measurable trade-offs.\n Advances tool-augmented grounding through schema-aware retrieval, function calling, knowledge graph joins, and real-time connectors to databases, cloud object stores, search indexes, and the web.\n Partners with platform engineering to productionize models with scalable inference, embedding services, feature stores, caching, and privacy-compliant multi-tenant systems.\n Nurtures collaborative relationships with product and business leaders across Microsoft, influencing strategic decisions and driving business impact through technology.\n Authors white papers, contributes to internal tools and services, and may publish research to generate intellectual property.\n Bridges the gap between researchers (e.g., Microsoft Research) and development teams, applying long-term research to solve immediate product needs.\n Leads high-stakes negotiations to ensure cutting-edge technologies are applied practically and effectively.\n Identifies and solves significant business problems using novel, scalable, and data-driven solutions.\n Shapes the direction of Microsoft and the broader industry through pioneering product and tooling work.\n Mentors applied scientists and data scientists, establishing best practices in experimentation, error analysis, and incident review.\n Collaborates cross-functionally with PMs, research, infrastructure, and security teams to align on milestones, SLAs, and safety protocols.\n Communicates clearly through design documentation, progress updates, and presentations to executives and customers.\n Contributes to ethics and privacy policies, identifies bias in product development, and proposes mitigation strategies.</p>\n<p>Qualifications\n Required Qualifications:\n  Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research)\n  OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)\n  OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)\n  OR equivalent experience.\n  Minimum of 4 years of hands-on experience designing and building search, retrieval, or ranking systems.\n  Proven track record of shipping LLM-powered or Retrieval-Augmented Generation (RAG) systems into production environments.\n  Solid coding skills and solid foundation in machine learning, with the ability to implement and optimize models effectively.\n  Demonstrated ability to lead through ambiguity, make principled trade-offs, and deliver measurable impact in cross-functional, fast-paced settings.\n Preferred Qualifications:\n  Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)\n  OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)\n  OR equivalent experience.\n  Demonstrated expertise in information retrieval, with publications in top-tier conferences or journals such as NeurIPS, ICML, ICLR, SIGIR, or ACL.\n  Hands-on experience in large language model (LLM) development, including pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL).\n  Proven track record in optimizing LLM inference, or active contributions to open-source frameworks like vLLM, SGLang, or related projects.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_4bd6468f-bc0","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/senior-applied-scientist-38/","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Statistics","Econometrics","Computer Science","Electrical or Computer Engineering","Search","Retrieval","Ranking","Machine Learning","Information Retrieval","Large Language Models","Pretraining","Supervised Fine-Tuning","Reinforcement Learning"],"x-skills-preferred":["Information Retrieval","Large Language Models","Pretraining","Supervised Fine-Tuning","Reinforcement Learning"],"datePosted":"2026-03-08T22:18:58.169Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Suzhou"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, Search, Retrieval, Ranking, Machine Learning, Information Retrieval, Large Language Models, Pretraining, Supervised Fine-Tuning, Reinforcement Learning, Information Retrieval, Large Language Models, Pretraining, Supervised Fine-Tuning, Reinforcement Learning"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d0214534-b6a"},"title":"Senior Applied Scientist","description":"<p>We&#39;re building the next-generation Grounding Service that powers the latest AI applications—chat assistants, copilots, and autonomous agents—with factual, cited, and trustworthy responses. Our platform stitches together retrieval, reasoning, and real-time data so that large language models stay anchored to enterprise knowledge, the public web, and proprietary tools. We&#39;re looking for a Senior Applied Scientist to lead end-to-end science for grounding: inventing retrieval and attribution methods, defining factuality/faithfulness metrics, and shipping production models and APIs that scale to billions of queries. You&#39;ll partner closely with engineering, product, research, and customers to deliver fast, reliable, and explainable answers with source citations across a diverse set of domains and modalities. As a team, we value curiosity, pragmatic rigor, and inclusive collaboration. We believe great systems emerge when scientists and engineers co-design metrics, models, and infrastructure—and when we obsess over customer impact, privacy, and safety. Microsoft&#39;s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction. Responsibilities</p>\n<p>Owns the science roadmap for grounding—including retrieval, re-ranking, attribution, and reasoning—driving initiatives from problem framing to production impact. Designs and evolves state-of-the-art retrieval and RAG orchestration across documents, tables, code, and images. Builds citation and provenance systems (e.g., passage highlighting, quote-level alignment, confidence scoring) to reduce hallucinations and increase user trust. Leads experimentation and evaluation using A/B testing, interleaving, NDCG, MRR, precision/recall, and calibration curves to guide measurable trade-offs. Advances tool-augmented grounding through schema-aware retrieval, function calling, knowledge graph joins, and real-time connectors to databases, cloud object stores, search indexes, and the web. Partners with platform engineering to productionize models with scalable inference, embedding services, feature stores, caching, and privacy-compliant multi-tenant systems. Nurtures collaborative relationships with product and business leaders across Microsoft, influencing strategic decisions and driving business impact through technology. Authors white papers, contributes to internal tools and services, and may publish research to generate intellectual property. Bridges the gap between researchers (e.g., Microsoft Research) and development teams, applying long-term research to solve immediate product needs. Leads high-stakes negotiations to ensure cutting-edge technologies are applied practically and effectively. Identifies and solves significant business problems using novel, scalable, and data-driven solutions. Shapes the direction of Microsoft and the broader industry through pioneering product and tooling work. Mentors applied scientists and data scientists, establishing best practices in experimentation, error analysis, and incident review. Collaborates cross-functionally with PMs, research, infrastructure, and security teams to align on milestones, SLAs, and safety protocols. Communicates clearly through design documentation, progress updates, and presentations to executives and customers. Contributes to ethics and privacy policies, identifies bias in product development, and proposes mitigation strategies.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_d0214534-b6a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/senior-applied-scientist-37/","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Statistics","Econometrics","Computer Science","Electrical or Computer Engineering","Machine Learning","Information Retrieval","Large Language Model Development","Pretraining","Supervised Fine-Tuning","Reinforcement Learning","Optimizing LLM Inference"],"x-skills-preferred":["Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field","6+ years related experience (e.g., statistics, predictive analytics, research)","Demonstrated expertise in information retrieval, with publications in top-tier conferences or journals such as NeurIPS, ICML, ICLR, SIGIR, or ACL","Hands-on experience in large language model (LLM) development, including pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL)","Proven track record in optimizing LLM inference, or active contributions to open-source frameworks like vLLM, SGLang, or related projects"],"datePosted":"2026-03-08T22:16:41.766Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Beijing"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, Machine Learning, Information Retrieval, Large Language Model Development, Pretraining, Supervised Fine-Tuning, Reinforcement Learning, Optimizing LLM Inference, Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field, 6+ years related experience (e.g., statistics, predictive analytics, research), Demonstrated expertise in information retrieval, with publications in top-tier conferences or journals such as NeurIPS, ICML, ICLR, SIGIR, or ACL, Hands-on experience in large language model (LLM) development, including pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL), Proven track record in optimizing LLM inference, or active contributions to open-source frameworks like vLLM, SGLang, or related projects"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_4396bfcf-940"},"title":"Software Engineer, Sandboxing","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;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.</p>\n<p><strong>About the Role</strong></p>\n<p>Anthropic&#39;s sandboxing infrastructure enables Claude to safely execute code and interact with external systems. As we expand Claude&#39;s capabilities, the reliability, security, and developer experience of this infrastructure becomes increasingly critical. We&#39;re looking for an engineer to join the sandboxing team and help shape both the client-side library/API and the underlying infrastructure.</p>\n<p>In this role, you&#39;ll combine deep infrastructure expertise with an obsession for developer experience. You&#39;ll help maintain and evolve a system that must be correct, performant, and intuitive to use. You&#39;ll work closely with internal teams to understand their needs, burn down errors and edge cases, and build a roadmap that anticipates where the product needs to go. This is a role for someone who finds satisfaction in both the craft of building reliable systems and the empathy required to serve developers and researchers well.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Contribute to the client library, API surface, and underlying infrastructure for Anthropic&#39;s sandboxing system, ensuring it is reliable, well-documented, and intuitive to use</li>\n</ul>\n<ul>\n<li>Drive down error rates and improve correctness through systematic debugging, monitoring, and proactive fixes</li>\n</ul>\n<ul>\n<li>Help develop and maintain a product roadmap for sandboxing capabilities, balancing immediate needs with long-term architectural improvements</li>\n</ul>\n<ul>\n<li>Partner closely with internal teams using the sandboxing system to understand their requirements, debug issues, and build tooling that serves their use cases</li>\n</ul>\n<ul>\n<li>Respond to incidents and production issues with urgency, conducting thorough root cause analysis and implementing preventive measures</li>\n</ul>\n<ul>\n<li>Build comprehensive testing, observability, and documentation to ensure the system meets a high quality bar</li>\n</ul>\n<ul>\n<li>Collaborate across the sandboxing team, flexing between client-side and infrastructure work as needed</li>\n</ul>\n<p><strong>You May Be a Good Fit If You</strong></p>\n<ul>\n<li>Have 5+ years of software engineering experience, with meaningful time spent maintaining libraries, SDKs, or developer-facing APIs</li>\n</ul>\n<ul>\n<li>Obsess over developer experience—you&#39;ve thought deeply about API design, error propagation, documentation, and the small details that make a library feel well-crafted</li>\n</ul>\n<ul>\n<li>Have experience operating complex distributed systems</li>\n</ul>\n<ul>\n<li>Bring a track record of systematically improving reliability—you&#39;ve burned down error budgets, built monitoring, and driven issues to resolution</li>\n</ul>\n<ul>\n<li>Can develop and articulate a long-term vision for a product, translating user feedback and technical constraints into a coherent roadmap</li>\n</ul>\n<ul>\n<li>Are comfortable with ambiguity and can context-switch between reactive incident work and proactive product development</li>\n</ul>\n<ul>\n<li>Communicate clearly with both technical and non-technical stakeholders</li>\n</ul>\n<p><strong>Strong Candidates May Also Have</strong></p>\n<ul>\n<li>Experience as a founder or early engineer at an infrastructure-focused startup, where you owned a product end-to-end</li>\n</ul>\n<ul>\n<li>Background in security, sandboxing, or isolation technologies (containers, VMs, seccomp, namespaces, etc.)</li>\n</ul>\n<ul>\n<li>Open-source contributions in the Python ecosystem</li>\n</ul>\n<ul>\n<li>Experience building developer tools, CLIs, or platforms used by other engineers</li>\n</ul>\n<ul>\n<li>History of working on incident response and on-call rotations for production systems</li>\n</ul>\n<ul>\n<li>Exposure to reinforcement learning or model training infrastructure</li>\n</ul>\n<p><strong>Representative Projects</strong></p>\n<p>These are examples of past work that would indicate a good fit—not a description of the role itself:</p>\n<ul>\n<li>Maintaining an open source SDK through multiple major version upgrades while minimizing breaking changes for users</li>\n</ul>\n<ul>\n<li>Leading an initiative to reduce P0 incidents by XX% through improved error handling, retries, and observability</li>\n</ul>\n<ul>\n<li>Building a developer platform at a startup from zero to product-market fit, iterating based on user feedback</li>\n</ul>\n<ul>\n<li>Embedding with an internal team for a quarter to deeply understand their workflows and shipping targeted improvements to a piece of infrastructure they rely on</li>\n</ul>\n<ul>\n<li>Developing a multi-quarter roadmap for a developer tools product, balancing user requests with technical debt reduction</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;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.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong> 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&#39;re interested in this work. We think AI systems like the ones we&#39;re building can have a huge impact on society, and we want to make sure that the people building them are representative of the people they&#39;ll be serving.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_4396bfcf-940","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5083039008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$300,000 - $405,000USD","x-skills-required":["software engineering","API design","error propagation","documentation","complex distributed systems","reliability","observability","testing","security","sandboxing","isolation technologies","containers","VMs","seccomp","namespaces","Python ecosystem","developer tools","CLIs","platforms","incident response","on-call rotations","reinforcement learning","model training infrastructure"],"x-skills-preferred":["founder","early engineer","infrastructure-focused startup","open-source contributions","developer platform","product-market fit","user feedback","incident response","on-call rotations","reinforcement learning","model training infrastructure"],"datePosted":"2026-03-08T14:03:30.986Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, API design, error propagation, documentation, complex distributed systems, reliability, observability, testing, security, sandboxing, isolation technologies, containers, VMs, seccomp, namespaces, Python ecosystem, developer tools, CLIs, platforms, incident response, on-call rotations, reinforcement learning, model training infrastructure, founder, early engineer, infrastructure-focused startup, open-source contributions, developer platform, product-market fit, user feedback, incident response, on-call rotations, reinforcement learning, model training infrastructure","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":300000,"maxValue":405000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_4e0b9271-cdd"},"title":"Research Engineer / Scientist, Alignment Science","description":"<p><strong>About the role:</strong></p>\n<p>You want to build and run elegant and thorough machine learning experiments to help us understand and steer the behavior of powerful AI systems. You care about making AI helpful, honest, and harmless, and are interested in the ways that this could be challenging in the context of human-level capabilities. You could describe yourself as both a scientist and an engineer. As a Research Engineer on Alignment Science, you&#39;ll contribute to exploratory experimental research on AI safety, with a focus on risks from powerful future systems (like those we would designate as ASL-3 or ASL-4 under our Responsible Scaling Policy), often in collaboration with other teams including Interpretability, Fine-Tuning, and the Frontier Red Team.</p>\n<p>Our blog provides an overview of topics that the Alignment Science team is either currently exploring or has previously explored. Our current topics of focus include...</p>\n<ul>\n<li><strong>Scalable Oversight:</strong> Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains.</li>\n</ul>\n<ul>\n<li><strong>AI Control:</strong> Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios.</li>\n</ul>\n<ul>\n<li><strong>Alignment Stress-testing</strong> <strong>:</strong> Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise.</li>\n</ul>\n<ul>\n<li><strong>Automated Alignment Research:</strong> Building and aligning a system that can speed up &amp; improve alignment research.</li>\n</ul>\n<ul>\n<li><strong>Alignment Assessments</strong>: Understanding and documenting the highest-stakes and most concerning emerging properties of models through pre-deployment alignment and welfare assessments (see our Claude 4 System Card), misalignment-risk safety cases, and coordination with third-party evaluators.</li>\n</ul>\n<ul>\n<li><strong>Safeguards Research</strong>: Developing robust defenses against adversarial attacks, comprehensive evaluation frameworks for model safety, and automated systems to detect and mitigate potential risks before deployment.</li>\n</ul>\n<ul>\n<li><strong>Model Welfare:</strong> Investigating and addressing potential model welfare, moral status, and related questions. See our program announcement and welfare assessment in the Claude 4 system card for more.</li>\n</ul>\n<p>_Note: For this role, we conduct all interviews in Python and prefer candidates to be based in the Bay Area._</p>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Testing the robustness of our safety techniques by training language models to subvert our safety techniques, and seeing how effective they are at subvertinng our interventions.</li>\n</ul>\n<ul>\n<li>Run multi-agent reinforcement learning experiments to test out techniques like AI Debate.</li>\n</ul>\n<ul>\n<li>Build tooling to efficiently evaluate the effectiveness of novel LLM-generated jailbreaks.</li>\n</ul>\n<ul>\n<li>Write scripts and prompts to efficiently produce evaluation questions to test models’ reasoning abilities in safety-relevant contexts.</li>\n</ul>\n<ul>\n<li>Contribute ideas, figures, and writing to research papers, blog posts, and talks.</li>\n</ul>\n<ul>\n<li>Run experiments that feed into key AI safety efforts at Anthropic, like the design and implementation of our Responsible Scaling Policy.</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have significant software, ML, or research engineering experience</li>\n</ul>\n<ul>\n<li>Have some experience contributing to empirical AI research projects</li>\n</ul>\n<ul>\n<li>Have some familiarity with technical AI safety research</li>\n</ul>\n<ul>\n<li>Prefer fast-moving collaborative projects to extensive solo efforts</li>\n</ul>\n<ul>\n<li>Pick up slack, even if it goes outside your job description</li>\n</ul>\n<ul>\n<li>Care about the impacts of AI</li>\n</ul>\n<p><strong>Strong candidates may also:</strong></p>\n<ul>\n<li>Have experience authoring research papers in machine learning, NLP, or AI safety</li>\n</ul>\n<ul>\n<li>Have experience with LLMs</li>\n</ul>\n<ul>\n<li>Have experience with reinforcement learning</li>\n</ul>\n<ul>\n<li>Have experience with Kubernetes clusters and complex shared codebases</li>\n</ul>\n<p><strong>Candidates need not have:</strong></p>\n<ul>\n<li>100% of the skills needed to perform the job</li>\n</ul>\n<ul>\n<li>Formal certifications or education credentials</li>\n</ul>\n<p>The annual compensation range for this role is listed below.</p>\n<p>For sales roles, the range provided is the role’s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.</p>\n<p>Annual Salary:</p>\n<p>$350,000 \\- $500,000USD</p>\n<p><strong><strong>Logistics</strong></strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</p>\n<p><strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;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.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong> 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&#39;re interested in this work. We think AI systems like the ones we&#39;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.</p>\n<p><strong>Your safety matters to us.</strong> To protect yourself from potential scams, remember that Anthropic recruits through our website and other job boards, and we will never ask you to pay for any part of the recruitment process.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_4e0b9271-cdd","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4631822008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $500,000USD","x-skills-required":["Python","Machine Learning","Research Engineering","AI Safety","Scalable Oversight","AI Control","Alignment Stress-testing","Automated Alignment Research","Alignment Assessments","Safeguards Research","Model Welfare"],"x-skills-preferred":["Experience authoring research papers in machine learning, NLP, or AI safety","Experience with LLMs","Experience with reinforcement learning","Experience with Kubernetes clusters and complex shared codebases"],"datePosted":"2026-03-08T13:51:34.613Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Machine Learning, Research Engineering, AI Safety, Scalable Oversight, AI Control, Alignment Stress-testing, Automated Alignment Research, Alignment Assessments, Safeguards Research, Model Welfare, Experience authoring research papers in machine learning, NLP, or AI safety, Experience with LLMs, Experience with reinforcement learning, Experience with Kubernetes clusters and complex shared codebases","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":500000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_9c72720b-6af"},"title":"Research Engineer, Science of Scaling","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;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.</p>\n<p><strong>About the role</strong></p>\n<p>Anthropic is seeking a Research Engineer/Scientist to join the Science of Scaling team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. You&#39;ll contribute across the entire stack, from low-level optimizations to high-level algorithm and experimental design, balancing research goals with practical engineering constraints.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Conduct research into the science of converting compute into intelligence</li>\n<li>Independently lead small research projects while collaborating with team members on larger initiatives</li>\n<li>Design, run, and analyse scientific experiments to advance our understanding of large language models</li>\n<li>Optimise training infrastructure to improve efficiency and reliability</li>\n<li>Develop dev tooling to enhance team productivity</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have significant software engineering experience and a proven track record of building complex systems</li>\n<li>Hold an advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field</li>\n<li>Are proficient in Python and experienced with deep learning frameworks</li>\n<li>Are results-oriented with a bias towards flexibility and impact</li>\n<li>Enjoy pair programming and collaborative work, and are willing to take on tasks outside your job description to support the team</li>\n<li>View research and engineering as two sides of the same coin, seeking to understand all aspects of the research program to maximise impact</li>\n<li>Care about the societal impacts of your work and have ambitious goals for AI safety and general progress</li>\n</ul>\n<p><strong>Strong candidates may have:</strong></p>\n<ul>\n<li>Experience with JAX</li>\n<li>Experience with reinforcement learning</li>\n<li>Experience working on high-performance, large-scale ML systems</li>\n<li>Familiarity with accelerators, Kubernetes, and OS internals</li>\n<li>Experience with language modeling using transformer architectures</li>\n<li>Background in large-scale ETL processes</li>\n<li>Experience with distributed training at scale (thousands of accelerators)</li>\n</ul>\n<p><strong>Strong candidates need not have:</strong></p>\n<ul>\n<li>Experience in all of the above areas — we value breadth of interest and willingness to learn over checking every box</li>\n<li>Prior work specifically on language models or transformers; strong engineering fundamentals and ML knowledge transfer well</li>\n<li>An advanced degree — exceptional engineers with strong research instincts are equally encouraged to apply</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>Education requirements: We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n<li>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.</li>\n<li>Visa sponsorship: We do sponsor visas! However, we aren&#39;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.</li>\n</ul>\n<p><strong>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&#39;re interested in this work.</strong></p>\n<p><strong>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&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</strong></p>\n<p><strong>How we&#39;re different</strong></p>\n<p>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&#39;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.</p>\n<p>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</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_9c72720b-6af","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5126127008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"£260,000 - £630,000GBP","x-skills-required":["software engineering","Python","deep learning frameworks","JAX","reinforcement learning","high-performance, large-scale ML systems","accelerators","Kubernetes","OS internals","language modeling using transformer architectures","large-scale ETL processes","distributed training at scale"],"x-skills-preferred":["JAX","reinforcement learning","high-performance, large-scale ML systems","accelerators","Kubernetes","OS internals","language modeling using transformer architectures","large-scale ETL processes","distributed training at scale"],"datePosted":"2026-03-08T13:50:55.750Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, Python, deep learning frameworks, JAX, reinforcement learning, high-performance, large-scale ML systems, accelerators, Kubernetes, OS internals, language modeling using transformer architectures, large-scale ETL processes, distributed training at scale, JAX, reinforcement learning, high-performance, large-scale ML systems, accelerators, Kubernetes, OS internals, language modeling using transformer architectures, large-scale ETL processes, distributed training at scale","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":260000,"maxValue":630000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_601a3593-052"},"title":"Research Engineer, Machine Learning (Reinforcement Learning)","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;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.</p>\n<p><strong>About the Role</strong></p>\n<p>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&#39;ll work on fundamental research in reinforcement learning, creating &#39;agentic&#39; 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.</p>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>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.</li>\n<li>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.</li>\n<li>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.</li>\n<li>Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Are proficient in Python and async/concurrent programming with frameworks like Trio</li>\n<li>Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)</li>\n<li>Have industry experience in machine learning research</li>\n<li>Can balance research exploration with engineering implementation</li>\n<li>Enjoy pair programming (we love to pair!)</li>\n<li>Care about code quality, testing, and performance</li>\n<li>Have strong systems design and communication skills</li>\n<li>Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems</li>\n</ul>\n<p><strong>Strong candidates may have:</strong></p>\n<ul>\n<li>Familiarity with LLM architectures and training methodologies</li>\n<li>Experience with reinforcement learning techniques and environments</li>\n<li>Experience with virtualization and sandboxed code execution environments</li>\n<li>Experience with Kubernetes</li>\n<li>Experience with distributed systems or high-performance computing</li>\n<li>Experience with Rust and/or C++</li>\n</ul>\n<p><strong>Strong candidates need not have:</strong></p>\n<ul>\n<li>Formal certifications or education credentials</li>\n<li>Academic research experience or publication history</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;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.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong> 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&#39;re interested in this work. We think AI systems like the ones we&#39;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.</p>\n<p><strong>Your safety matters to us.</strong> To protect yourself from potential</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_601a3593-052","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4613568008","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$500,000 - $850,000USD","x-skills-required":["Python","async/concurrent programming","Trio","PyTorch","TensorFlow","JAX","machine learning frameworks","reinforcement learning techniques","environments","virtualization","sandboxed code execution environments","Kubernetes","distributed systems","high-performance computing","Rust","C++"],"x-skills-preferred":["LLM architectures","training methodologies","reinforcement learning","distributed systems","high-performance computing"],"datePosted":"2026-03-08T13:49:41.142Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, async/concurrent programming, Trio, PyTorch, TensorFlow, JAX, machine learning frameworks, reinforcement learning techniques, environments, virtualization, sandboxed code execution environments, Kubernetes, distributed systems, high-performance computing, Rust, C++, LLM architectures, training methodologies, reinforcement learning, distributed systems, high-performance computing","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":500000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_447c26bd-a83"},"title":"Research Engineer, Universes","description":"<p><strong>About the Role</strong></p>\n<p>We&#39;re looking for Research Engineers to help us build the next generation of training environments for capable and safe agentic AI. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to research direction.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Build the next generation of agentic environments</li>\n<li>Build rigorous evaluations that measure real capability</li>\n<li>Collaborate across research and infrastructure teams to ship environments into production training</li>\n<li>Debug and iterate rapidly across research and production ML stacks</li>\n<li>Contribute to research culture through technical discussions and collaborative problem-solving</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Are highly impact-driven — you care about outcomes, not activity</li>\n<li>Operate with high agency</li>\n<li>Have good research taste or senior technical experience, demonstrating good judgment in identifying what actually matters in complex problem spaces</li>\n<li>Can balance research exploration with engineering implementation</li>\n<li>Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems</li>\n<li>Are comfortable with uncertainty and adapt quickly as the landscape shifts</li>\n<li>Have strong software engineering skills and can build robust infrastructure</li>\n<li>Enjoy pair programming (we love to pair!)</li>\n</ul>\n<p><strong>Strong candidates may also have one or more of the following:</strong></p>\n<ul>\n<li>Have industry experience with large language model training, fine-tuning or evaluation</li>\n<li>Have industry experience building RL environments, simulation systems, or large-scale ML infrastructure</li>\n<li>Senior experience in a relevant technical field even if transitioning domains</li>\n<li>Deep expertise in sandboxing, containerization, VM infrastructure, or distributed systems</li>\n<li>Published influential work in relevant ML areas</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>Education requirements: We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n<li>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.</li>\n<li>Visa sponsorship: We do sponsor visas! However, we aren&#39;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.</li>\n</ul>\n<p><strong>How we&#39;re different</strong></p>\n<p>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. 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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.</p>\n<p><strong>About the Role</strong></p>\n<p>We&#39;re looking for a Software Engineer to work at the intersection of engineering and research on the Claude Code team. In this role, you&#39;ll collaborate directly with Anthropic&#39;s researchers to improve Claude’s coding capabilities through tooling, infrastructure, and evaluations. You&#39;ll build systems that help us understand where Claude Code excels and where it falls short—and then help close those gaps.</p>\n<p>We&#39;re looking for engineers who can build robust, complex systems and who thrive in fast-paced, high-intensity environments. You&#39;ll take ambiguous problems and turn them into reliable infrastructure that accelerates our research.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Design and build eval systems that measure model capabilities across diverse coding tasks</li>\n</ul>\n<ul>\n<li>Build tooling and infrastructure that enables researchers to run experiments at scale</li>\n</ul>\n<ul>\n<li>Develop pipelines for data collection, processing, and analysis</li>\n</ul>\n<ul>\n<li>Create internal tools that improve researcher productivity and accelerate iteration cycles</li>\n</ul>\n<ul>\n<li>Serve as a bridge between product and research—bring strong product intuition to inform which capabilities matter most</li>\n</ul>\n<ul>\n<li>Work closely with researchers to translate research questions into engineering solutions</li>\n</ul>\n<ul>\n<li>Own systems end-to-end—from design through production reliability</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have built and owned complex systems—pipelines, infrastructure, or software that orchestrates many components and handles significant state and logic</li>\n</ul>\n<ul>\n<li>Thrive in high-intensity environments with fast iteration cycles</li>\n</ul>\n<ul>\n<li>Take full ownership of problems and drive them to completion independently</li>\n</ul>\n<ul>\n<li>Are a power user of agentic coding tools and have strong intuition about model capabilities and limitations</li>\n</ul>\n<ul>\n<li>Are comfortable diving into unfamiliar technical domains and figuring things out quickly</li>\n</ul>\n<ul>\n<li>Care deeply about correctness and reliability in the systems you build</li>\n</ul>\n<ul>\n<li>Are excited to work at the boundary between engineering and AI research</li>\n</ul>\n<ul>\n<li>Have at least 5 years of work experience</li>\n</ul>\n<p><strong>Strong candidates may also have experience with:</strong></p>\n<ul>\n<li>Writing or maintaining eval/evaluation frameworks</li>\n</ul>\n<ul>\n<li>Reinforcement learning systems</li>\n</ul>\n<ul>\n<li>Working in high-performance, demanding environments—trading firms, quant funds, competitive research labs, or fast-moving startups where intensity is the norm</li>\n</ul>\n<ul>\n<li>Have research computing or scientific infrastructure background</li>\n</ul>\n<ul>\n<li>Have a strong quantitative foundation (math, physics, or related fields)</li>\n</ul>\n<ul>\n<li>Python and TypeScript</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;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.</p>\n<p><strong>How we&#39;re different</strong></p>\n<p>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. 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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.</p>\n<p><strong>Key Responsibilities:</strong></p>\n<ul>\n<li>Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development</li>\n</ul>\n<ul>\n<li>Independently lead small research projects while collaborating with team members on larger initiatives</li>\n</ul>\n<ul>\n<li>Design, run, and analyse scientific experiments to advance our understanding of large language models</li>\n</ul>\n<ul>\n<li>Optimise and scale our training infrastructure to improve efficiency and reliability</li>\n</ul>\n<ul>\n<li>Develop and improve dev tooling to enhance team productivity</li>\n</ul>\n<ul>\n<li>Contribute to the entire stack, from low-level optimisations to high-level model design</li>\n</ul>\n<p><strong>Qualifications:</strong></p>\n<ul>\n<li>Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field</li>\n</ul>\n<ul>\n<li>Strong software engineering skills with a proven track record of building complex systems</li>\n</ul>\n<ul>\n<li>Expertise in Python and experience with deep learning frameworks (PyTorch preferred)</li>\n</ul>\n<ul>\n<li>Familiarity with large-scale machine learning, particularly in the context of language models</li>\n</ul>\n<ul>\n<li>Ability to balance research goals with practical engineering constraints</li>\n</ul>\n<ul>\n<li>Strong problem-solving skills and a results-oriented mindset</li>\n</ul>\n<ul>\n<li>Excellent communication skills and ability to work in a collaborative environment</li>\n</ul>\n<ul>\n<li>Care about the societal impacts of your work</li>\n</ul>\n<p><strong>Preferred Experience:</strong></p>\n<ul>\n<li>Work on high-performance, large-scale ML systems</li>\n</ul>\n<ul>\n<li>Familiarity with GPUs, Kubernetes, and OS internals</li>\n</ul>\n<ul>\n<li>Experience with language modelling using transformer architectures</li>\n</ul>\n<ul>\n<li>Knowledge of reinforcement learning techniques</li>\n</ul>\n<ul>\n<li>Background in large-scale ETL processes</li>\n</ul>\n<p><strong>You&#39;ll thrive in this role if you:</strong></p>\n<ul>\n<li>Have significant software engineering experience</li>\n</ul>\n<ul>\n<li>Are results-oriented with a bias towards flexibility and impact</li>\n</ul>\n<ul>\n<li>Willingly take on tasks outside your job description to support the team</li>\n</ul>\n<ul>\n<li>Enjoy pair programming and collaborative work</li>\n</ul>\n<ul>\n<li>Are eager to learn more about machine learning research</li>\n</ul>\n<ul>\n<li>Are enthusiastic to work at an organisation that functions as a single, cohesive team pursuing large-scale AI research projects</li>\n</ul>\n<ul>\n<li>Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research</li>\n</ul>\n<ul>\n<li>View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximise the impact of your insights</li>\n</ul>\n<ul>\n<li>Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term.</li>\n</ul>\n<p><strong>Sample Projects:</strong></p>\n<ul>\n<li>Optimising the throughput of novel attention mechanisms</li>\n</ul>\n<ul>\n<li>Comparing compute efficiency of different Transformer variants</li>\n</ul>\n<ul>\n<li>Preparing large-scale datasets for efficient model consumption</li>\n</ul>\n<ul>\n<li>Scaling distributed training jobs to thousands of GPUs</li>\n</ul>\n<ul>\n<li>Designing fault tolerance strategies for our training infrastructure</li>\n</ul>\n<ul>\n<li>Creating interactive visualisations of model internals, such as attention patterns</li>\n</ul>\n<p><strong>Benefits:</strong></p>\n<p>At Anthropic, we are committed to fostering a diverse and inclusive workplace. We strongly encourage applications from candidates of all backgrounds, including those from underrepresented groups in tech.</p>\n<p><strong>Logistics:</strong></p>\n<ul>\n<li>Education requirements: We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n</ul>\n<ul>\n<li>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.</li>\n</ul>\n<ul>\n<li>Visa sponsorship: We do sponsor visas! However, we aren&#39;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.</li>\n</ul>\n<ul>\n<li>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&#39;re interested in this work.</li>\n</ul>\n<ul>\n<li>Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from https://job-boards.greenhouse.io.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_390c02fb-0e8","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5119713008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"£260,000 - £630,000GBP","x-skills-required":["Python","Deep learning frameworks (PyTorch preferred)","Large-scale machine learning","Model architecture","Algorithms","Data processing","Optimizer development"],"x-skills-preferred":["GPU","Kubernetes","OS internals","Language modelling using transformer architectures","Reinforcement learning techniques","Background in large-scale ETL processes"],"datePosted":"2026-03-08T13:48:13.824Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Deep learning frameworks (PyTorch preferred), Large-scale machine learning, Model architecture, Algorithms, Data processing, Optimizer development, GPU, Kubernetes, OS internals, Language modelling using transformer architectures, Reinforcement learning techniques, Background in large-scale ETL processes","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":260000,"maxValue":630000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_cf63279d-d28"},"title":"Research Engineer, Reward Models Platform","description":"<p><strong>About the role</strong></p>\n<p>You will deeply understand the research workflows of our Finetuning teams and automate the high-friction parts – turning days of manual experimentation into hours. You’ll build the tools and infrastructure that enable researchers across the organisation to develop, evaluate, and optimise reward signals for training our models. Your scalable platforms will make it easy to experiment with different reward methodologies, assess their robustness, and iterate rapidly on improvements to help the rest of Anthropic train our reward models.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Design and build infrastructure that enables researchers to rapidly iterate on reward signals, including tools for rubric development, human feedback data analysis, and reward robustness evaluation</li>\n<li>Develop systems for automated quality assessment of rewards, including detection of reward hacks and other pathologies</li>\n<li>Create tooling that allows researchers to easily compare different reward methodologies (preference models, rubrics, programmatic rewards) and understand their effects</li>\n<li>Build pipelines and workflows that reduce toil in reward development, from dataset preparation to evaluation to deployment</li>\n<li>Implement monitoring and observability systems to track reward signal quality and surface issues during training runs</li>\n<li>Collaborate with researchers to translate science requirements into platform capabilities</li>\n<li>Optimise existing systems for performance, reliability, and ease of use</li>\n<li>Contribute to the development of best practices and documentation for reward development workflows</li>\n</ul>\n<p><strong>You may be a good fit if you</strong></p>\n<ul>\n<li>Have prior research experience</li>\n<li>Are excited to work closely with researchers and translate ambiguous requirements into well-scoped engineering projects</li>\n<li>Have strong Python skills</li>\n<li>Have experience with ML workflows and data pipelines, and building related infrastructure/tooling/platforms</li>\n<li>Are comfortable working across the stack, ranging from data pipelines to experiment tracking to user-facing tooling</li>\n<li>Can balance building robust, maintainable systems with the need to move quickly in a research environment</li>\n<li>Are results-oriented, with a bias towards flexibility and impact</li>\n<li>Pick up slack, even if it goes outside your job description</li>\n<li>Care about the societal impacts of your work and are motivated by Anthropic&#39;s mission to develop safe AI</li>\n</ul>\n<p><strong>Strong candidates may also have experience with</strong></p>\n<ul>\n<li>Experience with ML research</li>\n<li>Building internal tooling and platforms for ML researchers</li>\n<li>Data quality assessment and pipeline optimisation</li>\n<li>Experiment tracking, evaluation frameworks, or MLOps tooling</li>\n<li>Large-scale data processing (e.g., Spark, Hive, or similar)</li>\n<li>Kubernetes, distributed systems, or cloud infrastructure</li>\n<li>Familiarity with reinforcement learning or fine-tuning workflows</li>\n</ul>\n<p><strong>Representative projects</strong></p>\n<ul>\n<li>Building infrastructure that allows researchers to rapidly test new rubric designs against small models before scaling up</li>\n<li>Developing automated systems to detect reward hacks and surface problematic behaviours during training</li>\n<li>Creating tooling for comparing different grading methodologies and understanding their effects on model behaviour</li>\n<li>Building a data quality flywheel that helps researchers identify problematic transcripts and feed improvements back into the system</li>\n<li>Developing dashboards and monitoring systems that give researchers visibility into reward signal quality across training runs</li>\n<li>Streamlining dataset preparation workflows to reduce latency and operational overhead</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>Education requirements: We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n<li>Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. 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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.</p>\n<p><strong>About the Team</strong></p>\n<p>Our team is organised around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier. Our team likes to think across the whole model stack. Currently the team is focused on improving models&#39; abilities to use computers – as a laboratory for long horizon tasks and a key blocker to many scientific workflows.</p>\n<p><strong>About the role</strong></p>\n<p>As a Research Engineer on our team you will work end to end, identifying and addressing key blockers on the path to scientific AGI. Strong candidates should have familiarity with language model training, evaluation, and inference, be comfortable triaging research ideas and diagnosing problems and enjoy working collaboratively. Familiarity with performance optimisation, distributed systems, vm/sandboxing/container deployment, and large scale data pipelines is highly encouraged.</p>\n<p>Join us in our mission to develop advanced AI systems that are both powerful and beneficial for humanity.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Working across the full stack to identify and remove bottlenecks preventing progress toward scientific AGI</li>\n<li>Develop approaches to address long-horizon task completion and complex reasoning challenges essential for scientific discovery</li>\n<li>Scaling research ideas from prototype to production</li>\n<li>Create benchmarks and evaluation frameworks to measure model capabilities in scientific workflows and computer use</li>\n<li>Implement distributed training systems and performance optimisations to support large-scale model development</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have 8+ years of ML research experience</li>\n<li>Are familiar with large scale language model training, evaluation, and inference pipelines</li>\n<li>Enjoy obsessively iterating on immediate blockers towards longterm goals</li>\n<li>Thrive working collaboratively to solve problems</li>\n<li>Have expertise in performance optimisation and distributed computing systems</li>\n<li>Show strong problem-solving skills and ability to identify technical bottlenecks in complex systems</li>\n<li>Can translate research concepts into scalable engineering solutions</li>\n<li>Have a track record of shipping ML systems that tackle challenging multi-step reasoning problems</li>\n</ul>\n<p><strong>Strong candidates may also have:</strong></p>\n<ul>\n<li>Expertise with performance optimisation for language model inference and training</li>\n<li>Experience with computer use automation and agentic AI systems</li>\n<li>A history working on reinforcement learning approaches for complex task completion</li>\n<li>Knowledge of containerisation technologies (Docker, Kubernetes) and cloud deployment at scale</li>\n<li>Demonstrated ability to work across multiple domains (language modelling, systems engineering, scientific computing)</li>\n<li>Have experience with VM/sandboxing/container deployment and large-scale data processing</li>\n<li>Experience working with large scale data problem solving and infrastructure</li>\n<li>Published research or practical experience in scientific AI applications or long-horizon reasoning</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;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.</p>\n<p><strong>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&#39;re interested in this work. We think AI systems like the ones we&#39;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.</strong></p>\n<p><strong>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&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</strong></p>\n<p><strong>How we&#39;re different</strong></p>\n<p>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.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_82a4d6f7-01c","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4593216008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$350,000 - $850,000USD","x-skills-required":["language model training","evaluation","inference","performance optimisation","distributed systems","vm/sandboxing/container deployment","large scale data pipelines"],"x-skills-preferred":["performance optimisation for language model inference and training","computer use automation and agentic AI systems","reinforcement learning approaches for complex task completion","containerisation technologies (Docker, Kubernetes) and cloud deployment at scale","VM/sandboxing/container deployment and large-scale data processing"],"datePosted":"2026-03-08T13:47:19.194Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"language model training, evaluation, inference, performance optimisation, distributed systems, vm/sandboxing/container deployment, large scale data pipelines, performance optimisation for language model inference and training, computer use automation and agentic AI systems, reinforcement learning approaches for complex task completion, containerisation technologies (Docker, Kubernetes) and cloud deployment at scale, VM/sandboxing/container deployment and large-scale data processing","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_912450ea-c61"},"title":"Research Engineer, Environment Scaling","description":"<p><strong>About the role</strong></p>\n<p>The Environment Scaling team is a team of researchers and engineers whose goal is to improve the intelligence of our public models for novel verticals and use cases. The team builds the training environments that fuel RL at scale. This is a unique role that combines executing directly on ML research, data operations, and project management to improve our models. You&#39;ll own the end-to-end process of creating RL environments for new capabilities: identifying high-value tasks, designing reward signals, managing vendor relationships, and measuring impact on model performance.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Improve and execute our fine-tuning strategies for adapting Claude to new domains and tasks</li>\n<li>Manage technical relationships with external data vendors, including evaluation of data quality and reward design</li>\n<li>Collaborate with domain experts to design data pipelines and evaluations</li>\n<li>Explore novel ways of creating RL environments for high value tasks</li>\n<li>Develop and improve QA frameworks to catch reward hacking and ensure environment quality</li>\n<li>Partner with other RL research teams and product teams to translate capability goals into training environments and evals</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have experience with fine-tuning large language models for specific domains or real-world use cases and/or domain expertise in an area where we would like to make our models more useful.</li>\n<li>Have experience with reinforcement learning, reward design, or training data curation for LLMs</li>\n<li>Are comfortable managing technical vendor relationships and iterating quickly on feedback</li>\n<li>Find value in reading through datasets to understand them and spot issues</li>\n<li>Have strong project management and interpersonal skills</li>\n<li>Are passionate about making AI more useful and accessible across different industries</li>\n<li>Are excited about a role that includes a combination of ML research, data operations, and project management</li>\n</ul>\n<p><strong>Strong candidates may also:</strong></p>\n<ul>\n<li>Have experience training production ML systems</li>\n<li>Be familiar with distributed systems and cloud infrastructure</li>\n<li>Have domain expertise in an area where we would like to make our models more useful</li>\n<li>Have experience working with external vendors or technical partners</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>Education requirements: We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n<li>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.</li>\n<li>Visa sponsorship: We do sponsor visas! However, we aren&#39;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.</li>\n</ul>\n<p><strong>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&#39;re interested in this work.</strong></p>\n<p><strong>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&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</strong></p>\n<p><strong>How we&#39;re different</strong></p>\n<p>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&#39;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.</p>\n<p>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 &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.</p>\n<p><strong>Come work with us!</strong></p>\n<p>Anthropic is a public benefit corporation headquartered in San Francisco, CA.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_912450ea-c61","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4951064008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $850,000USD","x-skills-required":["fine-tuning large language models","reinforcement learning","reward design","training data curation","project management","interpersonal skills"],"x-skills-preferred":["experience training production ML systems","distributed systems and cloud infrastructure","domain expertise in an area where we would like to make our models more useful","experience working with external vendors or technical partners"],"datePosted":"2026-03-08T13:47:17.433Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"fine-tuning large language models, reinforcement learning, reward design, training data curation, project management, interpersonal skills, experience training production ML systems, distributed systems and cloud infrastructure, domain expertise in an area where we would like to make our models more useful, experience working with external vendors or technical partners","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_716d3247-e3f"},"title":"ML/Research Engineer, Safeguards","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;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.</p>\n<p><strong>About the role</strong></p>\n<p>We are looking for ML Engineers and Research Engineers to help detect and mitigate misuse of our AI systems. As a member of the Safeguards ML team, you will build systems that identify harmful use—from individual policy violations to sophisticated, coordinated attacks—and develop defenses that keep our products safe as capabilities advance. You will also work on systems that protect user wellbeing and ensure our models behave appropriately across a wide range of contexts. This work feeds directly into Anthropic&#39;s Responsible Scaling Policy commitments.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Develop classifiers to detect misuse and anomalous behavior at scale. This includes developing synthetic data pipelines for training classifiers and methods to automatically source representative evaluations to iterate on</li>\n<li>Build systems to monitor for harms that span multiple exchanges, such as coordinated cyber attacks and influence operations, and develop new methods for aggregating and analyzing signals across contexts</li>\n<li>Evaluate and improve the safety of agentic products—developing both threat models and environments to test for agentic risks, and developing and deploying mitigations for prompt injection attacks</li>\n<li>Conduct research on automated red-teaming, adversarial robustness, and other research that helps test for or find misuse</li>\n</ul>\n<p><strong>You may be a good fit if you</strong></p>\n<ul>\n<li>Have 4+ years of experience in ML engineering, research engineering, or applied research, in academia or industry</li>\n<li>Have proficiency in Python and experience building ML systems</li>\n<li>Are comfortable working across the research-to-deployment pipeline, from exploratory experiments to production systems</li>\n<li>Are worried about misuse risks of AI systems, and want to work to mitigate them</li>\n<li>Have strong communication skills and ability to explain complex technical concepts to non-technical stakeholders</li>\n</ul>\n<p><strong>Strong candidates may also have experience with</strong></p>\n<ul>\n<li>Language modeling and transformers</li>\n<li>Building classifiers, anomaly detection systems, or behavioral ML</li>\n<li>Adversarial machine learning or red-teaming</li>\n<li>Interpretability or probes</li>\n<li>Reinforcement learning</li>\n<li>High-performance, large-scale ML systems</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship</strong></p>\n<p>We do sponsor visas! However, we aren&#39;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.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong></p>\n<p>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&#39;re interested in this work.</p>\n<p><strong>Your safety matters to us.</strong></p>\n<p>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&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</p>\n<p><strong>How we&#39;re different</strong></p>\n<p>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&#39;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.</p>\n<p>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 &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.</p>\n<p><strong>Come work with us!</strong></p>\n<p>Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_716d3247-e3f","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4949336008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $500,000USD","x-skills-required":["Python","Machine Learning","Research Engineering","Adversarial Machine Learning","Red-teaming","Interpretability","Probes","Reinforcement Learning","High-performance, large-scale ML systems"],"x-skills-preferred":["Language modeling and transformers","Building classifiers, anomaly detection systems, or behavioral ML"],"datePosted":"2026-03-08T13:46:45.711Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Machine Learning, Research Engineering, Adversarial Machine Learning, Red-teaming, Interpretability, Probes, Reinforcement Learning, High-performance, large-scale ML systems, Language modeling and transformers, Building classifiers, anomaly detection systems, or behavioral ML","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":500000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_cd4d8376-407"},"title":"Research Engineer, Pre-training","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;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.</p>\n<p><strong>Key Responsibilities:</strong></p>\n<ul>\n<li>Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development</li>\n<li>Independently lead small research projects while collaborating with team members on larger initiatives</li>\n<li>Design, run, and analyse scientific experiments to advance our understanding of large language models</li>\n<li>Optimise and scale our training infrastructure to improve efficiency and reliability</li>\n<li>Develop and improve dev tooling to enhance team productivity</li>\n<li>Contribute to the entire stack, from low-level optimisations to high-level model design</li>\n</ul>\n<p><strong>Qualifications:</strong></p>\n<ul>\n<li>Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field</li>\n<li>Strong software engineering skills with a proven track record of building complex systems</li>\n<li>Expertise in Python and experience with deep learning frameworks (PyTorch preferred)</li>\n<li>Familiarity with large-scale machine learning, particularly in the context of language models</li>\n<li>Ability to balance research goals with practical engineering constraints</li>\n<li>Strong problem-solving skills and a results-oriented mindset</li>\n<li>Excellent communication skills and ability to work in a collaborative environment</li>\n<li>Care about the societal impacts of your work</li>\n</ul>\n<p><strong>Preferred Experience:</strong></p>\n<ul>\n<li>Work on high-performance, large-scale ML systems</li>\n<li>Familiarity with GPUs, Kubernetes, and OS internals</li>\n<li>Experience with language modelling using transformer architectures</li>\n<li>Knowledge of reinforcement learning techniques</li>\n<li>Background in large-scale ETL processes</li>\n</ul>\n<p><strong>You&#39;ll thrive in this role if you:</strong></p>\n<ul>\n<li>Have significant software engineering experience</li>\n<li>Are results-oriented with a bias towards flexibility and impact</li>\n<li>Willingly take on tasks outside your job description to support the team</li>\n<li>Enjoy pair programming and collaborative work</li>\n<li>Are eager to learn more about machine learning research</li>\n<li>Are enthusiastic to work at an organisation that functions as a single, cohesive team pursuing large-scale AI research projects</li>\n<li>Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research</li>\n<li>View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximise the impact of your insights</li>\n<li>Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term.</li>\n</ul>\n<p><strong>Sample Projects:</strong></p>\n<ul>\n<li>Optimising the throughput of novel attention mechanisms</li>\n<li>Comparing compute efficiency of different Transformer variants</li>\n<li>Preparing large-scale datasets for efficient model consumption</li>\n<li>Scaling distributed training jobs to thousands of GPUs</li>\n<li>Designing fault tolerance strategies for our training infrastructure</li>\n<li>Creating interactive visualisations of model internals, such as attention patterns</li>\n</ul>\n<p><strong>At Anthropic, we are committed to fostering a diverse and inclusive workplace. We strongly encourage applications from candidates of all backgrounds, including those from underrepresented groups in tech.</strong></p>\n<p><strong>If you&#39;re excited about pushing the boundaries of AI while prioritising safety and ethics, we want to hear from you!</strong></p>\n<p><strong>The annual compensation range for this role is listed below.</strong></p>\n<p>For sales roles, the range provided is the role’s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.</p>\n<p><strong>Annual Salary:</strong></p>\n<p>$350,000 - $850,000USD</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_cd4d8376-407","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4616971008","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $850,000USD","x-skills-required":["Python","Deep learning frameworks (PyTorch preferred)","Large-scale machine learning","Model architecture","Algorithms","Data processing","Optimizer development"],"x-skills-preferred":["GPU","Kubernetes","OS internals","Language modelling using transformer architectures","Reinforcement learning techniques","Background in large-scale ETL processes"],"datePosted":"2026-03-08T13:46:36.524Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA, Seattle, WA, New York City, NY"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Deep learning frameworks (PyTorch preferred), Large-scale machine learning, Model architecture, Algorithms, Data processing, Optimizer development, GPU, Kubernetes, OS internals, Language modelling using transformer architectures, Reinforcement learning techniques, Background in large-scale ETL processes","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_da726093-b19"},"title":"Research Engineer, Discovery","description":"<p><strong>About the Role</strong></p>\n<p>As a Research Engineer on our team, you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments</li>\n<li>Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities</li>\n<li>Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI.</li>\n<li>Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows</li>\n<li>Collaborate to translate experimental requirements into production-ready infrastructure</li>\n<li>Develop large scale data pipelines to handle advanced language model training requirements</li>\n<li>Optimize large scale training and inference pipelines for stable and efficient reinforcement learning</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems</li>\n<li>Are a strong communicator and enjoy working collaboratively</li>\n<li>Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads</li>\n<li>Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale</li>\n<li>Have proven track record of building large-scale data pipelines and distributed storage systems</li>\n<li>Excel at diagnosing and resolving complex infrastructure challenges in production environments</li>\n<li>Can work effectively across the full ML stack from data pipelines to performance optimization</li>\n<li>Have experience collaborating with other researchers to scale experimental ideas</li>\n<li>Thrive in fast-paced environments and can rapidly iterate from experimentation to production</li>\n</ul>\n<p><strong>Strong candidates may also have:</strong></p>\n<ul>\n<li>Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)</li>\n<li>Background in building infrastructure for AI research labs or large-scale ML organizations</li>\n<li>Knowledge of GPU/TPU architectures and language model inference optimization</li>\n<li>Experience with cloud platforms (AWS, GCP) at enterprise scale</li>\n<li>Familiarity with VM and container orchestration.</li>\n<li>Experience with workflow orchestration tools and experiment management systems</li>\n<li>History working with large scale reinforcement learning</li>\n<li>Comfort with large scale data pipelines (Beam, Spark, Dask, …)</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<ul>\n<li>Education requirements: We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</li>\n<li>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.</li>\n<li>Visa sponsorship: We do sponsor visas! However, we aren&#39;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.</li>\n</ul>\n<p><strong>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&#39;re interested in this work.</strong></p>\n<p><strong>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&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</strong></p>\n<p><strong>How we&#39;re different</strong></p>\n<p>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 projects, and we&#39;re committed to making a positive impact on the world.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_da726093-b19","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4669581008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $850,000 USD","x-skills-required":["infrastructure engineering","large-scale distributed systems","performance optimization","containerization technologies","orchestration at scale","data pipelines","distributed storage systems","complex infrastructure challenges","ML stack","workflow orchestration tools","experiment management systems","reinforcement learning","large scale data pipelines"],"x-skills-preferred":["language model training infrastructure","distributed ML frameworks","GPU/TPU architectures","language model inference optimization","cloud platforms","VM and container orchestration","workflow orchestration tools","experiment management systems","large scale reinforcement learning","large scale data pipelines"],"datePosted":"2026-03-08T13:46:32.661Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"infrastructure engineering, large-scale distributed systems, performance optimization, containerization technologies, orchestration at scale, data pipelines, distributed storage systems, complex infrastructure challenges, ML stack, workflow orchestration tools, experiment management systems, reinforcement learning, large scale data pipelines, language model training infrastructure, distributed ML frameworks, GPU/TPU architectures, language model inference optimization, cloud platforms, VM and container orchestration, workflow orchestration tools, experiment management systems, large scale reinforcement learning, large scale data pipelines","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_442b4d5e-4a8"},"title":"Research Engineer, Virtual Collaborator (Cowork)","description":"<p><strong>About the role</strong></p>\n<p>We are looking for a Research Engineer to help us train Claude specifically for virtual collaborator workflows. While Claude excels at general tasks, a lot of knowledge work requires targeted training on real organisational data and workflows. Your job will be to design and implement reinforcement learning (RL) environments that transform Claude into the best virtual collaborator, training on realistic tasks from navigating internal knowledge to creating financial models.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Training Claude on document manipulation with good taste, including understanding, enhancing, and co-creating (e.g., Office doc formats, data visualisation)</li>\n<li>Designing and implementing reinforcement learning pipelines targeted at virtual collaborator use cases (productivity, organisational navigation, vertical domains)</li>\n<li>Building and scaling our data creation platform for generating high-quality, open-ended tasks with domain experts and crowdworkers Integrating real organisational data to create realistic training environments</li>\n<li>Developing robust evaluation systems that maintain quality while avoiding reward hacking</li>\n<li>Partnering directly with product teams (e.g., Cowork, claude.ai) to ensure training aligns with product features</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Are a very experienced Python programmer who can quickly produce reliable, high-quality code that your teammates love using</li>\n<li>Have 5-8 years of strong machine learning experience</li>\n<li>Thrive at the intersection of research and product, with a pragmatic approach to solving real-world problems</li>\n<li>Are comfortable with ambiguity and can balance research rigor with shipping deadlines</li>\n<li>Enjoy collaborating across multiple teams (data operations, model training, product)</li>\n<li>Can context-switch between research problems and product engineering tasks</li>\n<li>Care about making AI genuinely helpful for everyday enterprise workflows</li>\n</ul>\n<p><strong>Strong candidates may also have experience with:</strong></p>\n<ul>\n<li>Creating RL envs for realistic tasks.</li>\n<li>Reward modelling and preventing reward hacking</li>\n<li>Building human-in-the-loop training systems or crowdsourcing platforms</li>\n<li>Working with enterprise tools and APIs (Google Workspace, Microsoft Office, Slack, etc.)</li>\n<li>Developing evaluation frameworks for open-ended tasks</li>\n<li>Domain expertise in finance, legal, or healthcare workflows</li>\n<li>Creating scalable data pipelines with quality control mechanisms</li>\n<li>Translating product requirements into technical training objectives</li>\n</ul>\n<p><strong>Deadline to apply:</strong></p>\n<p>None. Applications will be reviewed on a rolling basis.</p>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship:</strong></p>\n<p>We do sponsor visas! However, we aren&#39;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.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong></p>\n<p>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&#39;re interested in this work.</p>\n<p><strong>Your safety matters to us.</strong></p>\n<p>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&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</p>\n<p><strong>How we&#39;re different</strong></p>\n<p>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&#39;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.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_442b4d5e-4a8","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4946308008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$500,000 - $850,000 USD","x-skills-required":["Python","Machine learning","Reinforcement learning","Data visualisation","Enterprise tools and APIs"],"x-skills-preferred":["Human-in-the-loop training systems","Crowdsourcing platforms","Domain expertise in finance, legal, or healthcare workflows"],"datePosted":"2026-03-08T13:46:25.630Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York City, NY; San Francisco, CA; Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Machine learning, Reinforcement learning, Data visualisation, Enterprise tools and APIs, Human-in-the-loop training systems, Crowdsourcing platforms, Domain expertise in finance, legal, or healthcare workflows","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":500000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_58928a28-64d"},"title":"Research Engineer/Research Scientist, Audio","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;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.</p>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have hands-on experience with training audio models, whether that&#39;s conversational speech-to-speech, speech translation, speech recognition, text-to-speech, diarization, codecs, or generative audio models</li>\n<li>Genuinely enjoy both research and engineering work, and you&#39;d describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other</li>\n<li>Are comfortable working across abstraction levels, from signal processing fundamentals to large-scale model training and inference optimization</li>\n<li>Have deep expertise with JAX, PyTorch, or large-scale distributed training, and can debug performance issues across the full stack</li>\n<li>Thrive in fast-moving environments where the most important problem might shift as we learn more about what works</li>\n<li>Communicate clearly and collaborate effectively; audio touches many parts of our systems, so you&#39;ll work closely with teams across the company</li>\n<li>Are passionate about building conversational AI that feels natural, steerable, and safe</li>\n<li>Care about the societal impacts of voice AI and want to help shape how these systems are developed responsibly</li>\n</ul>\n<p><strong>Strong candidates may also have experience with:</strong></p>\n<ul>\n<li>Large language model pretraining and finetuning</li>\n<li>Training diffusion models for image and audio generation</li>\n<li>Reinforcement learning for large language models and diffusion models</li>\n<li>End-to-end system optimization, from performance benchmarking to kernel optimization</li>\n<li>GPUs, Kubernetes, PyTorch, or distributed training infrastructure</li>\n</ul>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Training state-of-the art neural audio codecs for 48 kHz stereo audio</li>\n<li>Developing novel algorithms for diffusion pretraining and reinforcement learning</li>\n<li>Scaling audio datasets to millions of hours of high quality audio</li>\n<li>Creating robust evaluation methodologies for hard-to-measure qualities such as naturalness or expressiveness</li>\n<li>Studying training dynamics of mixed audio-text language models</li>\n<li>Optimizing latency and inference throughput for deployed streaming audio systems</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;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.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong> 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&#39;re interested in this work.</p>\n<p><strong>Your safety matters to us.</strong> 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&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</p>\n<p><strong>How we&#39;re different</strong></p>\n<p>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 systems that benefit society.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_58928a28-64d","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5074815008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $500,000 USD","x-skills-required":["audio models","speech-to-speech","speech translation","speech recognition","text-to-speech","diarization","codecs","generative audio models","JAX","PyTorch","large-scale distributed training"],"x-skills-preferred":["large language model pretraining","training diffusion models","reinforcement learning","end-to-end system optimization","GPUs","Kubernetes","PyTorch","distributed training infrastructure"],"datePosted":"2026-03-08T13:46:24.550Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"audio models, speech-to-speech, speech translation, speech recognition, text-to-speech, diarization, codecs, generative audio models, JAX, PyTorch, large-scale distributed training, large language model pretraining, training diffusion models, reinforcement learning, end-to-end system optimization, GPUs, Kubernetes, PyTorch, distributed training infrastructure","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":500000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_9d8e34bd-10a"},"title":"Research Engineer / Research Scientist, Tokens","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;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.</p>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have significant software engineering experience</li>\n<li>Are results-oriented, with a bias towards flexibility and impact</li>\n<li>Pick up slack, even if it goes outside your job description</li>\n<li>Enjoy pair programming (we love to pair!)</li>\n<li>Want to learn more about machine learning research</li>\n<li>Care about the societal impacts of your work</li>\n</ul>\n<p><strong>Strong candidates may also have experience with:</strong></p>\n<ul>\n<li>High performance, large-scale ML systems</li>\n<li>GPUs, Kubernetes, Pytorch, or OS internals</li>\n<li>Language modeling with transformers</li>\n<li>Reinforcement learning</li>\n<li>Large-scale ETL</li>\n</ul>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Optimizing the throughput of a new attention mechanism</li>\n<li>Comparing the compute efficiency of two Transformer variants</li>\n<li>Making a Wikipedia dataset in a format models can easily consume</li>\n<li>Scaling a distributed training job to thousands of GPUs</li>\n<li>Writing a design doc for fault tolerance strategies</li>\n<li>Creating an interactive visualization of attention between tokens in a language model</li>\n</ul>\n<p><strong>Annual compensation range for this role is listed below.</strong></p>\n<p>Annual Salary:</p>\n<p>$350,000 - $500,000USD</p>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;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.</p>\n<p><strong>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&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</strong></p>\n<p><strong>How we&#39;re different</strong></p>\n<p>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&#39;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.</p>\n<p>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 &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.</p>\n<p><strong>Come work with us!</strong></p>\n<p>Anthropic is a public benefit corporation headquartered in California, USA.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_9d8e34bd-10a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4951814008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $500,000USD","x-skills-required":["software engineering","machine learning research","high performance","large-scale ML systems","GPUs","Kubernetes","Pytorch","OS internals","language modeling","reinforcement learning","large-scale ETL"],"x-skills-preferred":["pair programming","collaboration","communication skills"],"datePosted":"2026-03-08T13:46:19.922Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York City, NY; Seattle, WA; San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, machine learning research, high performance, large-scale ML systems, GPUs, Kubernetes, Pytorch, OS internals, language modeling, reinforcement learning, large-scale ETL, pair programming, collaboration, communication skills","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":500000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_48f07618-377"},"title":"Research Engineer, Frontier Red Team (Autonomy)","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;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.</p>\n<p><strong>About the Team</strong></p>\n<p>The Frontier Red Team (FRT) is a small, focused technical research team within Anthropic&#39;s Policy organization. Our goal is to make the entire world safer in this era of advanced AI by understanding what these systems can do and building the defenses that matter.</p>\n<p>In 2026, we&#39;re focused on researching and ensuring safety with self-improving, highly autonomous AI systems—especially ones with cyberphysical capabilities. See our previous related work on cyberdefense, robotics, and Project Vend. This is early-stage, high-conviction research with the potential for outsized impact.</p>\n<p><strong>About the Role</strong></p>\n<p>Our team is focused on a critical question: how do we defend against a world where powerful, autonomous, self-improving AI systems may be used adversarially?</p>\n<p>As a Research Engineer on our team, you&#39;ll build and eval model organisms of autonomous systems and develop the defensive agents needed to counter them. This work sits at the intersection of AI capabilities research, security, and policy—what we learn directly shapes how Anthropic and the world prepare for advanced AI.</p>\n<p>This is applied research with real-world stakes. Your work will inform decisions at the highest levels of the company, contribute to public demonstrations that shape policy discourse, and help build technical defenses that could matter enormously as AI systems become more capable.</p>\n<p><strong>What You&#39;ll Do</strong></p>\n<ul>\n<li>Design and build autonomous AI systems that can use tools and operate across diverse environments—creating model organisms that help us understand and defend against advanced adversarial AI</li>\n</ul>\n<ul>\n<li>Create evals and training environments to understand and shape agent behavior in desirable ways</li>\n</ul>\n<ul>\n<li>Develop defensive agents that can detect, disrupt, or outcompete adversarial AI systems in realistic scenarios</li>\n</ul>\n<ul>\n<li>Interface Claude with hardware platforms (e.g. robotics, physical systems) to understand cyberphysical risks and defenses</li>\n</ul>\n<ul>\n<li>Translate technical findings into compelling demonstrations and artifacts that inform policymakers and the public</li>\n</ul>\n<ul>\n<li>Collaborate with external experts in cybersecurity, national security, and AI safety to scope and validate research directions</li>\n</ul>\n<p><strong>Sample Projects</strong></p>\n<ul>\n<li>Developing systems where Claude controls diverse hardware and robotics platforms simultaneously</li>\n</ul>\n<ul>\n<li>Creating attack-defend simulations (CTFs, wargames, adversarial games) to test defensive AI capabilities</li>\n</ul>\n<ul>\n<li>Designing and implementing RL environments for training defensive agents</li>\n</ul>\n<ul>\n<li>Pointing autonomous systems at real-world security challenges to characterize risks and develop mitigations</li>\n</ul>\n<p><strong>You May Be a Good Fit If You</strong></p>\n<ul>\n<li>Have strong software engineering skills, particularly in Python</li>\n</ul>\n<ul>\n<li>Have experience building and working with LLM-based agents or autonomous systems</li>\n</ul>\n<ul>\n<li>Are driven to find solutions to ambiguously scoped, high-stakes problems</li>\n</ul>\n<ul>\n<li>Design and run experiments quickly, iterating fast toward useful results</li>\n</ul>\n<ul>\n<li>Thrive in collaborative environments (we love pair programming!)</li>\n</ul>\n<ul>\n<li>Care deeply about AI safety and want your work to have real-world impact on how humanity navigates advanced AI</li>\n</ul>\n<ul>\n<li>Can own entire problems end-to-end, including both technical and non-technical components</li>\n</ul>\n<ul>\n<li>Are comfortable working on sensitive projects that require discretion and integrity</li>\n</ul>\n<p><strong>Strong Candidates May Also Have</strong></p>\n<ul>\n<li>Experience with reinforcement learning, self-play, or multi-agent systems</li>\n</ul>\n<ul>\n<li>Experience with robotics, hardware interfaces, or cyberphysical systems</li>\n</ul>\n<ul>\n<li>Track record of building demos or prototypes that communicate complex technical ideas</li>\n</ul>\n<ul>\n<li>Experience working with external stakeholders (policymakers, government, researchers)</li>\n</ul>\n<ul>\n<li>Familiarity with AI safety research and threat modeling for advanced AI systems</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;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.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong> 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&#39;re interested in this work. We think AI systems like the ones we&#39;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.</p>\n<p><strong>Your safety matters to us.</strong> 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 recruiters to help us find the best candidates for our open roles.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_48f07618-377","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5067100008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$320,000 - $850,000USD","x-skills-required":["Python","LLM-based agents","Autonomous systems","Reinforcement learning","Self-play","Multi-agent systems","Robotics","Hardware interfaces","Cyberphysical systems","AI safety research","Threat modeling"],"x-skills-preferred":["Software engineering","Collaborative environments","AI safety","Discretion and integrity"],"datePosted":"2026-03-08T13:45:56.349Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, LLM-based agents, Autonomous systems, Reinforcement learning, Self-play, Multi-agent systems, Robotics, Hardware interfaces, Cyberphysical systems, AI safety research, Threat modeling, Software engineering, Collaborative environments, AI safety, Discretion and integrity","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":320000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c33b2d78-cc9"},"title":"Research Lead, Training Insights","description":"<p><strong>About the role</strong></p>\n<p>As a Research Lead on the Training Insights team, you&#39;ll develop the strategy for, and lead execution on, how we measure and characterise model capabilities across training and deployment. This is a hands-on leadership role: you&#39;ll drive original research into new evaluation methodologies while leading a small team of researchers and research engineers doing the same.</p>\n<p>Your work will span the full lifecycle of model development. You&#39;ll research and build new long-horizon evaluations that test the boundaries of what our models can achieve, develop novel approaches to measuring emerging capabilities, and deepen our understanding of how those capabilities develop — both during production RL training and after. You&#39;ll also take a cross-organisational view, working across Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, and other teams to map the landscape of model evaluations at Anthropic and identify critical gaps in coverage.</p>\n<p>This role carries significant visibility and impact. You&#39;ll help shape the evaluation narrative for model releases, contributing directly to how Anthropic communicates about its models to both internal and external audiences. Done well, you will change how the industry measures and understands model capabilities, significantly furthering our safety mission.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Build new novel and long-horizon evaluations</li>\n<li>Develop novel measurement approaches for understanding how model capabilities emerge and evolve during RL training</li>\n<li>Lead strategic evaluation coverage across the company</li>\n<li>Shape the evaluation narrative for model releases</li>\n<li>Lead and mentor a small team of researchers and research engineers, setting research direction and fostering a culture of rigorous, creative research</li>\n<li>Design evaluation frameworks that balance scientific rigor with the practical demands of production training schedules</li>\n<li>Build and maintain relationships across Anthropic&#39;s research organisation to ensure evaluation insights inform training and deployment decisions</li>\n<li>Contribute to the broader research community through publications, open-source contributions, or external engagement on evaluation best practices</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have significant experience designing and running evaluations for large language models or similar complex ML systems</li>\n<li>Have led technical projects or teams, either formally or through sustained ownership of critical research directions</li>\n<li>Are equally comfortable designing experiments and writing code—you can move between research and implementation fluidly</li>\n<li>Think strategically about what to measure and why, not just how to measure it</li>\n<li>Can synthesise information across multiple teams and workstreams to form a coherent picture of model capabilities</li>\n<li>Communicate complex technical findings clearly to both technical and non-technical audiences</li>\n<li>Are results-oriented and thrive in fast-paced environments where priorities shift based on research findings</li>\n<li>Care deeply about AI safety and want your work to directly influence how capable AI systems are developed and deployed</li>\n</ul>\n<p><strong>Strong candidates may also have:</strong></p>\n<ul>\n<li>Experience building evaluations for long-horizon or agentic tasks</li>\n<li>Deep familiarity with Reinforcement Learning training dynamics and how model behaviour changes during training</li>\n<li>Published research in machine learning evaluation, benchmarking, or related areas</li>\n<li>Experience with safety evaluation frameworks and red teaming methodologies</li>\n<li>Background in psychometrics, experimental psychology, or other measurement-focused disciplines</li>\n<li>A track record of communicating evaluation results to inform high-stakes decisions about model development or deployment</li>\n<li>Experience managing or mentoring researchers and engineers</li>\n</ul>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>Designing and implementing a suite of long-horizon evaluations that test model capabilities on tasks requiring sustained reasoning, planning, and tool use over extended interactions</li>\n<li>Building systems to track capability development across RL training checkpoints, surfacing insights about when and how specific capabilities emerge</li>\n<li>Conducting a cross-org audit of evaluation coverage, identifying blind spots, and prioritising new evaluations to fill critical gaps across Pretraining, RL, Inference, and Product</li>\n<li>Developing the evaluation methodology and narrative for a major model release, working with research leads and communications to clearly characterise model capabilities and limitations</li>\n<li>Researching and prototyping novel evaluation approaches for capabilities that are difficult to measure with existing benchmarks</li>\n<li>Leading a team effort to build reusable evaluation infrastructure that serves multiple teams across the research organisation</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> 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 repsectively.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas!</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c33b2d78-cc9","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5139654008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$850,000 - $850,000USD","x-skills-required":["machine learning","evaluation methodologies","Reinforcement Learning","Pretraining","Inference","Product","Alignment","Safeguards"],"x-skills-preferred":["psychometrics","experimental psychology","safety evaluation frameworks","red teaming methodologies"],"datePosted":"2026-03-08T13:45:37.187Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"machine learning, evaluation methodologies, Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, psychometrics, experimental psychology, safety evaluation frameworks, red teaming methodologies","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":850000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_221e855f-2b9"},"title":"Research Engineer, Machine Learning (Reinforcement Learning)","description":"<p><strong>About the Role</strong></p>\n<p>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&#39;ll work on fundamental research in reinforcement learning, creating &#39;agentic&#39; 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.</p>\n<p><strong>Representative projects:</strong></p>\n<ul>\n<li>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.</li>\n</ul>\n<ul>\n<li>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.</li>\n</ul>\n<ul>\n<li>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.</li>\n</ul>\n<ul>\n<li>Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Are proficient in Python and async/concurrent programming with frameworks like Trio</li>\n</ul>\n<ul>\n<li>Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)</li>\n</ul>\n<ul>\n<li>Have industry experience in machine learning research</li>\n</ul>\n<ul>\n<li>Can balance research exploration with engineering implementation</li>\n</ul>\n<ul>\n<li>Enjoy pair programming (we love to pair!)</li>\n</ul>\n<ul>\n<li>Care about code quality, testing, and performance</li>\n</ul>\n<ul>\n<li>Have strong systems design and communication skills</li>\n</ul>\n<ul>\n<li>Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems</li>\n</ul>\n<p><strong>Strong candidates may have:</strong></p>\n<ul>\n<li>Familiarity with LLM architectures and training methodologies</li>\n</ul>\n<ul>\n<li>Experience with reinforcement learning techniques and environments</li>\n</ul>\n<ul>\n<li>Experience with virtualization and sandboxed code execution environments</li>\n</ul>\n<ul>\n<li>Experience with Kubernetes</li>\n</ul>\n<ul>\n<li>Experience with distributed systems or high-performance computing</li>\n</ul>\n<ul>\n<li>Experience with Rust and/or C++</li>\n</ul>\n<p><strong>Strong candidates need not have:</strong></p>\n<ul>\n<li>Formal certifications or education credentials</li>\n</ul>\n<ul>\n<li>Academic research experience or publication history</li>\n</ul>\n<p><strong>Deadline to apply:</strong> None. Applications will be reviewed on a rolling basis.</p>\n<p>The annual compensation range for this role is listed below.</p>\n<p>For sales roles, the range provided is the role’s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.</p>\n<p>Annual Salary:</p>\n<p>£260,000 - £630,000GBP</p>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience.</p>\n<p><strong>Location-based hybrid policy:</strong> 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.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;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.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong> 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&#39;re interested in this work.</p>\n<p><strong>Your safety matters to us.</strong> To protect yourself from potential scams, remember that Anthropic is a legitimate company and we will never ask you to pay any fees or provide sensitive information via email or phone.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_221e855f-2b9","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://job-boards.greenhouse.io","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5115935008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"£260,000 - £630,000GBP","x-skills-required":["Python","async/concurrent programming","Trio","machine learning frameworks","PyTorch","TensorFlow","JAX","reinforcement learning techniques","environments","virtualization","sandboxed code execution environments","Kubernetes","distributed systems","high-performance computing","Rust","C++"],"x-skills-preferred":["LLM architectures","training methodologies","reinforcement learning techniques","environments","virtualization","sandboxed code execution environments","Kubernetes","distributed systems","high-performance computing","Rust","C++"],"datePosted":"2026-03-08T13:44:26.776Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, async/concurrent programming, Trio, machine learning frameworks, PyTorch, TensorFlow, JAX, reinforcement learning techniques, environments, virtualization, sandboxed code execution environments, Kubernetes, distributed systems, high-performance computing, Rust, C++, LLM architectures, training methodologies, reinforcement learning techniques, environments, virtualization, sandboxed code execution environments, Kubernetes, distributed systems, high-performance computing, Rust, C++","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":260000,"maxValue":630000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_33601d96-cc8"},"title":"Research Engineer","description":"<p><strong>Research Engineer</strong></p>\n<p>Cursor is building the future of coding. We train frontier coding agents and scale RL on real user data to make them increasingly effective.</p>\n<p><strong>About the role</strong></p>\n<p>We’re looking for Research Engineers to build the training, inference, and data systems behind our frontier coding models. You’ll work directly with researchers to make progress repeatable and iteration fast.</p>\n<p><strong><strong>What you’ll do</strong></strong></p>\n<ul>\n<li>Build our distributed training, inference, and RL infrastructure</li>\n<li>Write libraries to simplify how researchers do large-scale data jobs</li>\n<li>Architect the systems that turn Cursor user data into effective training data</li>\n</ul>\n<p><strong><strong>You may be a fit if</strong></strong></p>\n<ul>\n<li>You have a strong infrastructure/distributed systems background</li>\n<li>You are able to architect and ship end-to-end with high ownership</li>\n<li>You have strong intuitions about how language models work</li>\n<li>You’re excited to learn more about ML</li>\n</ul>\n<p>Name<em> Email</em> ↥ Upload file LinkedIn URL GitHub Profile Please write a short note on a project you&#39;re proud of: Will you now or in the future require visa sponsorship to work in the country where this position is located? * Has someone at Cursor referred you for this role? If so, please include their email here</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_33601d96-cc8","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Cursor","sameAs":"https://cursor.com","logo":"https://logos.yubhub.co/cursor.com.png"},"x-apply-url":"https://cursor.com/careers/software-engineer-ml-research","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["distributed systems","infrastructure","Reinforcement Learning","language models","large-scale data jobs"],"x-skills-preferred":["ML"],"datePosted":"2026-03-08T00:18:43.059Z","jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"distributed systems, infrastructure, Reinforcement Learning, language models, large-scale data jobs, ML"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_213bb1e4-c37"},"title":"Research Scientist","description":"<p><strong>Research Scientist</strong></p>\n<p>Cursor is building the future of coding. We train frontier coding agents and scale RL on real user data to make them increasingly effective.</p>\n<p><strong>About the role</strong></p>\n<p>We’re looking for Research Scientists who can drive effective RL or mid-training research in a small-team setting. You’ll own ambiguous, hard research problems end-to-end: forming hypotheses, designing experiments, building the training/eval/data needed to test them, and pushing results into the next model. You should expect significantly more scope and autonomy than in other research labs.</p>\n<p><strong><strong>What you’ll do</strong></strong></p>\n<ul>\n<li>Improve our understanding of RL, what it takes to handle longer horizon tasks, and train with less compute</li>\n</ul>\n<ul>\n<li>Train graders to improve performance on coding tasks with non-verbal reward</li>\n</ul>\n<ul>\n<li>Improve the quality and difficulty of datapoints we use for training our models</li>\n</ul>\n<ul>\n<li>Realtime RL for coding agents</li>\n</ul>\n<p><strong>You may be a fit if</strong></p>\n<ul>\n<li>You have a deep background in RL and strong machine learning fundamentals</li>\n</ul>\n<ul>\n<li>You’re an excellent programmer and software engineer</li>\n</ul>\n<ul>\n<li>You can handle ambiguous research tasks with little guidance</li>\n</ul>\n<ul>\n<li>You care a lot about data quality, and can dive into the data when appropriate</li>\n</ul>\n<ul>\n<li>You are truth-seeking, aiming to learn more about the science than proving your ideas are correct.</li>\n</ul>\n<p>Name Email ↥ Upload file LinkedIn URL GitHub Profile</p>\n<p>Please write a short note on a project you&#39;re proud of:</p>\n<p>Will you now or in the future require visa sponsorship to work in the country where this position is located?</p>\n<p>Has someone at Cursor referred you for this role? If so, please include their email here</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_213bb1e4-c37","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Cursor","sameAs":"https://cursor.com","logo":"https://logos.yubhub.co/cursor.com.png"},"x-apply-url":"https://cursor.com/careers/research-scientist","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["reinforcement learning","machine learning","programming","software engineering","data quality"],"x-skills-preferred":["RL","ML","Python","Java","C++"],"datePosted":"2026-03-08T00:18:18.922Z","jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"reinforcement learning, machine learning, programming, software engineering, data quality, RL, ML, Python, Java, C++"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_b01a6d01-525"},"title":"Researcher, Synthetic RL","description":"<p><strong>Job Posting</strong></p>\n<p><strong>Researcher, Synthetic RL</strong></p>\n<p><strong>Location</strong></p>\n<p>San Francisco</p>\n<p><strong>Employment Type</strong></p>\n<p>Full time</p>\n<p><strong>Department</strong></p>\n<p>Research</p>\n<p><strong>Compensation</strong></p>\n<ul>\n<li>$295K – $445K • Offers Equity</li>\n</ul>\n<p>The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.</p>\n<ul>\n<li>Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts</li>\n</ul>\n<ul>\n<li>Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)</li>\n</ul>\n<ul>\n<li>401(k) retirement plan with employer match</li>\n</ul>\n<ul>\n<li>Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)</li>\n</ul>\n<ul>\n<li>Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees</li>\n</ul>\n<ul>\n<li>13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)</li>\n</ul>\n<ul>\n<li>Mental health and wellness support</li>\n</ul>\n<ul>\n<li>Employer-paid basic life and disability coverage</li>\n</ul>\n<ul>\n<li>Annual learning and development stipend to fuel your professional growth</li>\n</ul>\n<ul>\n<li>Daily meals in our offices, and meal delivery credits as eligible</li>\n</ul>\n<ul>\n<li>Relocation support for eligible employees</li>\n</ul>\n<ul>\n<li>Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.</li>\n</ul>\n<p>More details about our benefits are available to candidates during the hiring process.</p>\n<p>This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.</p>\n<p><strong>About the Team</strong></p>\n<p>The Synthetic RL team develops reinforcement learning methods that leverage synthetic data, environments, and feedback to train and evaluate frontier AI models. The team explores approaches such as self-play, simulators, and other synthetic evaluations to push model capability, generalization, and alignment beyond what is possible with the current prevailing methodology.</p>\n<p><strong>About the Role</strong></p>\n<p>As a <strong>Research Scientist</strong> on the Synthetic RL team, you will develop novel reinforcement learning techniques that use synthetic environments and feedback to improve large-scale models. You’ll work closely with other researchers to design experiments, analyze learning dynamics, and translate research insights into training approaches used in production systems.</p>\n<p>We’re looking for researchers who enjoy working on open-ended problems, value fast iteration, and want their work to directly shape how frontier models are trained.</p>\n<p>This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.</p>\n<p><strong>In this role, you will:</strong></p>\n<ul>\n<li>Research and develop reinforcement learning algorithms</li>\n</ul>\n<ul>\n<li>Design and run experiments to study training dynamics and model behavior at scale</li>\n</ul>\n<ul>\n<li>Collaborate with engineers and researchers to integrate successful approaches into model training pipelines</li>\n</ul>\n<p><strong>You might thrive in this role if you:</strong></p>\n<ul>\n<li>Have a strong background in reinforcement learning, machine learning research, or related fields</li>\n</ul>\n<ul>\n<li>Have strong engineering and statistical analysis skills</li>\n</ul>\n<ul>\n<li>Enjoy exploring new problem spaces where data, objectives, and evaluation are imperfect or evolving</li>\n</ul>\n<ul>\n<li>Are motivated by seeing research ideas influence real-world AI systems</li>\n</ul>\n<p><strong>About OpenAI</strong></p>\n<p>OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. 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They focus on pushing the boundaries of reinforcement learning research, building next-generation generative models, and deploying them at scale.</p>\n<p><strong>About the Role</strong></p>\n<p>As a Research Engineer/Research Scientist at OpenAI, you will advance the frontier of AI alignment and capabilities through cutting-edge RL methods. Your work will sit at the heart of training intelligent, aligned, and general-purpose agents, including the systems that power various models.</p>\n<p>We’re looking for people who have a background in reinforcement learning research, are able to iterate quickly, and are proficient at coding.</p>\n<p>This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.</p>\n<p><strong>You might thrive in this role if:</strong></p>\n<ul>\n<li>You love being on the cutting edge of RL and language model research.</li>\n</ul>\n<ul>\n<li>You’re a self-starter who takes initiative and ownership of ideas, driving them to completion.</li>\n</ul>\n<ul>\n<li>You value principled approaches, simple experiments in tightly-controlled settings, and reaching trustworthy conclusions which stand the test of time.</li>\n</ul>\n<ul>\n<li>You thrive in a fast-paced, dynamic, and technically complex environment where rapid iteration is key.</li>\n</ul>\n<ul>\n<li>You’re comfortable diving into a large ML codebase to debug and improve it.</li>\n</ul>\n<ul>\n<li>You have a deep understanding of machine learning and machine learning applications.</li>\n</ul>\n<p><strong>About OpenAI</strong></p>\n<p>OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. 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The team partners closely with research and product teams across the company, and conducts research as a final step to prepare for real world deployment to millions of users, ensuring that our models are safe, efficient, and reliable.</p>\n<p><strong>About the Role</strong></p>\n<p>As a Research Engineer / Scientist, you will research and develop improvements to our models. Our team works in research areas combining reinforcement learning and products.</p>\n<p>We&#39;re looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models. An ideal candidate is passionate about product-driven research.</p>\n<p>This role is based in San Francisco, CA. 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The work includes evaluating multi-turn and tool-using systems, designing agent harnesses, and applying reinforcement learning and related methods in production settings. Engineers who succeed in this role bring both a builder’s mindset and the judgment to create reusable systems that others can build on. Many thrive here by operating like founders or founding engineers, taking initiative, moving quickly, and creating structure where none exists.</p>\n<p>This role is based in our San Francisco HQ. 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We support experimentation and production with top researchers, engineers, faculty, and PhD candidates globally that can lead to publications at esteemed venues.</p>\n<p><strong><strong>Teams Hiring For This Role:</strong></strong></p>\n<ul>\n<li><strong>Discovery:</strong> Build recommender and search systems, connecting millions of experiences to users via generative modeling, LLMs, and multimodal content understanding.</li>\n</ul>\n<ul>\n<li><strong>Economy:</strong> Expand the ML backbone powering monetization, marketplace, and payments, solving problems related to fraud, pricing, and personalization</li>\n</ul>\n<ul>\n<li><strong>Ads &amp; Brands:</strong> Evolve the intersection of recommendation systems, ranking, and marketplace/auction theory to optimize sponsored content delivery</li>\n</ul>\n<ul>\n<li><strong>Safety:</strong> Create scalable AI for real-time safety &amp; moderation using LLMs, NLP, and computer vision to safeguard text, voice, and accounts.</li>\n</ul>\n<ul>\n<li><strong>Engine:</strong> Develop innovative technology for our immersive 3D graphics, advancing geometry, physics simulation, motion diffusion models, and scalable distributed systems</li>\n</ul>\n<ul>\n<li><strong>Creator:</strong> Empower creators with generative AI and NLP technologies, building intuitive tools for automated content understanding and real-time localization</li>\n</ul>\n<ul>\n<li><strong>Foundational AI</strong>: Design and train 3D foundational models and optimize large-scale inference systems, enabling intelligent 3D content generation.</li>\n</ul>\n<ul>\n<li><strong>Infrastructure:</strong> Build systems that ensure the reliability and scalability of Roblox’s datacenter, backbone, and edge networks, which support all of our production services.</li>\n</ul>\n<p><strong><strong>You Will</strong></strong></p>\n<ul>\n<li>Prototype and experiment with advanced AI, machine learning, and 3D technologies to solve real-world challenges at scale</li>\n</ul>\n<ul>\n<li>Translate cutting-edge research into production-ready features by working alongside world class engineering and product teams</li>\n</ul>\n<ul>\n<li>Develop and deliver solutions that enhance user experience, safety, and creativity on a global platform</li>\n</ul>\n<p><strong><strong>You Have</strong></strong></p>\n<ul>\n<li>Possessing or pursuing a PhD in computer science, engineering, or a related field, with a thesis aligned to Roblox’s research areas.</li>\n</ul>\n<ul>\n<li>Deep expertise in a relevant technical domain such as deep learning, computer vision, NLP, reinforcement learning, or distributed systems.</li>\n</ul>\n<ul>\n<li>Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) and experience building and optimizing large-scale systems.</li>\n</ul>\n<ul>\n<li>A strong research track record, evidenced by multiple publications and presentations in top-tier, peer-reviewed venues.</li>\n</ul>\n<p>You may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.</p>\n<p>For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual hourly rate could fall outside of this expected range. This pay range is subject to change and may be modified in the future. _Please note that not all benefits shown on this page are applicable to internship opportunities._</p>\n<p>Hourly Pay Range</p>\n<p>$72—$72 USD</p>\n<p>Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_4f57038e-438","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Roblox","sameAs":"https://careers.roblox.com","logo":"https://logos.yubhub.co/careers.roblox.com.png"},"x-apply-url":"https://careers.roblox.com/jobs/7142298","x-work-arrangement":"onsite","x-experience-level":"entry","x-job-type":"internship","x-salary-range":"$72—$72 USD","x-skills-required":["deep learning","computer vision","NLP","reinforcement learning","distributed systems"],"x-skills-preferred":["Python","C++","Go","Java"],"datePosted":"2026-03-06T14:19:39.553Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Mateo, CA"}},"employmentType":"INTERN","occupationalCategory":"Engineering","industry":"Technology","skills":"deep learning, computer vision, NLP, reinforcement learning, distributed systems, Python, C++, Go, Java"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c9a056a8-13c"},"title":"Senior Machine Learning Engineer, Engine Optimization - PhD Early Career","description":"<p><strong>Job Posting</strong></p>\n<p><strong>[2026] Senior Machine Learning Engineer, Engine Optimization - PhD Early Career</strong></p>\n<p>San Mateo, CA, United StatesEarly CareerID: 5626</p>\n<p>Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.</p>\n<p>At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.</p>\n<p>A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.</p>\n<p>Our engine’s resource management and streaming systems are critical to delivering a smooth, stable, and responsive experience for Roblox users across a huge range of devices and network conditions. These systems work together to intelligently allocate compute, memory, bandwidth, and rendering quality while dynamically delivering world content in real time as players move, explore, and interact. The challenges span highly dynamic environments, unpredictable user behavior, and opaque signals from device and OS constraints.</p>\n<p>This role offers a rare opportunity to pioneer the application of machine learning in real-time engine optimization. You will establish the ML framework for predictive resource allocation and content fetching, replacing heuristic-based logic with adaptive, data-driven decision-making. Your work will directly shape stability, visual quality, responsiveness, and content delivery across billions of global play sessions.</p>\n<p><strong>You Will</strong></p>\n<ul>\n<li>Analyze massive-scale engine performance, streaming patterns, and user behavior telemetry to uncover optimization opportunities and guide the long-term ML roadmap.</li>\n<li>Design ML models that infer player and interaction patterns for predictive resource management and content delivery.</li>\n<li>Build adaptive control systems that translate ML outputs into real-time adjustments of fidelity and system decisions, ensuring high-quality experiences without compromising stability or latency.</li>\n<li>Collaborate with core engine and performance engineering teams to integrate ML solutions directly into the critical path of gameplay across multiple platforms.</li>\n<li>Define the architectural strategy for deploying and scaling ML across resource management and streaming components at massive global scale.</li>\n</ul>\n<p><strong>You Have</strong></p>\n<ul>\n<li>Strong expertise in applied ML—such as reinforcement learning for control, predictive modeling (especially time-series and intent inference), trajectory prediction, or real-time optimization.</li>\n<li>Proficiency in C++, Python, Go, Java, or similar languages, with experience deploying ML models in performance-critical systems.</li>\n<li>A solid understanding of systems-level concepts (memory management, threading, OS signals) or a deep interest in learning them.</li>\n<li>A track record of solving complex optimization problems or integrating ML into real-time systems, ideally in gaming, simulation, robotics, or mobile environments</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<p>As a full-time employee at Roblox, you will be eligible for equity compensation and benefits as described on <strong>this page</strong>.</p>\n<p><strong>Salary</strong></p>\n<p>The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future.</p>\n<p>Annual Salary Range</p>\n<p>$195,780—$242,100 USD</p>\n<p><strong>Work Schedule</strong></p>\n<p>Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c9a056a8-13c","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Roblox","sameAs":"https://careers.roblox.com","logo":"https://logos.yubhub.co/careers.roblox.com.png"},"x-apply-url":"https://careers.roblox.com/jobs/7421746","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$195,780—$242,100 USD","x-skills-required":["applied ML","reinforcement learning","predictive modeling","trajectory prediction","real-time optimization","C++","Python","Go","Java","memory management","threading","OS signals"],"x-skills-preferred":["systems-level concepts","performance-critical systems","gaming","simulation","robotics","mobile environments"],"datePosted":"2026-03-06T14:18:36.559Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Mateo, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"applied ML, reinforcement learning, predictive modeling, trajectory prediction, real-time optimization, C++, Python, Go, Java, memory management, threading, OS signals, systems-level concepts, performance-critical systems, gaming, simulation, robotics, mobile environments","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":195780,"maxValue":242100,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_5276e91e-221"},"title":"Senior Machine Learning Engineer, Recommendation Systems - PhD Early Career","description":"<p><strong>[2026] Senior Machine Learning Engineer, Recommendation Systems - PhD Early Career</strong></p>\n<p>San Mateo, CA, United States</p>\n<p>Early Career</p>\n<p>ID: 5471</p>\n<p>Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.</p>\n<p>At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.</p>\n<p>We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.</p>\n<p>A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.</p>\n<p>Recommendation Systems are a key growth lever at Roblox, driving retention, engagement, and monetization for hundreds of millions of users. This role offers the unique opportunity to redefine how users search and discover everything from the most interesting immersive experiences and digital avatars in our Marketplace to personalized advertising. You will solve a diverse range of high-scale ranking, retrieval, and personalization problems across our platform.</p>\n<p>We combine cutting-edge research —including deep learning, generative AI, and reinforcement learning techniques— with large-scale engineering to bridge experimentation and production; you&#39;ll design algorithms that operate at massive scale and shape the next generation of recommender systems for user-generated content.</p>\n<p><strong>Teams Hiring for This Role</strong></p>\n<ul>\n<li><strong>Search:</strong> powers major recommendation surfaces—drives user engagement by redesigning core surfaces and search/homepage ranking</li>\n</ul>\n<ul>\n<li><strong>Notifications:</strong> owns the distributed systems and ML platform that transform billions of Roblox signals into high‑value notifications for hundreds of millions of players.</li>\n</ul>\n<ul>\n<li><strong>Economy:</strong> builds the ML backbone for marketplace, monetization, and commerce (including fraud, pricing, and bundling)</li>\n</ul>\n<ul>\n<li><strong>Ads &amp; Brands:</strong> focuses on ranking, retrieval, and marketplace/auction theory to optimize sponsored content delivery.</li>\n</ul>\n<ul>\n<li><strong>Safety, Alt Defense:</strong> architects a massive-scale detection engine that identifies recidivist bad actors across billions of accounts to ensure the long-term integrity of the Roblox community.</li>\n</ul>\n<p><strong>You Will</strong></p>\n<ul>\n<li>Design and implement large-scale recommendation systems that power discovery across Roblox’s surfaces — experiences, avatars, and creator content.</li>\n</ul>\n<ul>\n<li>Develop deep learning models for ranking, retrieval, and personalization using approaches in multimodal models, LLMs, and generative AI.</li>\n</ul>\n<ul>\n<li>Collaborate with applied researchers, engineers, and product teams to advance experimentation and accelerate innovation.</li>\n</ul>\n<ul>\n<li>Translate research into production systems that impact hundreds of millions of daily active users.</li>\n</ul>\n<ul>\n<li>Work backward from user and product needs to deliver ML solutions that drive engagement, retention, and ecosystem growth.</li>\n</ul>\n<p><strong>You Have</strong></p>\n<ul>\n<li>Possessing or pursuing a PhD in computer science, engineering, or a related field, with a thesis aligned to Roblox’s research areas.</li>\n</ul>\n<ul>\n<li>Expertise in one or more areas: recommender systems, search systems, information retrieval, or generative models (e.g., LLMs, VLMs, VLAs)</li>\n</ul>\n<ul>\n<li>Ability to design and architect systems for efficient personalization and user interest modeling using advanced attention mechanisms (e.g., sparse/linear attention).</li>\n</ul>\n<ul>\n<li>A strong research track record, evidenced by multiple publications and presentations in top-tier, peer-reviewed venues (e.g., SIGIR, KDD, RecSys, ICLR, ICML, NeurIPS)</li>\n</ul>\n<ul>\n<li>Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) and experience building and optimizing large-scale systems.</li>\n</ul>\n<p>You may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.</p>\n<p>As you apply, you can find more information about our process by signing up for Speak\\_. You&#39;ll gain access to our practice assessment, comprehensive guides, FAQs, and modules designed to help you ace the hiring process.</p>\n<p>For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on <strong>this page</strong>.</p>\n<p>Annual Salary Range</p>\n<p>$195,780—$242,100 USD</p>\n<p>Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_5276e91e-221","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Roblox","sameAs":"https://careers.roblox.com","logo":"https://logos.yubhub.co/careers.roblox.com.png"},"x-apply-url":"https://careers.roblox.com/jobs/7350081","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$195,780—$242,100 USD","x-skills-required":["recommender systems","search systems","information retrieval","generative models","deep learning","generative AI","reinforcement learning","multimodal models","LLMs","VLMs","VLAs","Python","C++","Go","Java"],"x-skills-preferred":["sparse/linear attention","top-tier, peer-reviewed venues","SIGIR","KDD","RecSys","ICLR","ICML","NeurIPS"],"datePosted":"2026-03-06T14:17:16.772Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Mateo, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"recommender systems, search systems, information retrieval, generative models, deep learning, generative AI, reinforcement learning, multimodal models, LLMs, VLMs, VLAs, Python, C++, Go, Java, sparse/linear attention, top-tier, peer-reviewed venues, SIGIR, KDD, RecSys, ICLR, ICML, NeurIPS","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":195780,"maxValue":242100,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d8710a49-05d"},"title":"Principal Applied Scientist","description":"<p><strong>Summary</strong></p>\n<p>Microsoft AI are looking for a talented Principal Applied Scientist at their New York office. This role sits at the heart of advanced AI at web scale, shaping the future of key systems within Microsoft Ads, Copilot, and beyond. You&#39;ll work directly with leadership to deliver scalable solutions that enhance ad relevance and optimize user and advertiser experiences.</p>\n<p><strong>About the Role</strong></p>\n<p>In this role, the Principal Applied Scientist will design and implement state-of-the-art machine learning models and algorithms that power key systems within Microsoft Ads, Copilot, and beyond. The work will directly impact millions of users and advertisers by delivering scalable solutions that enhance ad relevance and optimize user and advertiser experiences.</p>\n<p><strong>Accountabilities</strong></p>\n<ul>\n<li>Develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot.</li>\n<li>Design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation and ad relevance.</li>\n</ul>\n<p><strong>The Candidate we&#39;re looking for</strong></p>\n<p><strong>Experience:</strong></p>\n<ul>\n<li>Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.</li>\n</ul>\n<p><strong>Technical skills:</strong></p>\n<ul>\n<li>Proven experience in programming and data analysis skills.</li>\n<li>Proven expertise in the areas of Generative AI, deep learning, Reinforcement learning, transformers or LLM.</li>\n</ul>\n<p><strong>Personal attributes:</strong></p>\n<ul>\n<li>6+ years of experience in developing and deploying large-scale machine learning models.</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Competitive salary</li>\n<li>Comprehensive benefits package</li>\n<li>Opportunities for professional growth and development</li>\n<li>Collaborative and dynamic work environment</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_d8710a49-05d","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft AI","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/principal-applied-scientist-17/","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$139,900 – $274,800 per year","x-skills-required":["machine learning","statistics","econometrics","computer science","electrical or computer engineering"],"x-skills-preferred":["generative AI","deep learning","reinforcement learning","transformers","LLM"],"datePosted":"2026-03-06T07:33:03.226Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"machine learning, statistics, econometrics, computer science, electrical or computer engineering, generative AI, deep learning, reinforcement learning, transformers, LLM","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":139900,"maxValue":274800,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_066d45bf-f6e"},"title":"Principal Applied Scientist","description":"<p><strong>Summary</strong></p>\n<p>Microsoft AI are looking for a talented Principal Applied Scientist at their Mountain View office. 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This role sits at the heart of advanced AI at web scale, shaping the future of key systems within Microsoft Ads, Copilot, and beyond. You&#39;ll design and implement state-of-the-art machine learning models and algorithms that power scalable solutions that enhance ad relevance and optimize user and advertiser experiences.</p>\n<p><strong>About the Role</strong></p>\n<p>In this role, you will develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot. You will design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation and ad relevance. You will drive experimentation through A/B testing and offline validation to evaluate model performance and refine user behavior predictions. You will build robust data pipelines and frameworks for handling large-scale, high-dimensional datasets to support advanced AI applications. You will stay at the forefront of AI research, incorporating the latest advancements to drive innovation and impact across Microsoft platforms.</p>\n<p><strong>Accountabilities</strong></p>\n<ul>\n<li>Develop and deploy cutting-edge machine learning models, including transformers, generative AI, and reinforcement learning, to optimize user interactions and ad relevance across Microsoft Ads and Copilot.</li>\n<li>Design scalable algorithms for online and offline systems, delivering innovative solutions for ads selection, ad generation and ad relevance.</li>\n<li>Drive experimentation through A/B testing and offline validation to evaluate model performance and refine user behavior predictions.</li>\n<li>Build robust data pipelines and frameworks for handling large-scale, high-dimensional datasets to support advanced AI applications.</li>\n</ul>\n<p><strong>The Candidate we&#39;re looking for</strong></p>\n<p><strong>Experience:</strong></p>\n<ul>\n<li>Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.</li>\n</ul>\n<p><strong>Technical skills:</strong></p>\n<ul>\n<li>Proven experience in programming and data analysis skills.</li>\n<li>Proven expertise in the areas of Generative AI, deep learning, Reinforcement learning, transformers or LLM.</li>\n</ul>\n<p><strong>Personal attributes:</strong></p>\n<ul>\n<li>Strong problem-solving skills and ability to work independently.</li>\n<li>Excellent communication and collaboration skills.</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Competitive salary.</li>\n<li>Comprehensive benefits package.</li>\n<li>Opportunities for professional growth and development.</li>\n<li>Collaborative and dynamic work environment.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_9c3ddd52-017","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft AI","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/principal-applied-scientist-15/","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$139,900 - $274,800 per year","x-skills-required":["machine learning","deep learning","reinforcement learning","transformers","LLM","programming","data analysis"],"x-skills-preferred":["Generative AI","A/B testing","offline validation","data pipelines","frameworks"],"datePosted":"2026-03-06T07:31:51.153Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Redmond"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"machine learning, deep learning, reinforcement learning, transformers, LLM, programming, data analysis, Generative AI, A/B testing, offline validation, data pipelines, frameworks","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":139900,"maxValue":274800,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_cfee4a87-9c7"},"title":"Member of Technical Staff, Multimodal Infrastructure","description":"<p><strong>Summary</strong></p>\n<p>Microsoft AI are looking for a talented Member of Technical Staff, Multimodal Infrastructure to help build the next wave of capabilities of our personalized AI assistant, Copilot. 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As a contributing member of the core group of engineers, you would also bring to the table best practices driving architectural changes and influence roadmap of relevant software and hardware components.</p>\n<p><strong>Accountabilities</strong></p>\n<ul>\n<li>Design, develop and maintain large-scale multimodal data processing pipelines.</li>\n<li>Design, develop and maintain large-scale multimodal model pretraining and post-training frameworks.</li>\n<li>Design, develop and maintain large-scale multimodal model inference and serving frameworks.</li>\n</ul>\n<p><strong>The Candidate we&#39;re looking for</strong></p>\n<p><strong>Experience:</strong></p>\n<ul>\n<li>Bachelor&#39;s Degree in Computer Science, or related technical discipline AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.</li>\n</ul>\n<p><strong>Technical skills:</strong></p>\n<ul>\n<li>Strong proficiency in distributed data processing infra (resource utilization management, fault tolerance, ray &amp; spark) and CPU/GPU batch processing optimizations.</li>\n<li>Experience with state-of-art model inference and serving frameworks.</li>\n<li>Experience with image/video/audio data processing.</li>\n<li>Experience with common data formats for efficient I/O.</li>\n</ul>\n<p><strong>Personal attributes:</strong></p>\n<ul>\n<li>Enjoy working in a fast-paced, design-driven, product development cycle.</li>\n<li>Embody our Culture and Values.</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.</li>\n<li>Comprehensive health and wellbeing benefits.</li>\n<li>Professional development opportunities.</li>\n<li>Financial benefits (bonus, equity, pension, etc.).</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_cfee4a87-9c7","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft AI","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/member-of-technical-staff-multimodal-infrastructure-mai-superintelligence-team-2/","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["C","C++","C#","Java","JavaScript","Python","Distributed data processing infra","CPU/GPU batch processing optimizations","State-of-art model inference and serving frameworks","Image/video/audio data processing","Common data formats for efficient I/O"],"x-skills-preferred":["Deep learning frameworks","Auto-regressive and diffusion transformer models","Distributed training techniques","Image/video generation and editing","Efficient architectures","Efficient model design","Reinforcement learning training methods"],"datePosted":"2026-03-06T07:31:05.608Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Redmond"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"C, C++, C#, Java, JavaScript, Python, Distributed data processing infra, CPU/GPU batch processing optimizations, State-of-art model inference and serving frameworks, Image/video/audio data processing, Common data formats for efficient I/O, Deep learning frameworks, Auto-regressive and diffusion transformer models, Distributed training techniques, Image/video generation and editing, Efficient architectures, Efficient model design, Reinforcement learning training methods"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_ba2d1729-7b0"},"title":"Member of Technical Staff - Machine Learning (AI Team)","description":"<p><strong>Summary</strong></p>\n<p>Microsoft are looking for a talented Member of Technical Staff - Machine Learning (AI Team) at their New York office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that&#39;s revolutionising AI technology. You&#39;ll work directly with leadership to shape the company&#39;s direction in the AI market.</p>\n<p><strong>About the Role</strong></p>\n<p>As a Member of Technical Staff - Machine Learning (AI Team), you will work to create LLM models for general purpose capabilities and for products. You may be responsible for developing new methods to train core LLM capabilities (including agentive), collecting data, evaluating LLMs, creating data flywheels, tooling for LLM training/evals, writing production quality code, and creating new user-facing features. You should be comfortable creating Reinforcement Learning data, fine tuning, or training classifiers or engineering prompts to support Microsoft products and the Cloud API.</p>\n<p><strong>Accountabilities</strong></p>\n<ul>\n<li>Leverage subject matter expertise to improve model quality for interactive and agentive experiences.</li>\n<li>Oversee data acquisition or generation efforts, ensuring that the data meets the model needs.</li>\n</ul>\n<p><strong>The Candidate we&#39;re looking for</strong></p>\n<p><strong>Experience:</strong></p>\n<ul>\n<li>Bachelor’s Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.</li>\n</ul>\n<p><strong>Technical skills:</strong></p>\n<ul>\n<li>Experience prompting, evaluating, and working with large language models.</li>\n<li>Experience writing production-quality Python code.</li>\n</ul>\n<p><strong>Personal attributes:</strong></p>\n<ul>\n<li>Demonstrated engineering experience or research experience (e.g. creating or leading the creation of a feature in a different company, complex graduate work, research papers, or other experience).</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Software Engineering IC4 – The typical base pay range for this role across the U.S. is USD $119,800 – $234,700 per year.</li>\n<li>Certain roles may be eligible for benefits and other compensation.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_ba2d1729-7b0","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/member-of-technical-staff-machine-learning-ai-team-9/","x-work-arrangement":"onsite","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"USD $119,800 – $234,700 per year","x-skills-required":["Machine Learning","Python","C","C++","C#"],"x-skills-preferred":["Reinforcement Learning","Data Science"],"datePosted":"2026-03-06T07:30:55.485Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, Python, C, C++, C#, Reinforcement Learning, Data Science","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":119800,"maxValue":234700,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_86b2aaed-25f"},"title":"Member of Technical Staff - Machine Learning (AI Team)","description":"<p><strong>Summary</strong></p>\n<p>Microsoft are looking for a talented Member of Technical Staff - Machine Learning (AI Team) at their New York office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that&#39;s revolutionising AI technology. You&#39;ll work directly with leadership to shape the company&#39;s direction in the AI market.</p>\n<p><strong>About the Role</strong></p>\n<p>As a Member of Technical Staff - Machine Learning, you will work to create LLM models for general purpose capabilities and for products. You may be responsible for developing new methods to train core LLM capabilities (including agentive), collecting data, evaluating LLMs, creating data flywheels, tooling for LLM training/evals, writing production quality code, and creating new user-facing features. You should be comfortable creating Reinforcement Learning data, fine tuning, or training classifiers or engineering prompts to create SOTA foundation models and support Microsoft products and the Cloud API.</p>\n<p><strong>Accountabilities</strong></p>\n<ul>\n<li>Own and pursue a research agenda to improve model capability and performance for agentive application.</li>\n<li>Collaborate closely with the other research and product teams, from pretraining to model hosting to unlock new model capabilities.</li>\n<li>Build robust evaluations for tracking modeling improvements.</li>\n<li>Design, implement, test, and debug code across our research stack.</li>\n</ul>\n<p><strong>The Candidate we&#39;re looking for</strong></p>\n<p><strong>Experience:</strong></p>\n<ul>\n<li>Bachelor’s Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.</li>\n</ul>\n<p><strong>Technical skills:</strong></p>\n<ul>\n<li>Experience prompting, evaluating, and working with large language models.</li>\n<li>Experience writing production-quality Python code.</li>\n</ul>\n<p><strong>Personal attributes:</strong></p>\n<ul>\n<li>Demonstrated engineering experience or research experience (e.g. creating or leading the creation of a feature in a different company, complex graduate work, research papers, or other experience).</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Software Engineering IC6 – The typical base pay range for this role across the U.S. is USD $163,000 – $296,400 per year.</li>\n<li>Certain roles may be eligible for benefits and other compensation.</li>\n<li>Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_86b2aaed-25f","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/member-of-technical-staff-machine-learning-ai-team-3/","x-work-arrangement":"onsite","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"USD $163,000 – $296,400 per year","x-skills-required":["Machine Learning","Python","C","C++","C#"],"x-skills-preferred":["Reinforcement Learning","Large Language Models","Production-quality Code"],"datePosted":"2026-03-06T07:30:42.823Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, Python, C, C++, C#, Reinforcement Learning, Large Language Models, Production-quality Code","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":163000,"maxValue":296400,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_a82f064b-623"},"title":"Member of Technical Staff, Multimodal Infrastructure","description":"<p><strong>Summary</strong></p>\n<p>Microsoft AI are looking for a talented Member of Technical Staff, Multimodal Infrastructure to help build the next wave of capabilities of our personalized AI assistant, Copilot. We’re looking for someone who will bring an abundance of positive energy, empathy, and kindness to the team every day, in addition to being highly effective.</p>\n<p><strong>About the Role</strong></p>\n<p>As a Member of Technical Staff, Multimodal Infrastructure, you will be responsible for designing, developing, and maintaining large-scale multimodal data processing pipelines, model pretraining and post-training frameworks, and model inference and serving frameworks. You will work closely with research scientists and product engineers to solve infra-related problems and find a path to get things done despite roadblocks to get your work into the hands of users quickly and iteratively.</p>\n<p><strong>Accountabilities</strong></p>\n<ul>\n<li>Design, develop, and maintain large-scale multimodal data processing pipelines.</li>\n<li>Design, develop, and maintain large-scale multimodal model pretraining and post-training frameworks.</li>\n<li>Design, develop, and maintain large-scale multimodal model inference and serving frameworks.</li>\n</ul>\n<p><strong>The Candidate we&#39;re looking for</strong></p>\n<p><strong>Experience:</strong></p>\n<ul>\n<li>Bachelor’s Degree in Computer Science, or related technical discipline AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.</li>\n</ul>\n<p><strong>Technical skills:</strong></p>\n<ul>\n<li>Strong proficiency in distributed data processing infra (resource utilization management, fault tolerance, ray &amp; spark) and CPU/GPU batch processing optimizations.</li>\n<li>Experience with state-of-art model inference and serving frameworks.</li>\n<li>Experience with image/video/audio data processing.</li>\n<li>Experience with common data formats for efficient I/O.</li>\n</ul>\n<p><strong>Personal attributes:</strong></p>\n<ul>\n<li>Enjoy working in a fast-paced, design-driven, product development cycle.</li>\n<li>Embody our Culture and Values.</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.</li>\n<li>Comprehensive health and wellbeing benefits.</li>\n<li>Professional development opportunities.</li>\n<li>Financial benefits (bonus, equity, pension, etc.).</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_a82f064b-623","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft AI","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/member-of-technical-staff-multimodal-infrastructure-mai-superintelligence-team/","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["C","C++","C#","Java","JavaScript","Python","Distributed data processing infra","CPU/GPU batch processing optimizations","State-of-art model inference and serving frameworks","Image/video/audio data processing","Common data formats for efficient I/O"],"x-skills-preferred":["Auto-regressive and diffusion transformer models","Distributed training techniques","Image/video generation and editing","Efficient architectures","Efficient model design","Reinforcement learning training methods"],"datePosted":"2026-03-06T07:30:19.312Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"C, C++, C#, Java, JavaScript, Python, Distributed data processing infra, CPU/GPU batch processing optimizations, State-of-art model inference and serving frameworks, Image/video/audio data processing, Common data formats for efficient I/O, Auto-regressive and diffusion transformer models, Distributed training techniques, Image/video generation and editing, Efficient architectures, Efficient model design, Reinforcement learning training methods"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_146f30de-73a"},"title":"Principal Applied Scientist","description":"<p><strong>Summary</strong></p>\n<p>Microsoft AI are looking for a talented Principal Applied Scientist at their Beijing office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that&#39;s revolutionising the field of AI. You&#39;ll work directly with leadership to shape the company&#39;s direction in the AI market.</p>\n<p><strong>About the Role</strong></p>\n<p>We are seeking an Applied Scientist / AI Architect with strong hands-on experience in building and optimizing large language models (LLMs), agentic AI systems, and end-to-end model training workflows. This role is ideal for scientists with a solid applied background who can translate state-of-the-art research into real-world impact. A research-oriented mindset with publications in top AI/ML venues is highly preferred but not strictly required.</p>\n<p><strong>Accountabilities</strong></p>\n<ul>\n<li>Design and implement advanced LLM-based architectures and agentic systems for real-world product scenarios.</li>\n<li>Translate research breakthroughs into production-ready algorithms, contributing to core capabilities such as reasoning, planning, long-term memory, code-gen based design.</li>\n<li>Monitor and improve model performance post-deployment through data-driven iteration and error analysis.</li>\n<li>Collaborate across teams to deliver robust, scalable models aligned with product objectives and user value.</li>\n<li>Contribute to the organization’s scientific direction by identifying research opportunities that drive long-term differentiation.</li>\n</ul>\n<p><strong>The Candidate we&#39;re looking for</strong></p>\n<p><strong>Experience:</strong></p>\n<ul>\n<li>M.S. or Ph.D. in Computer Science, Machine Learning, or a related field, or equivalent practical experience.</li>\n<li>5+ years of experience in applied machine learning, with a focus on LLMs, agent systems, or reinforcement learning.</li>\n</ul>\n<p><strong>Technical skills:</strong></p>\n<ul>\n<li>Strong hands-on experience with prompt engineering, context engineering, retrieval-augmented generation (RAG), tool use, planning agents, and long-context modeling, etc.</li>\n<li>Familiarity with model training pipelines using PyTorch, TensorFlow, JAX, or similar frameworks, evaluation strategies, and model deployment best practices.</li>\n</ul>\n<p><strong>Personal attributes:</strong></p>\n<ul>\n<li>Strong coding and debugging skills, and comfort working in cross-functional, agile environments.</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Competitive salary and benefits package.</li>\n<li>Opportunities for professional growth and development.</li>\n<li>Collaborative and dynamic work environment.</li>\n<li>Access to cutting-edge technology and resources.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_146f30de-73a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft AI","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/principal-applied-scientist-12/","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"Competitive salary and benefits package","x-skills-required":["Applied machine learning","LLMs","Agent systems","Reinforcement learning","PyTorch","TensorFlow","JAX"],"x-skills-preferred":["Prompt engineering","Context engineering","RAG","Tool use","Planning agents","Long-context modeling"],"datePosted":"2026-03-06T07:30:14.499Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Beijing, China"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Applied machine learning, LLMs, Agent systems, Reinforcement learning, PyTorch, TensorFlow, JAX, Prompt engineering, Context engineering, RAG, Tool use, Planning agents, Long-context modeling"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_ec07c55d-132"},"title":"Member of Technical Staff - Machine Learning (AI Team)","description":"<p><strong>Summary</strong></p>\n<p>Microsoft are looking for a talented Member of Technical Staff - Machine Learning (AI Team) at their Redmond office. 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