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You will apply leading GenAI frameworks and LLM platforms , including Anthropic, OpenAI, LangChain, LangGraph, DSPy, and vector databases,while operating across the full Agentic SDLC.</p>\n<p>P&amp;C Insurance knowledge and experience is a significant plus. 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If so, an exciting opportunity awaits you at AstraZeneca!</p>\n<p>We are looking for Associate Principal AI Data Scientists eager to utilise their expertise in these advanced technologies to revolutionise our drug development processes. In the Pharmaceutical Technology and Development (PT&amp;D) department, you will be a key player in transforming molecules into groundbreaking medical treatments.</p>\n<p>Your role involves contributing data science expertise into cross-functional global pharmaceutical development projects in support of transforming the way we deliver medicines to patients. You&#39;ll play a pivotal role in shaping our AI strategy and driving the co-development of sophisticated HITL multi-agent systems.</p>\n<p>We are hiring two candidates for this position, and the roles will be based at our dynamic site in Durham (USA).</p>\n<p><strong>Work Modality: Hybrid (3 days office and 2 remote)</strong></p>\n<p>Accountabilities:</p>\n<ul>\n<li>Drive innovation in agentic AI, multi-agent systems, and digital twins, exploring new methodologies and applications.</li>\n</ul>\n<ul>\n<li>Design, implement, and optimise algorithms for autonomous decision-making, coordination, and policy learning among agents and digital twins using techniques like Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), and multi-agent reinforcement learning (MARL).</li>\n</ul>\n<ul>\n<li>Evaluate agent performance in the context of decision making, collaboration, competition, uncertainty.</li>\n</ul>\n<ul>\n<li>Collaborate with cross-functional teams ensuring knowledge transfer to IT engineering teams for IT solution builds and deployment.</li>\n</ul>\n<ul>\n<li>Keep pace with industry advancements by reviewing academic papers and attending conferences. 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If you meet these criteria, please highlight merged GitHub PRs in your application.</li>\n</ul>\n<ul>\n<li>Strong publication record in the field of AI.</li>\n</ul>\n<ul>\n<li>Experience designing multi-agent systems in the pharmaceutical sector.</li>\n</ul>\n<ul>\n<li>Experience delivering machine learning projects with applications in pharmaceutical development, chemical engineering or chemistry.</li>\n</ul>\n<ul>\n<li>Experience with one or more of the following applied machine learning domains such as transfer learning, federated learning, few/zero shot learning, meta learning, explainable AI.</li>\n</ul>\n<p>When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That&#39;s why we work, on average, a minimum of three days per week from the office. But that doesn&#39;t mean we&#39;re not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.</p>\n<p>AstraZeneca is a place where change is embraced, and new solutions are trialed with patients and business in mind. Here, technology is a key lever for delivering medicines quickly, affordably, and sustainably. Our diverse workforce is united by curiosity, sharing learnings to scale fast. Be part of a digitally-enabled environment that impacts all parts of the business,from robotic process automation to machine learning for quality batches,while contributing to society and the planet.</p>\n<p>Ready to make a difference? Apply now to join our team!</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_3409e243-d5f","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Data Science & AI","sameAs":"https://astrazeneca.eightfold.ai","logo":"https://logos.yubhub.co/astrazeneca.eightfold.ai.png"},"x-apply-url":"https://astrazeneca.eightfold.ai/careers/job/563877689945901","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","R","Deep Learning","Machine Learning","TensorFlow","PyTorch","GenAI","Reinforcement Learning","OpenAI Gym","Ray RLlib","Stable Baselines"],"x-skills-preferred":["Contributions to open-source projects","Strong publication record in the field of AI","Experience designing multi-agent systems in the pharmaceutical sector","Experience delivering machine learning projects with applications in pharmaceutical development, chemical engineering or chemistry","Experience with one or more of the following applied machine learning domains such as transfer learning, federated learning, few/zero shot learning, meta learning, explainable AI"],"datePosted":"2026-04-24T14:19:14.699Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Durham, North Carolina, United States of America"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Healthcare","skills":"Python, R, Deep Learning, Machine Learning, TensorFlow, PyTorch, GenAI, Reinforcement Learning, OpenAI Gym, Ray RLlib, Stable Baselines, Contributions to open-source projects, Strong publication record in the field of AI, Experience designing multi-agent systems in the pharmaceutical sector, Experience delivering machine learning projects with applications in pharmaceutical development, chemical engineering or chemistry, Experience with one or more of the following applied machine learning domains such as transfer learning, federated learning, few/zero shot learning, meta learning, explainable AI"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_5c7e3c9c-ece"},"title":"AI Product Engineer - Agentic AI Platforms (Financial Services)","description":"<p>Capgemini is at the forefront of Generative AI innovation, helping Financial Services clients industrialize GenAI and Agentic AI platforms at enterprise scale.</p>\n<p>We are seeking an experienced and innovative AI Product Engineer – Agentic Platforms to join our Financial Services Artificial Intelligence &amp; Business Lines (FS-ABL) practice. This role is ideal for a consulting technologist with deep expertise in modern GenAI tooling, agentic system design, and enterprise SDLC, who can partner directly with clients to envision, design, develop, and deploy Agentic AI platforms in regulated environments.</p>\n<p>In this role, you will work at the intersection of client advisory, AI product engineering, and delivery execution, helping banks, insurers, and capital markets firms transition from GenAI pilots to production-grade, governed, multi-agent systems. You will apply leading GenAI frameworks and LLM platforms , including Anthropic, OpenAI, LangChain, LangGraph, DSPy, and vector databases,while operating across the full Agentic SDLC.</p>\n<p>P&amp;C Insurance knowledge and experience is a significant plus. 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Business Lines (FS-ABL) practice. This role is ideal for a consulting technologist with deep expertise in modern GenAI tooling, agentic system design, and enterprise SDLC, who can partner directly with clients to envision, design, develop, and deploy Agentic AI platforms in regulated environments.</p>\n<p>In this role, you will work at the intersection of client advisory, AI product engineering, and delivery execution, helping banks, insurers, and capital markets firms transition from GenAI pilots to production-grade, governed, multi-agent systems. You will apply leading GenAI frameworks and LLM platforms , including Anthropic, OpenAI, LangChain, LangGraph, DSPy, and vector databases,while operating across the full Agentic SDLC.</p>\n<p>P&amp;C Insurance knowledge and experience is a significant plus. 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Engineering Delivery</p>\n<p>Own delivery across the full Software Development Lifecycle (SDLC), extending it into a formal Agentic SDLC, including:</p>\n<p>Agent design specifications and behavior contracts</p>\n<p>Prompt, policy, and tool versioning</p>\n<p>Simulation environments and offline evaluation</p>\n<p>Automated testing of agent flows and guardrails</p>\n<p>Controlled rollout, telemetry-driven optimization, and continuous learning</p>\n<p>Build production-grade AI services primarily using Python, integrating:</p>\n<p>LLM providers such as Anthropic (Claude), OpenAI, and open-source models</p>\n<p>Retrieval-Augmented Generation (RAG) using vector databases (e.g., Pinecone, FAISS, Milvus, Weaviate)</p>\n<p>Implement CI/CD pipelines for agent code, prompts, and policies.</p>\n<p>Integrate GenAI agents with client systems via APIs, workflow engines, event streams, and data platforms.</p>\n<p>Observability, Evaluation &amp; Optimization</p>\n<p>Implement agent observability including tracing, decision logging, tool usage, and failure analysis.</p>\n<p>Apply evaluation frameworks for hallucination detection, consistency checks, and fitness scoring.</p>\n<p>Design feedback loops incorporating human-in-the-loop review and reinforcement.</p>\n<p>Monitor cost, latency, throughput, and behavioral drift across deployed agents.</p>\n<p>Governance, Risk &amp; Financial Services Compliance</p>\n<p>Design Agentic AI platforms aligned with Financial Services regulatory expectations, including:</p>\n<p>Auditability and traceability of agent decisions</p>\n<p>Model and prompt explainability</p>\n<p>Data privacy and security controls</p>\n<p>Resilience and fail-safe mechanisms</p>\n<p>Embed guardrails and policies addressing hallucination risk, bias, unauthorized actions, and escalation failures.</p>\n<p>Produce documentation supporting risk, compliance, internal audit, and regulator engagement.</p>\n<p>Team Leadership &amp; Firm Contribution</p>\n<p>Provide technical leadership and mentorship to consulting delivery teams.</p>\n<p>Contribute to internal GenAI accelerators, agent frameworks, and reusable assets.</p>\n<p>Support RFPs, proposals, and client solution designs with credible GenAI and agentic architectures.</p>\n<p>Participate in thought leadership on Agentic SDLC, GenAI engineering, and responsible autonomy.</p>\n<p>Benefits</p>\n<p>This position comes with competitive compensation and benefits package:</p>\n<p>Competitive salary and performance-based bonuses</p>\n<p>Comprehensive benefits package</p>\n<p>Career development and training opportunities</p>\n<p>Flexible work arrangements (remote and/or office-based)</p>\n<p>Dynamic and inclusive work culture within a globally known group</p>\n<p>Private Health Insurance</p>\n<p>Retirement Benefits</p>\n<p>Paid Time Off</p>\n<p>Training &amp; 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most important challenges? We are growing and are looking for people to join our team. You&#39;ll be part of an entrepreneurial, high-growth environment of 300,000 employees. Our dynamic organization allows you to work across functional business pillars, contributing your ideas, experiences, diverse thinking, and a strong mindset.</p>\n<p>We are looking for technically hands-on professionals to design and deliver client-centric intelligent systems and support business growth through strategic pre-sales and solutioning initiatives. As part of our growing Enterprise AI consulting practice, we deliver real-world business value through the convergence of AI agents, machine learning, and modern enterprise architecture.</p>\n<p>Key Responsibilities: Design and implement AI agents using LangChain, CrewAI, or AutoGen for enterprise-grade use cases. Develop modular code for task decomposition, memory handling, and tool integration with APIs. Collaborate with AI strategists and architects on project design and MVP delivery. Conduct testing and fine-tuning of agent behavior using LLM APIs and embeddings. Participate in internal code reviews, documentation, and reusable framework building. Support pre-sales demos and client innovation sessions with hands-on prototypes.</p>\n<p>Requirements: Bachelor&#39;s or Master&#39;s degree in Computer Science, AI, or related field. PhD preferred for architect-level roles. 3+ years in AI/ML, with recent experience in agentic systems and hands-on development of LLM-based applications. Strong experience with Python and orchestration libraries such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, or similar. Deep knowledge of LLMs (GPT, Claude, LLaMA, Mistral, etc.), prompt engineering, agent memory, tool calling, and autonomous task execution. Experience with RFP/RFI support, and proposal creation in a consulting or enterprise services environment. Understanding of enterprise solutioning with cloud platforms (AWS, Azure, GCP), API integration, and data security best practices. Exceptional communication and consulting skills, with the ability to present solutions to both technical and non-technical stakeholders.</p>\n<p>Preferred Skills: Strong proficiency in programming languages such as Python, Java, Spring, Maven, JSON. Object Oriented Programming Hands-on exposure to cognitive architectures, planning-based agents, or reinforcement learning in real-world deployments. Experience integrating AI agents into enterprise apps like Salesforce, ServiceNow, SAP, or custom apps via APIs. Understanding of AI observability, performance monitoring, and ethical guidelines in GenAI systems.</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_5b50c2ef-97b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Infosys Consulting - Europe","sameAs":"https://www.infosys.com/","logo":"https://logos.yubhub.co/infosys.com.png"},"x-apply-url":"https://jobs.workable.com/view/xpARkFa3XjTnbyVRmoHpEd/remote-genai-engineer-in-poland-at-infosys-consulting---europe","x-work-arrangement":"remote","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","LangChain","CrewAI","AutoGen","LLM APIs","embeddings","API integration","data security best practices","RFP/RFI support","proposal creation"],"x-skills-preferred":["Java","Spring","Maven","JSON","Object Oriented Programming","cognitive architectures","planning-based agents","reinforcement learning","AI observability","performance monitoring","ethical guidelines"],"datePosted":"2026-04-24T14:11:56.124Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Poland"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, LangChain, CrewAI, AutoGen, LLM APIs, embeddings, API integration, data security best practices, RFP/RFI support, proposal creation, Java, Spring, Maven, JSON, Object Oriented Programming, cognitive architectures, planning-based agents, reinforcement learning, AI observability, performance monitoring, ethical guidelines"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_8073098e-063"},"title":"Agentic AI Architect","description":"<p>Do you want to boost your career and collaborate with expert, talented colleagues to solve and deliver against our clients&#39; most important challenges? We are growing and are looking for people to join our team. You&#39;ll be part of an entrepreneurial, high-growth environment of 300,000 employees. Our dynamic organization allows you to work across functional business pillars, contributing your ideas, experiences, diverse thinking, and a strong mindset. Are you ready?</p>\n<p>Job Overview:</p>\n<p>Infosys Consulting is at the forefront of applied AI innovation, delivering real-world business value through the convergence of AI agents, machine learning, and modern enterprise architecture. 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PhD preferred for architect-level roles.</li>\n<li>8+ years of experience in AI/ML, including 5+ years as a Solution Architect and 4+ years of hands-on development with LLMs and autonomous AI agents</li>\n<li>Strong experience with Python and orchestration libraries such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, or similar.</li>\n<li>Deep knowledge of LLMs (GPT, Claude, LLaMA, Mistral, etc.), prompt engineering, agent memory, tool calling, and autonomous task execution.</li>\n<li>Experience with pre-sales, RFP/RFI support, and proposal creation in a consulting or enterprise services environment.</li>\n<li>Understanding of enterprise solutioning with cloud platforms (AWS, Azure, GCP), API integration, and data security best practices.</li>\n<li>Exceptional communication and consulting skills, with the ability to present solutions to both technical and non-technical stakeholders.</li>\n</ul>\n<p>Preferred Skills:</p>\n<ul>\n<li>Hands-on exposure to cognitive architectures, planning-based agents, or reinforcement learning in real-world deployments.</li>\n<li>Experience integrating AI agents into enterprise apps like Salesforce, ServiceNow, SAP, or custom apps via APIs.</li>\n<li>Understanding of AI observability, performance monitoring, and ethical guidelines in GenAI systems.</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_8073098e-063","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Infosys Consulting - Europe","sameAs":"https://www.infosys.com/","logo":"https://logos.yubhub.co/infosys.com.png"},"x-apply-url":"https://jobs.workable.com/view/qRNKkoyRyMYbqe7zLDz6tb/remote-agentic-ai-architect-in-poland-at-infosys-consulting---europe","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","LangChain","AutoGen","CrewAI","Semantic Kernel","LLMs","prompt engineering","agent memory","tool calling","autonomous task execution","pre-sales","RFP/RFI support","proposal creation","cloud platforms","API integration","data security best practices"],"x-skills-preferred":["cognitive architectures","planning-based agents","reinforcement learning","AI observability","performance monitoring","ethical guidelines"],"datePosted":"2026-04-24T14:09:59.450Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Poland"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, LangChain, AutoGen, CrewAI, Semantic Kernel, LLMs, prompt engineering, agent memory, tool calling, autonomous task execution, pre-sales, RFP/RFI support, proposal creation, cloud platforms, API integration, data security best practices, cognitive architectures, planning-based agents, reinforcement learning, AI observability, performance monitoring, ethical guidelines"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_21b20e0c-c8a"},"title":"Software Developer - Battlefield QV","description":"<p>Electronic Arts creates next-level entertainment experiences that inspire players and fans around the world. Our Quality Verification and Standards (QVS) team is an important part of our development process, consistently delivering actionable insights that support our game teams to optimize software performance and elevate gameplay.</p>\n<p>As a Software Developer within the Quality Verification Engineering (QVE) Team, you will report to the Engineering Manager. 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As a Senior Machine Learning Engineer, you will join our multidisciplinary effort to deliver new core game mechanics that act in more believable ways by leveraging Machine Learning (ML) and Reinforcement Learning (RL).</p>\n<p>Your responsibilities will include researching, evaluating, and deploying Reinforcement Learning models and techniques for real-time applications, investigating novel imitation learning techniques applicable to the game&#39;s mechanics, and tailoring the functionality of these models and inference frameworks to fit FC&#39;s requirements and target platform constraints.</p>\n<p>You will also develop, optimize, and polish game features that leverage these models to deliver them to FC players, share knowledge on your work by directly engaging with other members of the game team to develop and ship features, evangelize the craft through presentations and interactive demonstrations, promoting Machine Learning best practices and applications within the team, and stay abreast of the latest advancements in the Machine Learning and Reinforcement Learning field.</p>\n<p>Required qualifications include a PhD or Masters degree in Computer Science, Mathematics, or a related field, or equivalent professional experience, strong computer programming fundamentals, proficiency in Python, and experience with C++. 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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 Future of Computing Research team is an applied research team within the Consumer Devices group focused on developing new methods, models, and evaluation frameworks that support our vision for the future of computing. We work at the frontier of multimodal AI, helping turn emerging model capabilities into product experiences that are useful, delightful, and worthy of long-term trust.</p>\n<p>Our work explores a new class of AI systems that can learn over time, adapt to individuals, and support people in the flow of daily life. This includes long-term memory, user modeling, and personalization systems that are aligned not just with immediate satisfaction, but with a person’s broader goals, values, and well-being.</p>\n<p>We work closely across research, engineering, design, product, and safety to define what it means to build AI systems that know you over time, act at the right moment, and help in ways that are context-aware, respectful, and demonstrably beneficial.</p>\n<p><strong>About the Role</strong></p>\n<p>We are looking for a Research Engineer / Scientist to join the Future of Computing Research team to work on RLHF and post-training for personalized, multimodal AI systems.</p>\n<p>This role will focus on building the learning and evaluation foundations that help models become more context-aware, adaptive, and useful over time. You will work on problems such as reward modeling, preference learning, long-horizon evaluation, and policy improvement for systems that must make high-quality behavioral decisions in realistic user settings. The work is deeply product-grounded: success is not just higher benchmark performance, but better model behavior in real-world use.</p>\n<p>The ideal candidate is excited about pushing beyond one-turn assistant behavior toward systems that improve through feedback, learn from richer signals, and are trained against meaningful notions of user value. Internally, that maps closely to the need for careful reward design, feedback loops, and evaluation frameworks that test whether interventions are actually beneficial over longer horizons.</p>\n<p><strong>This role is based in San Francisco, CA. We use a hybrid work model of four days in the office per week and offer relocation assistance to new employees.</strong></p>\n<p><strong>In this role, you will:</strong></p>\n<p>orderId</p>\n<ul>\n<li>Develop RLHF and post-training methods for multimodal models.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Build reward models and preference-learning pipelines for adaptive, personalized model behavior.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Design datasets, rubrics, and evaluation frameworks that capture user preferences, contextual appropriateness, and long-term value in realistic tasks.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Run experiments on policy improvement using explicit feedback, implicit signals, and model-based grading.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Work on long-horizon evaluation problems, where model quality depends not just on a single response but on whether behavior improves outcomes over time.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Collaborate closely with safety researchers to ensure that adaptation and personalization remain aligned, interpretable, and bounded by clear constraints.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Prototype and iterate quickly on training recipes, reward formulations, data pipelines, and evaluation suites for product-relevant behaviors.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Help define how OpenAI measures success for personalized AI systems including trust, appropriateness, and long-term user benefit.</li>\n</ul>\n<p><strong>You might thrive in this role if you:</strong></p>\n<p>orderId</p>\n<ul>\n<li>Have a strong background in machine learning research, with experience in RLHF, reward modeling, preference optimization, or post-training for large models.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Have worked on one or more of: reinforcement learning, ranking, recommender systems, personalization, memory, or human-in-the-loop evaluation.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Care about rigorous empirical work and know how to design clean experiments, reliable evals, and decision-useful metrics.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Are excited by the challenge of training models against nuanced behavioral objectives.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Have experience building datasets or eval pipelines grounded in human preferences, rubrics, or real-world product behavior.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Are comfortable working across the stack, from data generation and labeling strategy to training runs, reward functions, and analysis.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Are interested in multimodal AI and in how models can learn from richer interaction signals over time.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Want to work on product-shaping research with unusually high stakes for trust, alignment, and long-term user value.</li>\n</ul>\n<p>orderId</p>\n<ul>\n<li>Enjoy close collaboration with engineers, designers, and safety researchers to turn frontier research into real 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. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.</p>\n<p>We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.</p>\n<p>For additional information, please see [OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement](https://cdn.openai.com/policies/eeo-policy-statement.pdf).</p>\n<p>Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history has a direct and adverse relationship with your current or prospective employment. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider the nature and severity of the offense, as well as the time elapsed since the offense occurred, in making our decision.</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_1a6953e8-a44","directApply":true,"hiringOrganization":{"@type":"Organization","name":"OpenAI","sameAs":"https://openai.com","logo":"https://logos.yubhub.co/openai.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/openai/09253a0e-a2b8-49d1-80bb-6955ba3b08a3","x-work-arrangement":"hybrid","x-experience-level":null,"x-job-type":"Full time","x-salary-range":"$380K – $445K","x-skills-required":["Machine Learning","Reinforcement Learning","Reward Modeling","Preference Optimization","Post-Training","Multimodal Models","Long-Horizon Evaluation","Policy Improvement","Human-in-the-Loop Evaluation","Data Generation","Labeling Strategy","Training Runs","Reward Functions","Analysis"],"x-skills-preferred":[],"datePosted":"2026-04-24T12:20:58.680Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, Reinforcement Learning, Reward Modeling, Preference Optimization, Post-Training, Multimodal Models, Long-Horizon Evaluation, Policy Improvement, Human-in-the-Loop Evaluation, Data Generation, Labeling Strategy, Training Runs, Reward Functions, Analysis","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":380000,"maxValue":445000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_4f66126c-72e"},"title":"AI Deployment Engineer, Startups","description":"<p>We are seeking a technically proficient, product-minded engineer to help push the frontier of advanced AI with our strategic startup customers. You&#39;ll work with some of the most exciting AI startups in the world, helping them optimize their own systems and turning those learnings into durable improvements across OpenAI’s research and products.</p>\n<p>This role is well suited to engineers who are equally comfortable debugging a workflow, iterating on prompts or agents, designing evaluations, and collaborating across research and product. You should be excited by ambiguous, high-impact problems and motivated by the opportunity to shape how advanced AI systems improve in practice.</p>\n<p>In this role, you will:</p>\n<ul>\n<li>Work directly with strategic startup customers to understand critical workflows, uncover failure modes, and identify high-impact opportunities for improvement.</li>\n<li>Prototype and iterate on prompts, agents, and workflow designs to better understand system behavior and unlock customer value.</li>\n<li>Synthesize and deliver valuable feedback to the Product and Research teams, turning real usage patterns into clear, reproducible evals, benchmarks, and technical artifacts that improve model and product quality and ensure customer-grounded learnings influence roadmap and model development.</li>\n<li>Build repeatable tools, patterns, and evaluation approaches that raise the quality bar across multiple use cases.</li>\n<li>Operate with strong judgment in ambiguous environments, balancing immediate technical problem-solving with longer-term system improvement.</li>\n<li>Build relationships within the startup ecosystem, serving as a technical partner to both individual customers and the broader community.</li>\n<li>Create technical presentations, demos, and other forms of community engagement with top developers and startups around the region.</li>\n</ul>\n<p>You’ll thrive in this role if you:</p>\n<ul>\n<li>Have strong software engineering &amp; AI fundamentals. For example, experience as a software engineer, ML engineer, Data Scientist or equivalent. Experience shipping production systems end-to-end is a strong plus.</li>\n<li>Have familiarity with, or interest in, model training pipelines and reinforcement learning.</li>\n<li>Have experience building AI applications, agents, or evaluation systems, and can reason clearly about model behavior in complex workflows.</li>\n<li>Are comfortable working directly with highly technical users and translating their challenges into concrete technical signals.</li>\n<li>Can move fluidly between prototyping, debugging, evaluation design, and cross-functional collaboration.</li>\n<li>Communicate clearly across technical and non-technical audiences.</li>\n<li>Bring high agency, strong product sense, and a bias toward building durable improvements rather than one-off fixes</li>\n<li>Enjoy having some days where you engage in community events and present to large audiences, and other days where you go deep on specific customer problems.</li>\n<li>Speak multiple languages: Strong proficiency in English is required. Additional proficiency in Mandarin, Korean, and/or Japanese is a nice to have.</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_4f66126c-72e","directApply":true,"hiringOrganization":{"@type":"Organization","name":"OpenAI","sameAs":"https://openai.com/","logo":"https://logos.yubhub.co/openai.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/openai/16df5b48-917d-41d8-b0c6-dbfdf11400f7","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"Full time","x-salary-range":null,"x-skills-required":["software engineering","AI fundamentals","model training pipelines","reinforcement learning","AI applications","agents","evaluation systems"],"x-skills-preferred":[],"datePosted":"2026-04-24T12:19:45.669Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Singapore"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, AI fundamentals, model training pipelines, reinforcement learning, AI applications, agents, evaluation systems"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_6ca1bab3-645"},"title":"Research Engineer — Reinforcement Learning","description":"<p>You&#39;ll bring reinforcement learning to Firecrawl&#39;s core product , building the training infrastructure, reward pipelines, and fine-tuning systems that make our models meaningfully better at extracting, understanding, and structuring web data.</p>\n<p>This isn&#39;t theoretical RL research. You&#39;ll build your own training infra, run fast experiments, ship models to production, and bridge the gap between classical RL approaches and modern LLM agent systems. If you care as much about training throughput as you do about reward design, this is the role.</p>\n<p><strong>Salary Range:</strong> $180,000–$290,000/year (Range shown is for U.S.-based employees. Compensation outside the U.S. is adjusted fairly based on your country&#39;s cost of living.)</p>\n<p><strong>Equity Range:</strong> Up to 0.15%</p>\n<p><strong>Location:</strong> San Francisco, CA or Remote (Americas, UTC-3 to UTC-10)</p>\n<p><strong>Job Type:</strong> Full-Time</p>\n<p><strong>Experience:</strong> 3+ years in applied RL, ML engineering, or model training , with production systems</p>\n<p><strong>Visa:</strong> US Citizenship/Visa required for SF; N/A for Remote</p>\n<p><strong>Build training infrastructure and reward pipelines from scratch.</strong> Design and operate the systems that train and evaluate Firecrawl&#39;s models. You&#39;ll own the full loop , data collection, reward modeling, training runs, evaluation, and deployment. You build the infra yourself because you&#39;re the one who needs it to work.</p>\n<p><strong>Fine-tune models to achieve state-of-the-art results.</strong> Take foundation models and make them dramatically better at web data extraction, content understanding, and structured output generation. You know how to get from &#39;decent fine-tune&#39; to &#39;best-in-class&#39; and you have the patience and rigor to close that gap.</p>\n<p><strong>Bridge LLM agents and classical RL.</strong> The most interesting problems at Firecrawl sit at the intersection of modern LLM-based agents and classical RL techniques. You&#39;ll design reward signals for agent behaviors, apply RL methods to improve multi-step agent workflows, and figure out where traditional RL approaches outperform prompting , and vice versa.</p>\n<p><strong>Run fast experiments and iterate.</strong> You design experiments that test meaningful hypotheses, run them quickly, and make decisions based on results. You don&#39;t spend weeks on experiment infrastructure before getting a single result. Speed of iteration is a core part of how you work.</p>\n<p><strong>Communicate clearly to non-RL people.</strong> RL can be opaque. You translate your work into language that engineers, product people, and leadership can understand and act on. You know how to explain why a reward function matters without requiring everyone to read the paper.</p>\n<p><strong>Collaborate closely with the team.</strong> Work directly with the Search/IR-focused Research Engineer and the engineering team to connect RL improvements with search, ranking, and the broader product roadmap.</p>\n<p><strong>Builds their own training infra and reward pipelines.</strong> You don&#39;t wait for an ML platform team to set things up. You build the training loops, reward models, data pipelines, and evaluation frameworks yourself , because you understand that infra choices directly affect the quality of results. You&#39;ve operated GPU clusters, managed training runs, and debugged convergence issues in production.</p>\n<p><strong>Can fine-tune models to SOTA.</strong> You&#39;ve taken models from baseline to best-in-class on tasks that matter. You understand the full fine-tuning lifecycle , data curation, training dynamics, hyperparameter sensitivity, evaluation methodology , and you have the taste to know when a model is actually good versus when the eval is flattering.</p>\n<p><strong>Bridges LLM agents and classical RL.</strong> You&#39;re fluent in both worlds. You understand PPO, RLHF, reward modeling, and policy optimization , and you understand how modern LLM agents work, where they fail, and how RL techniques make them better. You see connections between these domains that most people miss.</p>\n<p><strong>Production-minded.</strong> You care about whether your models work in production, not just on benchmarks. You&#39;ve deployed models that serve real traffic and made hard tradeoffs between model quality, latency, and cost. Research that doesn&#39;t ship isn&#39;t research that matters here.</p>\n<p><strong>Runs fast experiments and communicates clearly.</strong> You&#39;d rather run three rough experiments this week than one polished one next month. When you have results, anyone on the team can understand what they mean , no decoder ring required.</p>\n<p><strong>Backgrounds that tend to do well:</strong> RL engineers at AI labs or applied ML teams who&#39;ve shipped models to production. Researchers who&#39;ve done RLHF or reward modeling for LLM systems. ML engineers who&#39;ve built training infrastructure at startups and cared as much about the pipeline as the model. People who&#39;ve worked at the intersection of RL and language models , whether in academic labs with a production bent or at companies building agent systems.</p>\n<p><strong>What We&#39;re NOT Looking For:</strong></p>\n<p><strong>Pure theorists.</strong> If your best RL work lives in a paper and you&#39;ve never trained a model on real data at real scale, this isn&#39;t the role. We need someone who builds and ships.</p>\n<p><strong>Researchers who need a platform team.</strong> If you expect training infrastructure, data pipelines, and evaluation frameworks to be set up before you can be productive, you&#39;ll be frustrated here. You build the tools you need.</p>\n<p><strong>People who only know one paradigm.</strong> Deep in classical RL but never worked with LLMs? LLM fine-tuner who&#39;s never touched RL? You&#39;ll be missing half the picture. This role requires fluency in both.</p>\n<p><strong>Slow iterators.</strong> If your standard experiment cycle is measured in weeks, not days, you&#39;ll struggle with the pace. We need someone who can run a meaningful experiment, interpret results, and decide next steps within a day or two.</p>\n<p><strong>Black-box communicators.</strong> If your typical update is a wall of metrics only another RL researcher can parse, this isn&#39;t the right fit. We need someone who can explain what&#39;s working, what&#39;s not, and why it matters , to people without RL PhDs.</p>\n<p><strong>A Note On Pace:</strong> We operate at an absurd level of urgency because the window for what we&#39;re building won&#39;t stay open forever. If that excites you, keep reading. If it doesn&#39;t, no hard feelings , but this role probably isn&#39;t for you.</p>\n<p><strong>Benefits &amp; Perks:</strong></p>\n<p><strong>Available to all employees</strong></p>\n<ul>\n<li><strong>Salary that makes sense</strong> , $180,000–$290,000/year, based on impact, not tenure</li>\n</ul>\n<ul>\n<li><strong>Own a piece</strong> , Up to 0.15% equity in what you&#39;re helping build</li>\n</ul>\n<ul>\n<li><strong>Generous PTO</strong> , 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge</li>\n</ul>\n<ul>\n<li><strong>Parental leave</strong> , 12 weeks fully paid, for moms and dads</li>\n</ul>\n<ul>\n<li><strong>Wellness stipend</strong> , $100/month for the gym, therapy, massages, or whatever keeps you human</li>\n</ul>\n<ul>\n<li><strong>Learning &amp; Development</strong> , Expense up to $1,000/year toward anything that helps you grow professionally</li>\n</ul>\n<ul>\n<li><strong>Team offsites</strong> , A change of scenery, minus the trust falls</li>\n</ul>\n<ul>\n<li><strong>Sabbatical</strong> , 3 paid months off after 4 years, do something fun and new</li>\n</ul>\n<p><strong>Available to US-based full-time employees</strong></p>\n<ul>\n<li><strong>Full coverage, no red tape</strong> , Medical, dental, and vision (100% for</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_6ca1bab3-645","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Firecrawl","sameAs":"https://www.firecrawl.dev","logo":"https://logos.yubhub.co/firecrawl.dev.png"},"x-apply-url":"https://jobs.ashbyhq.com/firecrawl/26abaf11-ff85-4f8d-ba44-2b6d32aae2a1","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"Full time","x-salary-range":"$180,000–$290,000/year","x-skills-required":["Reinforcement Learning","Machine Learning","Deep Learning","Python","GPU Clusters","Training Runs","Evaluation Frameworks","Data Pipelines","Reward Modeling","Policy Optimization","LLM Agents","Classical RL Techniques"],"x-skills-preferred":[],"datePosted":"2026-04-24T12:17:17.208Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA (Hybrid) OR Remote (Americas, UTC-3 to UTC-10)"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Reinforcement Learning, Machine Learning, Deep Learning, Python, GPU Clusters, Training Runs, Evaluation Frameworks, Data Pipelines, Reward Modeling, Policy Optimization, LLM Agents, Classical RL Techniques","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":180000,"maxValue":290000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_f7ea1e7a-8a7"},"title":"Research Engineer","description":"<p>You&#39;ll own the quality of AI across everything Gamma creates. As our Research Engineer, you&#39;ll design evaluation frameworks that measure AI output quality, systematically improve production prompts, and fine-tune models to ensure millions of users get exceptional results every time they generate content.</p>\n<p>This role sits at the intersection of research rigor and product impact. You&#39;ll diagnose failure patterns in AI-generated presentations, docs, and websites, then craft targeted improvements through iterative experimentation. You&#39;ll build the tools and workflows that enable rapid testing, validate changes against quality benchmarks, and ensure our AI gets smarter with every iteration.</p>\n<p>Our team has a strong in-office culture and works in person 4–5 days per week in San Francisco. 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You&#39;ll contribute to the development of scalable ML systems serving hundreds of millions of users. You&#39;ll promote best practices in ML system design, testing, evaluation, and deployment across the organization. You&#39;ll actively contribute to a strong community of machine learning practitioners at Spotify.</p>\n<p>We&#39;re looking for experienced machine learning engineers who enjoy solving complex real-world problems in collaborative environments. You should have a strong background in machine learning, natural language processing, and generative AI. You should be comfortable applying theory to build real-world, production-ready applications. You should have hands-on experience building and deploying end-to-end ML systems at scale. You should be familiar with LLM-based systems and techniques for improving them using human feedback such as reinforcement fine-tuning, DPO, or similar approaches. You should have experience designing modular ML architectures and writing technical specifications in partnership with product teams. You should be experienced with large-scale distributed data processing tools such as Apache Beam or Apache Spark. 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Leveraging cutting-edge AI/ML technologies, you will design, build, deploy, and refine highly reliable AI agents operating at massive scale. Your work will power Dropbox Dash&#39;s universal agentic search and autonomous organization features, transforming how millions of users collaborate, stay organized, and focus on the work that truly matters.</p>\n<p>Our ideal candidate has a BS, MS, or PhD in Computer Science, Mathematics, Statistics, or a related quantitative field (or equivalent work experience). They should have 8+ years of software engineering experience, with at least 5+ years dedicated to building and deploying production-scale AI/ML systems. They should also have professional experience in ML modeling for complex systems such as Search, Ranking, or Recommender Systems.</p>\n<p>The successful candidate will have deep familiarity with LLM architectures and hands-on experience with ML libraries (e.g., PyTorch, JAX, or similar). 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Applicants are encouraged to ask for more details of the rotations to which the applicant is applying.</p>\n<p>We offer a competitive salary range of $245,200-$331,800 USD (US Zone 1), $220,700-$298,700 USD (US Zone 2), or $196,200-$265,400 USD (US Zone 3).</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_9619f96b-6ca","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Dash Agentic AI","sameAs":"https://www.dropbox.com/","logo":"https://logos.yubhub.co/dropbox.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/dropbox/jobs/7644886","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$245,200-$331,800 USD (US Zone 1), $220,700-$298,700 USD (US Zone 2), or $196,200-$265,400 USD (US Zone 3)","x-skills-required":["BS, MS, or PhD in Computer Science, Mathematics, Statistics, or a related quantitative field","8+ years of software engineering experience","5+ years dedicated to building and deploying production-scale AI/ML systems","Professional experience in ML modeling for complex systems such as Search, Ranking, or Recommender Systems","Deep familiarity with LLM architectures","Hands-on experience with ML libraries (e.g., PyTorch, JAX, or similar)","Strong proficiency in Python","Experience with systems languages like Go or C/C++","Extensive experience working with large-scale distributed data systems and high-throughput production environments","Exceptional analytical skills","Bias to action when navigating ambiguous technical challenges"],"x-skills-preferred":["PhD with a focus on Deep Learning, NLP, or Reinforcement Learning (RLHF/RLAIF)","Proven track record of taking AI products from concept to launch","Hands-on experience with autonomous agent frameworks","Multi-step planning","Tool-use (function calling)","Advanced RAG","Inference optimization","Model distillation","Fine-tuning techniques"],"datePosted":"2026-04-24T12:12:17.443Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - US: All locations"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"BS, MS, or PhD in Computer Science, Mathematics, Statistics, or a related quantitative field, 8+ years of software engineering experience, 5+ years dedicated to building and deploying production-scale AI/ML systems, Professional experience in ML modeling for complex systems such as Search, Ranking, or Recommender Systems, Deep familiarity with LLM architectures, Hands-on experience with ML libraries (e.g., PyTorch, JAX, or similar), Strong proficiency in Python, Experience with systems languages like Go or C/C++, Extensive experience working with large-scale distributed data systems and high-throughput production environments, Exceptional analytical skills, Bias to action when navigating ambiguous technical challenges, PhD with a focus on Deep Learning, NLP, or Reinforcement Learning (RLHF/RLAIF), Proven track record of taking AI products from concept to launch, Hands-on experience with autonomous agent frameworks, Multi-step planning, Tool-use (function calling), Advanced RAG, Inference optimization, Model distillation, Fine-tuning techniques","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":196200,"maxValue":331800,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_3e0a4df0-973"},"title":"Principal Applied Scientist","description":"<p>This role is part of the Microsoft Search &amp; Ads Network (MSAN) modeling team, focused on building large-scale machine learning systems for ads retrieval, ranking, user understanding and marketplace optimization across different surfaces. The team develops end-to-end models that predict user engagement and advertiser value – powering candidate generation, relevance scoring, and serving stack ranking that directly impact ad quality, delivery efficiency, and revenue. Responsibilities span the full modeling lifecycle, including training data and labeling strategy, feature and signal design, model development, and rigorous offline and online evaluation. Engineers and applied scientists work closely at the intersection of machine learning, economics, and large-scale systems to deliver high-performance real-time inference and robust experimentation in production.</p>\n<p>As a Principal Applied Scientist, you will have a solid background in Machine Learning, Reinforcement Learning, Causal Inference, Data Science, Data Mining, or related field. You will play a key role in driving algorithmic improvements to online and offline systems, develop and deliver robust and scalable solutions, make direct impact to both user and advertisers experience, and continually increase the revenue for Bing ads.</p>\n<p>Qualifications:</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)</li>\n<li>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)</li>\n<li>Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Equivalent experience</li>\n</ul>\n<p>Preferred Qualifications:</p>\n<ul>\n<li>Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Equivalent experience</li>\n</ul>\n<p>Research experience (publications) in the following areas: statistical machine learning, deep learning, data mining, causal inference, information retrieval, and Bayesian inference. 2+ years of experience in any of the following areas: ads retrieval and ranking system, statistical machine learning, deep learning, data mining, causal inference, information retrieval, game theory, mechanism design, optimization and Bayesian inference. Proficient problem solving and data analysis skills. Proficient software design and development skills/experience.</p>\n<p>#MicrosoftAI Applied Sciences IC5 – The typical base pay range for this role across Canada is CAD $142,400 – CAD $257,500 per year. Find additional pay information here: https://careers.microsoft.com/v2/global/en/canada-pay-information.html</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_3e0a4df0-973","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/principal-applied-scientist-30/","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"CAD $142,400 – CAD $257,500 per year","x-skills-required":["Machine Learning","Reinforcement Learning","Causal Inference","Data Science","Data Mining","Statistics","Econometrics","Computer Science","Electrical or Computer Engineering"],"x-skills-preferred":["statistical machine learning","deep learning","information retrieval","Bayesian inference","ads retrieval and ranking system","game theory","mechanism design","optimization"],"datePosted":"2026-04-24T12:11:59.042Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Vancouver"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, Reinforcement Learning, Causal Inference, Data Science, Data Mining, Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, statistical machine learning, deep learning, information retrieval, Bayesian inference, ads retrieval and ranking system, game theory, mechanism design, optimization","baseSalary":{"@type":"MonetaryAmount","currency":"CAD","value":{"@type":"QuantitativeValue","minValue":142400,"maxValue":257500,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c00d615d-431"},"title":"Principal Applied Scientist","description":"<p>This role is part of the Microsoft Search &amp; Ads Network (MSAN) modeling team, focused on building large-scale machine learning systems for ads retrieval, ranking, user understanding and marketplace optimization across different surfaces. The team develops end-to-end models that predict user engagement and advertiser value – powering candidate generation, relevance scoring, and serving stack ranking that directly impact ad quality, delivery efficiency, and revenue. Responsibilities span the full modeling lifecycle, including training data and labeling strategy, feature and signal design, model development, and rigorous offline and online evaluation. Engineers and applied scientists work closely at the intersection of machine learning, economics, and large-scale systems to deliver high-performance real-time inference and robust experimentation in production.</p>\n<p>As a Principal Applied Scientist, you will have a solid background in Machine Learning, Reinforcement Learning, Causal Inference, Data Science, Data Mining, or related field. You will play a key role in driving algorithmic improvements to online and offline systems, develop and deliver robust and scalable solutions, make direct impact to both user and advertisers experience, and continually increase the revenue for Bing ads.</p>\n<p>Qualifications:</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)</li>\n<li>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)</li>\n<li>Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Equivalent experience</li>\n</ul>\n<p>Preferred Qualifications:</p>\n<ul>\n<li>Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Equivalent experience</li>\n</ul>\n<p>Research experience (publications) in the following areas: statistical machine learning, deep learning, data mining, causal inference, information retrieval, and Bayesian inference. 2+ years of experience in any of the following areas: ads retrieval and ranking system, statistical machine learning, deep learning, data mining, causal inference, information retrieval, game theory, mechanism design, optimization and Bayesian inference. Proficient problem solving and data analysis skills. Proficient software design and development skills/experience.</p>\n<p>#MicrosoftAI Applied Sciences IC5 – The typical base pay range for this role across Canada is CAD $142,400 – CAD $257,500 per year.</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_c00d615d-431","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/principal-applied-scientist-31/","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Machine Learning","Reinforcement Learning","Causal Inference","Data Science","Data Mining","Statistics","Econometrics","Computer Science","Electrical or Computer Engineering"],"x-skills-preferred":["statistical machine learning","deep learning","information retrieval","Bayesian inference","ads retrieval and ranking system","game theory","mechanism design","optimization"],"datePosted":"2026-04-24T12:11:19.587Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Vancouver"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, Reinforcement Learning, Causal Inference, Data Science, Data Mining, Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, statistical machine learning, deep learning, information retrieval, Bayesian inference, ads retrieval and ranking system, game theory, mechanism design, optimization"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c3894069-07b"},"title":"Senior Applied Scientist","description":"<p>The Core Recommendation Ranking team in Microsoft AI Content Org powers the end-to-end ranking and reranking stack behind Microsoft’s content experiences , including news, interest, video, and AI-generated content (AIGC) feeds, reaching hundreds of millions of users worldwide.</p>\n<p>We are at the forefront of integrating Generative AI and agentic systems into large-scale recommendation pipelines. We are seeking a Senior Applied Scientist to design, build, and optimize ranking and recommendation models that directly impact user engagement across Microsoft’s content ecosystem.</p>\n<p>In this role, you will work hands-on with cutting-edge deep learning and LLM-enhanced ranking systems while collaborating closely with engineering and product partners to deliver production-quality solutions at scale.</p>\n<p>Microsoft’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.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Design &amp; implement ranking, reranking, and retrieval models using deep learning, LLMs, and advanced recommendation techniques.</li>\n<li>Own end-to-end ML pipelines , feature engineering, model training, offline/online evaluation, and production inference optimization.</li>\n<li>Innovate by applying state-of-the-art methods including LLM-enhanced ranking, contextual bandits, reinforcement learning, and generative recommendation approaches.</li>\n<li>Collaborate cross-functionally with engineering, product, and platform teams to translate research insights into shipped features.</li>\n<li>Contribute to technical direction within the team , propose experiments, identify opportunities, and drive projects from ideation to production.</li>\n<li>Mentor less experienced scientists and engineers, fostering a culture of technical excellence and knowledge sharing.</li>\n</ul>\n<p>Qualifications:</p>\n<ul>\n<li>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) 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) 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) OR equivalent experience.</li>\n<li>4+ years of industry experience in applied science, machine learning, or deep learning at scale.</li>\n<li>Solid foundation in recommendation systems, ranking models, or search relevance.</li>\n<li>Hands-on experience with deep learning frameworks (PyTorch or TensorFlow) and cloud-scale ML infrastructure.</li>\n<li>Proficiency in Python and data processing tools (Spark, Pandas, or equivalent).</li>\n<li>Track record of shipping ML models to production with measurable user impact.</li>\n<li>Experience with LLM-based ranking, retrieval-augmented generation (RAG), or generative recommendation systems.</li>\n<li>Familiarity with multi-objective optimization, heterogeneous signal fusion, or user modeling.</li>\n<li>Experience with online experimentation (A/B testing, interleaving) and metrics-driven development.</li>\n<li>Publications at top venues (NeurIPS, ICML, KDD, WWW, RecSys, SIGIR).</li>\n<li>Exposure to agentic AI systems or autonomous content curation pipelines.</li>\n<li>Experience with distributed ML training and large-scale data pipelines.</li>\n</ul>\n<p>#MicrosoftAI Applied Sciences IC4 – The typical base pay range for this role across the U.S. is USD $119,800 – $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 – $258,000 per year.</p>\n<p>This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.</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_c3894069-07b","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/senior-applied-scientist-53/","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"USD $119,800 – $234,700 per year","x-skills-required":["deep learning","LLMs","advanced recommendation techniques","feature engineering","model training","offline/online evaluation","production inference optimization","state-of-the-art methods","LLM-enhanced ranking","contextual bandits","reinforcement learning","generative recommendation approaches","cross-functional collaboration","engineering","product","platform teams","research insights","shipped features","technical direction","experiments","opportunities","projects","ideation","production","mentorship","technical excellence","knowledge sharing","statistics","predictive analytics","research","applied science","machine learning","deep learning at scale","recommendation systems","ranking models","search relevance","deep learning frameworks","cloud-scale ML infrastructure","Python","data processing tools","Spark","Pandas","shipping ML models","LLM-based ranking","retrieval-augmented generation","generative recommendation systems","multi-objective optimization","heterogeneous signal fusion","user modeling","online experimentation","metrics-driven development","publications","agentic AI systems","autonomous content curation pipelines","distributed ML training","large-scale data pipelines"],"x-skills-preferred":[],"datePosted":"2026-04-24T12:10:43.896Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"deep learning, LLMs, advanced recommendation techniques, feature engineering, model training, offline/online evaluation, production inference optimization, state-of-the-art methods, LLM-enhanced ranking, contextual bandits, reinforcement learning, generative recommendation approaches, cross-functional collaboration, engineering, product, platform teams, research insights, shipped features, technical direction, experiments, opportunities, projects, ideation, production, mentorship, technical excellence, knowledge sharing, statistics, predictive analytics, research, applied science, machine learning, deep learning at scale, recommendation systems, ranking models, search relevance, deep learning frameworks, cloud-scale ML infrastructure, Python, data processing tools, Spark, Pandas, shipping ML models, LLM-based ranking, retrieval-augmented generation, generative recommendation systems, multi-objective optimization, heterogeneous signal fusion, user modeling, online experimentation, metrics-driven development, publications, agentic AI systems, autonomous content curation pipelines, distributed ML training, large-scale data pipelines","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_39bd766f-e02"},"title":"Member of Technical Staff - Data Scientist","description":"<p>We&#39;re looking for data scientists to help build the next generation of post-training methods for frontier models at Microsoft AI. You&#39;ll join a small, high-impact team working across all stages of post-training, with a focus on evaluation design, high-quality training data, and scalable data pipelines for state-of-the-art foundation models.</p>\n<p>In this role, you&#39;ll help turn raw model capability into reliable, aligned, and measurable performance improvements, directly shaping how frontier models behave in real-world deployments.</p>\n<p>About the Role:</p>\n<p>Microsoft AI is building the next generation of frontier models that power Copilot and other large-scale AI experiences. The Post-Training team is responsible for transforming powerful pretrained models into robust, aligned, and high-performing systems used by millions of people worldwide.</p>\n<p>Our work focuses on improving general quality, instruction following, coding and math ability, tool use, agentic behaviors, personality, and other critical model capabilities. We operate across the full post-training lifecycle , from data generation and curation, to evaluation and diagnostics, to reward modeling and reinforcement learning.</p>\n<p>We are a small, highly autonomous team that works closely with pre-training, product, and engineering partners to rapidly iterate on ideas, run large-scale experiments, and safely advance model capabilities. Each team member owns meaningful parts of the post-training pipeline and has direct access to the compute, data, and decision-making needed to move quickly from insight to production.</p>\n<p>Microsoft Superintelligence Team</p>\n<p>This role is part of Microsoft AI’s Superintelligence Team. The MAIST is a startup-like team inside Microsoft AI, created to push the boundaries of AI toward Humanist Superintelligence,ultra-capable systems that remain controllable, safety-aligned, and anchored to human values.</p>\n<p>Our mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society,advancing science, education, and global well-being. We’re also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact.</p>\n<p>Responsibilities</p>\n<p>Design evaluations of advanced model capabilities and use them to drive rapid, high-signal iteration loops Work with vendors to produce high quality evaluation and training data Build data pipelines to produce high quality evaluation and training data Build data flywheels to hill-climb on model weaknesses, using data from various surfaces where our models are deployed Ensure optimal quality, quantity and coverage of data across our post-training stages Run post-training experiments and ablations to produce models that climb our evals Embody our culture and values.</p>\n<p>We’re Looking For People Who:</p>\n<p>Have deep experience with LLMs, either training them or applying them in production Have developed production-scale data pipelines for synthesizing, curating, or processing large quantities of data Can design, run, and interpret large-scale ML experiments with careful statistical and empirical reasoning.</p>\n<p>Possess strong generalist engineering and mathematical skills.</p>\n<p>Have clear written and verbal communication, and the ability to collaborate effectively with researchers, engineers and other disciplines.</p>\n<p>Bonus skills:</p>\n<p>Demonstrated SOTA results in any area of large-scale training, inference, or evaluation.</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_39bd766f-e02","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-data-scientist-5/","x-work-arrangement":"onsite","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["large language models","post-training experiments","data pipelines","evaluation and diagnostics","reward modeling and reinforcement learning","Python","statistical and experimental fundamentals"],"x-skills-preferred":[],"datePosted":"2026-04-24T12:09:30.610Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Zurich"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"large language models, post-training experiments, data pipelines, evaluation and diagnostics, reward modeling and reinforcement learning, Python, statistical and experimental fundamentals"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_7b33f213-4b5"},"title":"Machine Learning Manager, Notifications Relevance","description":"<p>We are looking for an Engineering Manager to lead our Notifications Relevance team, shaping the future of Notifications at Reddit. 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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.</p>\n<p>Scale employees in eligible roles are also granted equity-based compensation, subject to Board of Director approval. 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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. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models.</p>\n<p>Key Responsibilities:</p>\n<ul>\n<li>Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities.</li>\n<li>Design and experiment new approaches to preference optimization.</li>\n<li>Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness.</li>\n<li>Publish research findings in top-tier AI conferences.</li>\n</ul>\n<p>Ideal Candidate:</p>\n<ul>\n<li>Ph.D. or Master&#39;s degree in Computer Science, Machine Learning, AI, or a related field.</li>\n<li>Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.</li>\n<li>Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning.</li>\n<li>Excellent written and verbal communication skills</li>\n<li>Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals</li>\n<li>Previous experience in a customer-facing role.</li>\n</ul>\n<p>Compensation packages at Scale for eligible roles include base salary, equity, and benefits. 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.</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_683a40cb-69e","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale","sameAs":"https://scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4528009005","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$252,000-$315,000 USD","x-skills-required":["deep learning","reinforcement learning","large-scale model fine-tuning","post-training techniques","RLHF","preference modeling","instruction tuning"],"x-skills-preferred":["published research","customer-facing role"],"datePosted":"2026-04-18T15:59:43.190Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; Seattle, WA; New York, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"deep learning, reinforcement learning, large-scale model fine-tuning, post-training techniques, RLHF, preference modeling, instruction tuning, published research, customer-facing role","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":252000,"maxValue":315000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_2da35265-f56"},"title":"Machine Learning Research Engineer - Robotics","description":"<p>We&#39;re seeking a Machine Learning Research Engineer to join our Robotics business unit. As a key contributor, you&#39;ll conduct applied research in Robotics and develop ML pipelines for training and fine-tuning on data collected by Scale. In this role, you&#39;ll advance Robotic research, shape Scale&#39;s robotics offerings, and expand the frontier of Robotics data and model evaluation.</p>\n<p>Key responsibilities include: Collaborating closely with Robotics customers to drive the industry forward in using VLA data Developing ML pipelines to train/fine-tune models using Scale&#39;s data Conducting research on robotics data collection, cross-embodiment training, and policy fine-tuning Developing novel methods for evaluating VLA models, including new robotics industry benchmarks Partnering with cross-functional stakeholders and Scale&#39;s customers to improve data collection Collaborating with product teams to bring ML outcomes to Scale&#39;s platform</p>\n<p>You&#39;ll have: Practical experience building training VLA models and/or building robotics data 3+ years of relevant industry experience in areas relating to: robotics, computer vision, embodied AI, sim-to-real, imitation learning, reinforcement learning, and vision language actions models PhD or equivalent experience in Machine Learning or Robotics A track record of published research in robotics Experience conducting data collection and performing evaluations Strong written and verbal communication skills and the ability to work with cross-functional teams and customers Intellectual curiosity, empathy, and ability to operate with a high degree of autonomy</p>\n<p>Nice to haves: Experience working with robotics hardware platforms (robotic arms, perception systems, etc.) Experience deploying machine learning models on robotic systems in the field Experience with teleoperated or human-driven data for robotics (ALOHA, UMI, hand tracking, etc.)</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_2da35265-f56","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale","sameAs":"https://www.scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4600908005","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$248,800-$311,000 USD","x-skills-required":["Machine Learning","Robotics","Computer Vision","Embodied AI","Sim-to-Real","Imitation Learning","Reinforcement Learning","Vision Language Actions Models"],"x-skills-preferred":["Robotics Hardware Platforms","Deploying Machine Learning Models","Teleoperated or Human-Driven Data"],"datePosted":"2026-04-18T15:58:56.349Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, Robotics, Computer Vision, Embodied AI, Sim-to-Real, Imitation Learning, Reinforcement Learning, Vision Language Actions Models, Robotics Hardware Platforms, Deploying Machine Learning Models, Teleoperated or Human-Driven Data","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":248800,"maxValue":311000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_9dfc8dc1-ef4"},"title":"Senior Machine Learning Scientist","description":"<p>We are looking for a Senior Machine Learning Scientist to join our AI Group in Berlin. As a Senior Machine Learning Scientist, you will be responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers&#39; hands. You will work in partnership with Product and Design functions of teams we support. Our team&#39;s dedicated ML product engineers enable us to move to production fast, often shipping to beta in weeks after a successful offline test. We are passionate about applying machine learning technology, and have productized everything from classic supervised models, to cutting-edge unsupervised clustering algorithms, to novel applications of transformer neural networks. We test and measure the real customer impact of each model we deploy.</p>\n<p>Your responsibilities will include identifying areas where ML can create value for our customers, identifying the right ML framing of product problems, working with teammates and Product and Design stakeholders, conducting exploratory data analysis and research, deeply understanding the problem area, researching and identifying the right algorithms and tools, being pragmatic, but innovating right to the cutting-edge when needed, performing offline evaluation to gather evidence an algorithm will work, working with engineers to bring prototypes to production, planning, measuring &amp; socializing learnings to inform iteration, and partnering deeply with the rest of team, and others, to build excellent ML products.</p>\n<p>To be successful in this role, you will need to have broad applied machine learning knowledge, 3-5 years applied ML experience, practical stats knowledge (experiment design, dealing with confounding etc), intermediate programming skills, strong communication skills, both within engineering teams and across disciplines, comfort with ambiguity, typically have advanced education in ML or related field (e.g. MSc), and scientific thinking skills. Bonus skills and attributes include track record shipping ML products, PhD or other experience in a research environment, deep experience in an applicable ML area. e.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering, strong stats or math background, visualization, data skills, SQL, matplotlib, etc.</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_9dfc8dc1-ef4","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Intercom","sameAs":"https://www.intercom.com/","logo":"https://logos.yubhub.co/intercom.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/intercom/jobs/7372016","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Broad applied machine learning knowledge","3-5 years applied ML experience","Practical stats knowledge (experiment design, dealing with confounding etc)","Intermediate programming skills","Strong communication skills, both within engineering teams and across disciplines"],"x-skills-preferred":["Track record shipping ML products","PhD or other experience in a research environment","Deep experience in an applicable ML area. e.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering","Strong stats or math background","Visualization, data skills, SQL, matplotlib, etc."],"datePosted":"2026-04-18T15:58:02.443Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Berlin, Germany"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Broad applied machine learning knowledge, 3-5 years applied ML experience, Practical stats knowledge (experiment design, dealing with confounding etc), Intermediate programming skills, Strong communication skills, both within engineering teams and across disciplines, Track record shipping ML products, PhD or other experience in a research environment, Deep experience in an applicable ML area. e.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering, Strong stats or math background, Visualization, data skills, SQL, matplotlib, etc."},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_3238a958-3d9"},"title":"AI Product Manager","description":"<p>We&#39;re looking for an AI Product Manager to own one of the Agent &amp; Reinforcement Learning Environments data verticals, with a focus on Computer Using Agent (CUA) data.</p>\n<p>In this role, you&#39;ll oversee the product roadmap for your data vertical, owning &#39;data as a product&#39;, pipelines for data generation and quality, and researcher-facing tools that help labs train and evaluate intelligent agents in complex environments.</p>\n<p>You&#39;ll work directly with Scale&#39;s most important customers and their leading researchers, representing Scale as the technical expert for your products and influencing both internal and external roadmaps.</p>\n<p>The ideal candidate brings together a strong entrepreneurial &amp; go-to-market mindset, technical depth, and a sense for AI research, enabling them to get in front of technical stakeholders to drive mission-critical outcomes.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Own the roadmap for the Agent &amp; RL Environment Data vertical, setting product direction and driving execution across engineering, operations, and go-to-market teams.</li>\n</ul>\n<ul>\n<li>Build technical partnerships with research teams at leading AI labs, identifying insights that shape new product lines and competitive strategies for your vertical.</li>\n</ul>\n<ul>\n<li>Design, experiment with, and deliver high-quality data pipelines, tooling, and evaluation frameworks that advance RL and agentic model capabilities.</li>\n</ul>\n<ul>\n<li>Scope out and scale the creation of RL environments that simulate real-world use cases.</li>\n</ul>\n<ul>\n<li>Collaborate cross-functionally, influencing business priorities and diving in the weeds of research, operations, and customer interactions.</li>\n</ul>\n<p>Ideally, You&#39;d Have:</p>\n<ul>\n<li>Entrepreneurial mindset: A builder excited by ambiguity and motivated to create new products from the ground up.</li>\n</ul>\n<ul>\n<li>6+ years of experience in product management or a customer-facing role.</li>\n</ul>\n<ul>\n<li>Technical fluency: Software engineering background (a degree in computer science or equivalent experience).</li>\n</ul>\n<ul>\n<li>Understanding of reinforcement learnings, simulation environments, or data pipelines for model training and evaluation</li>\n</ul>\n<ul>\n<li>Strong customer intuition and the ability to translate technical requirements into impactful product decisions.</li>\n</ul>\n<ul>\n<li>Bias for action and comfort wearing multiple hats and operating in fast-moving environments.</li>\n</ul>\n<p>Compensation packages at Scale for eligible roles include base salary, equity, and benefits. 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.</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_3238a958-3d9","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale","sameAs":"https://scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4609736005","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$216,000-$270,000 USD","x-skills-required":["reinforcement learnings","simulation environments","data pipelines","model training","evaluation frameworks"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:57:37.306Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"reinforcement learnings, simulation environments, data pipelines, model training, evaluation frameworks","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":216000,"maxValue":270000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_8871a994-591"},"title":"Machine Learning Engineer, Core Engineering","description":"<p>We&#39;re seeking a talented Machine Learning Engineer to join our Core Engineering team. As a Machine Learning Engineer at Pinterest, you will build cutting-edge technology using the latest advances in deep learning and machine learning to personalize Pinterest. You will partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces, while gaining knowledge of how ML works in different areas.</p>\n<p>Key Responsibilities:</p>\n<ul>\n<li>Build cutting-edge technology using the latest advances in deep learning and machine learning to personalize Pinterest</li>\n<li>Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas</li>\n<li>Use data-driven methods and leverage the unique properties of our data to improve candidate retrieval</li>\n<li>Work in a high-impact environment with quick experimentation and product launches</li>\n<li>Keep up with industry trends in recommendation systems</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)</li>\n<li>End-to-end hands-on experience with building data processing pipelines, large-scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)</li>\n<li>Degree in computer science, machine learning, statistics, or related field</li>\n</ul>\n<p>Nice to Have:</p>\n<ul>\n<li>M.S. or PhD in Machine Learning or related areas</li>\n<li>Publications at top ML conferences</li>\n<li>Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring</li>\n<li>Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration</li>\n<li>Expertise in scalable real-time systems that process stream data</li>\n<li>Passion for applied ML and the Pinterest product</li>\n</ul>\n<p>Relocation Statement:</p>\n<p>This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.</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_8871a994-591","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Pinterest","sameAs":"https://www.pinterest.com/","logo":"https://logos.yubhub.co/pinterest.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/pinterest/jobs/6121450","x-work-arrangement":"remote","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$138,905-$285,982 USD","x-skills-required":["machine learning","deep learning","data processing pipelines","large-scale machine learning systems","big data technologies","Hadoop","Spark","natural language processing","reinforcement learning","graph representation learning"],"x-skills-preferred":["Cursor","Copilot","Codex","LLM-powered productivity tools","scalable real-time systems","stream data"],"datePosted":"2026-04-18T15:57:30.186Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US; Remote, US"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"machine learning, deep learning, data processing pipelines, large-scale machine learning systems, big data technologies, Hadoop, Spark, natural language processing, reinforcement learning, graph representation learning, Cursor, Copilot, Codex, LLM-powered productivity tools, scalable real-time systems, stream data","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":138905,"maxValue":285982,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_1ccfb615-468"},"title":"Senior Machine Learning Engineer, Public Sector","description":"<p>We are seeking a Senior Machine Learning Engineer to join our Public Sector team. As a Senior Machine Learning Engineer, you will leverage techniques in generative AI, computer vision, reinforcement learning, and agentic AI to improve Scale&#39;s products and customer experience in production environments.</p>\n<p>Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like Donovan and Thunderforge. You will take state-of-the-art models developed internally and from the community, use them in production to solve problems for our customers and taskers.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Improving and maintaining production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics</li>\n<li>Collaborating with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines</li>\n<li>Working with massive datasets to develop both generic models as well as fine-tune models for specific products</li>\n<li>Building scalable machine learning infrastructure to automate and optimize our ML services</li>\n<li>Serving as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations</li>\n</ul>\n<p>Ideal candidates will have extensive experience using computer vision, deep learning, and deep reinforcement learning, or natural language processing in a production environment. Solid background in algorithms, data structures, and object-oriented programming is also required.</p>\n<p>Nice to haves include a graduate degree in Computer Science, Machine Learning, or Artificial Intelligence specialization, experience working with cloud platforms, and familiarity with ML evaluation frameworks and agentic model design.</p>\n<p>Compensation packages at Scale for eligible roles include base salary, equity, and benefits. 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.</p>\n<p>You&#39;ll also receive benefits including comprehensive health, dental, and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. This role may be eligible for additional benefits such as a commuter stipend.</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_1ccfb615-468","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale","sameAs":"https://scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4281519005","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$216,300-$300,300 USD","x-skills-required":["computer vision","deep learning","deep reinforcement learning","natural language processing","algorithms","data structures","object-oriented programming","Python","TensorFlow","PyTorch"],"x-skills-preferred":["graduate degree in Computer Science, Machine Learning, or Artificial Intelligence specialization","experience working with cloud platforms","familiarity with ML evaluation frameworks and agentic model design"],"datePosted":"2026-04-18T15:56:56.828Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; New York, NY; Washington, DC"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"computer vision, deep learning, deep reinforcement learning, natural language processing, algorithms, data structures, object-oriented programming, Python, TensorFlow, PyTorch, graduate degree in Computer Science, Machine Learning, or Artificial Intelligence specialization, experience working with cloud platforms, familiarity with ML evaluation frameworks and agentic model design","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":216300,"maxValue":300300,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_fe04c8cc-782"},"title":"Forward Deployed Engineering Manager","description":"<p>Shape the Future of AI</p>\n<p>At Labelbox, we&#39;re building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. 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. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications.</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_fe04c8cc-782","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/5101195007","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$180,000-$220,000 USD","x-skills-required":["Software engineering experience (Python)","Containerization and sandboxing (Docker, Firecracker, or similar)","Reinforcement learning fundamentals (MDPs, reward design, episode structure, observation/action spaces)","Infrastructure, developer tooling, or distributed systems","Debugging skills and systems thinking"],"x-skills-preferred":["Experience building or working with RL environments (Gym, PettingZoo) or agent benchmarks (SWE-bench, WebArena, OSWorld, TerminalBench)","Familiarity with cloud infrastructure (GCP or AWS)","Prior experience in AI/ML platforms, data companies, or research environments","Contributions to open-source projects in RL, agents, or developer tooling"],"datePosted":"2026-04-18T15:56:05.491Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco Bay Area"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Software engineering experience (Python), Containerization and sandboxing (Docker, Firecracker, or similar), Reinforcement learning fundamentals (MDPs, reward design, episode structure, observation/action spaces), Infrastructure, developer tooling, or distributed systems, Debugging skills and systems thinking, Experience building or working with RL environments (Gym, PettingZoo) or agent benchmarks (SWE-bench, WebArena, OSWorld, TerminalBench), Familiarity with cloud infrastructure (GCP or AWS), Prior experience in AI/ML platforms, data companies, or research environments, Contributions to open-source projects in RL, agents, or developer tooling","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":180000,"maxValue":220000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_fab21c7e-6bf"},"title":"Research Engineer / Scientist, Alignment Science - London","description":"<p>About the role:</p>\n<p>You will contribute to exploratory experimental research on AI safety, with a focus on risks from powerful future systems. As a Research Engineer on Alignment Science, you&#39;ll work on creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Conduct research on AI control and alignment stress-testing</li>\n<li>Develop and implement new techniques for ensuring AI safety</li>\n<li>Collaborate with other teams, including Interpretability, Fine-Tuning, and the Frontier Red Team</li>\n<li>Test and evaluate the effectiveness of AI safety techniques</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Significant software, ML, or research engineering experience</li>\n<li>Familiarity with technical AI safety research</li>\n<li>Experience contributing to empirical AI research projects</li>\n</ul>\n<p>Preferred qualifications:</p>\n<ul>\n<li>Experience authoring research papers in machine learning, NLP, or AI safety</li>\n<li>Experience with LLMs</li>\n<li>Experience with reinforcement learning</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Competitive compensation and benefits</li>\n<li>Optional equity donation matching</li>\n<li>Generous vacation and parental leave</li>\n<li>Flexible working hours</li>\n</ul>\n<p>Note:</p>\n<p>This role requires all candidates to be based at least 25% in London and travel to San Francisco occasionally.</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_fab21c7e-6bf","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/4610158008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"£260,000-£370,000 GBP","x-skills-required":["software engineering","machine learning","research engineering","AI safety","technical AI safety research"],"x-skills-preferred":["research paper authoring","LLMs","reinforcement learning"],"datePosted":"2026-04-18T15:55:40.617Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, machine learning, research engineering, AI safety, technical AI safety research, research paper authoring, LLMs, reinforcement learning","baseSalary":{"@type":"MonetaryAmount","currency":"GBP","value":{"@type":"QuantitativeValue","minValue":260000,"maxValue":370000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c9ab5cbc-dd6"},"title":"Research Engineer, Performance RL","description":"<p>We&#39;re hiring a Research Engineer to join our Code RL team within the RL organization. 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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. Familiarity with languages such as Python, Scala, Spark and libraries such as Pandas, NumPy, and Scikit-learn.</li>\n<li>Expertise in areas of machine learning such as supervised and unsupervised learning techniques, ML operations, and possibly experience in Large Language Models or Reinforcement Learning.</li>\n<li>Demonstrated ability to work on collaborative projects, with experience in receiving and applying feedback from various stakeholders.</li>\n<li>A proactive approach to learning unfamiliar systems and a demonstrated ability to understand complex systems independently.</li>\n</ul>\n<p><strong>What We Offer</strong></p>\n<ul>\n<li>Join us for an unforgettable summer internship and help shape the future of global commerce.</li>\n<li>At Stripe, you won&#39;t just be working on theoretical projects; you&#39;ll make a tangible impact on the world&#39;s economic infrastructure.</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_01819c10-867","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Stripe","sameAs":"https://stripe.com/","logo":"https://logos.yubhub.co/stripe.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/stripe/jobs/7216664","x-work-arrangement":"remote","x-experience-level":"entry","x-job-type":"internship","x-salary-range":null,"x-skills-required":["Python","Scala","Spark","Pandas","NumPy","Scikit-learn","Supervised learning","Unsupervised learning","ML operations","Large Language Models","Reinforcement Learning"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:53:40.222Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, New York City, Seattle"}},"jobLocationType":"TELECOMMUTE","employmentType":"INTERN","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Scala, Spark, Pandas, NumPy, Scikit-learn, Supervised learning, Unsupervised learning, ML operations, Large Language Models, Reinforcement Learning"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_ed5725bb-311"},"title":"Applied Research Engineer, Agents","description":"<p>Shape the Future of AI</p>\n<p>At Labelbox, we&#39;re building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. 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>As an Applied Research Engineer at Labelbox, you&#39;ll sit at the junction of advanced AI research and real product impact, with a focus on the data that makes modern agents work,browser interactions, SWE/code traces, GUI sessions, and multi-turn workflows. You&#39;ll drive the data landscape required to advance capable, adaptable agents and help shape Labelbox&#39;s strategy for collecting, synthesizing, and evaluating it.</p>\n<p>Create frameworks and tools to construct, train, benchmark and evaluate autonomous agent capabilities.</p>\n<p>Design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies.</p>\n<p>Develop data pipelines from diverse sources like code repositories, web browsers, and computer systems.</p>\n<p>Implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models.</p>\n<p>Engage with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs for frontier models and share best practices.</p>\n<p>Collaborate closely with frontier AI lab customers to understand requirements and guide model development.</p>\n<p>Publish research findings in academic journals, conferences, and blog posts.</p>\n<p>What You Bring</p>\n<p>Ph.D. or Master&#39;s degree in Computer Science, Machine Learning, AI, or related field.</p>\n<p>At least 3 years of experience addressing sophisticated ML problems with successful delivery to customers.</p>\n<p>Experience building and training autonomous agents,tool use, structured outputs, multi-step planning,across browsers/GUI, codebases, and databases using SFT and RL.</p>\n<p>Constructed and evaluated agentic benchmarks (e.g. SWE-bench, WebArena, τ-bench, OSWorld) and reliability/efficiency suites (e.g. WABER).</p>\n<p>Adept at interpreting research literature and quickly turning new ideas into prototypes.</p>\n<p>Deep understanding of frontier models (autoregressive, diffusion), post-training (SFT, RLVR, RLAIF, RLHF, et al.), and their human data requirements.</p>\n<p>Proficient in Python, data science libraries and deep learning frameworks (e.g., PyTorch, JAX, TensorFlow).</p>\n<p>Strong analytical and problem-solving abilities in ambiguous situations.</p>\n<p>Excellent communication skills.</p>\n<p>Track record of publications in top-tier AI/ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, etc.).</p>\n<p>Labelbox Applied Research</p>\n<p>At Labelbox Applied Research, we&#39;re committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced human-AI interaction techniques. 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. 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 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_272bd1ad-99d","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/5083039008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$300,000-$405,000 USD","x-skills-required":["software engineering","infrastructure expertise","developer experience","API design","error propagation","documentation","distributed systems","complex systems","reliability","monitoring","root cause analysis","preventive measures","testing","observability","collaboration","communication"],"x-skills-preferred":["founder","early engineer","security","sandboxing","isolation technologies","open-source contributions","developer tools","incident response","on-call rotations","reinforcement learning","model training infrastructure"],"datePosted":"2026-04-18T15:51:53.000Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, infrastructure expertise, developer experience, API design, error propagation, documentation, distributed systems, complex systems, reliability, monitoring, root cause analysis, preventive measures, testing, observability, collaboration, communication, founder, early engineer, security, sandboxing, isolation technologies, open-source contributions, developer tools, 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_7b627879-34d"},"title":"Anthropic Fellows Program — Reinforcement Learning","description":"<p>We are seeking highly motivated individuals to join our Anthropic Fellows Program, a 4-month full-time research opportunity focused on reinforcement learning. As a fellow, you will work on an empirical project aligned with our research priorities, with the goal of producing a public output such as a paper submission.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Work on an empirical project aligned with our research priorities</li>\n<li>Produce a public output such as a paper submission</li>\n<li>Collaborate with our research team</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Strong technical background in computer science, mathematics, or physics</li>\n<li>Fluent in Python programming</li>\n<li>Available to work full-time on the Fellows program</li>\n</ul>\n<p>Preferred qualifications include experience in areas of research or engineering related to reinforcement learning, and strong software engineering skills with experience building complex ML systems.</p>\n<p>As a fellow, you will have access to a shared workspace in London, direct mentorship from our researchers, and a weekly stipend of $3,850 USD / £2,310 GBP / $4,300 CAD. You will also have the opportunity to collaborate with our research team and contribute to the development of our AI systems.</p>\n<p>Logistics:</p>\n<ul>\n<li>To participate in the Fellows program, you must have work authorization in the UK and be located in the UK during the program.</li>\n<li>We have designated shared workspaces in London where fellows will work from and mentors will visit.</li>\n<li>We are not currently able to sponsor visas for fellows.</li>\n</ul>\n<p>Please note that we do not guarantee that we will make any full-time offers to fellows. However, strong performance during the program may indicate that a Fellow would be a good fit for full-time roles at Anthropic.</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_7b627879-34d","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://anthropic.co/","logo":"https://logos.yubhub.co/anthropic.co.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5183052008","x-work-arrangement":"onsite","x-experience-level":"entry","x-job-type":"full-time","x-salary-range":"$3,850 USD / £2,310 GBP / $4,300 CAD per week","x-skills-required":["Python","Reinforcement learning","Machine learning","Computer science","Mathematics","Physics"],"x-skills-preferred":["Software engineering","Complex ML systems","Research or engineering related to reinforcement learning"],"datePosted":"2026-04-18T15:51:11.089Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK; Ontario, CAN; Remote-Friendly, United States; San Francisco, CA"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Reinforcement learning, Machine learning, Computer science, Mathematics, Physics, Software engineering, Complex ML systems, Research or engineering related to reinforcement learning","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":2310,"maxValue":4300,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_854e95b5-76b"},"title":"Sr. Director of Product, Research and Training Infrastructure","description":"<p>CoreWeave is seeking a visionary Sr. Director of Product, Research Training Infrastructure to lead the product strategy and engineering execution for the services that power the most ambitious AI research labs in the world.</p>\n<p>This executive leader will own the product strategy and engineering execution for the Research Training Stack, focusing on the specialized orchestration, evaluation, and iteration tools required for massive-scale pre-training and post-training.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Frontier Orchestration: Oversee the evolution of SUNK (Slurm on Kubernetes) to provide researchers with deterministic, bare-metal performance through a cloud-native interface.</li>\n</ul>\n<ul>\n<li>Holistic Training Services: Drive the development of next-generation orchestrators and automated training-based evaluation frameworks that ensure model quality throughout the lifecycle.</li>\n</ul>\n<ul>\n<li>Post-Training Excellence: Build the infrastructure required for sophisticated Reinforcement Learning (RL) and RLHF pipelines, enabling labs to refine foundation models with maximum efficiency.</li>\n</ul>\n<ul>\n<li>Customer Advocacy: Act as the primary technical partner for lead researchers at global AI labs, translating their &#39;future-state&#39; requirements into actionable product roadmaps.</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>Proven leadership experience in engineering leadership, with at least 5+ years managing large-scale infrastructure at a top-tier research lab or an AI-native cloud provider.</li>\n</ul>\n<ul>\n<li>Deep, hands-on knowledge of Slurm, Kubernetes, and the specific networking requirements (InfiniBand/RDMA) for distributed training clusters.</li>\n</ul>\n<ul>\n<li>Research mindset and understanding of the &#39;pain points&#39; of a research scientist.</li>\n</ul>\n<ul>\n<li>Scaling experience delivering mission-critical services on multi-thousand GPU clusters (H100/Blackwell/Rubin architectures).</li>\n</ul>\n<ul>\n<li>Strategic vision to define &#39;what&#39;s next&#39; in the AI stack, from automated RL loops to specialized sandbox environments.</li>\n</ul>\n<p>Why CoreWeave?</p>\n<p>In 2026, CoreWeave is the foundation of the largest infrastructure buildout in human history. We are building AI Factories, not just data centers.</p>\n<ul>\n<li>Silicon-Up Innovation: Work directly with the latest NVIDIA architectures.</li>\n</ul>\n<ul>\n<li>Impact: You will be the architect of the environment that enables the next new discovery.</li>\n</ul>\n<p>Velocity: We move at the speed of the researchers we support, bypassing legacy cloud bottlenecks to deliver raw power.</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_854e95b5-76b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"CoreWeave","sameAs":"https://www.coreweave.com","logo":"https://logos.yubhub.co/coreweave.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/coreweave/jobs/4665964006","x-work-arrangement":"hybrid","x-experience-level":"executive","x-job-type":"full-time","x-salary-range":"$233,000 to $341,000","x-skills-required":["Slurm","Kubernetes","InfiniBand/RDMA","Distributed training clusters","GPU clusters","H100/Blackwell/Rubin architectures","Reinforcement Learning (RL)","RLHF pipelines"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:50:11.130Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Livingston, NJ / New York, NY / Sunnyvale, CA / Bellevue, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Slurm, Kubernetes, InfiniBand/RDMA, Distributed training clusters, GPU clusters, H100/Blackwell/Rubin architectures, Reinforcement Learning (RL), RLHF pipelines","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":233000,"maxValue":341000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_46c30960-10d"},"title":"AI Applied Scientist","description":"<p>We&#39;re looking for applied scientists with a Machine Learning and Artificial Intelligence background to build AI technologies and make Figma products more magical. 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You will be combining industry best practices and a first-principles approach to design and build AI/ML models and systems to improve Figma&#39;s products.</p>\n<p>What you&#39;ll do at Figma:</p>\n<ul>\n<li>Drive fundamental and applied research in AI.</li>\n<li>Explore the boundaries of what is possible with the current technology set to build best in class models for Figma&#39;s domains.</li>\n<li>Combine industry best practices and a first-principles approach to build cutting edge Generative AI models, using techniques like Supervised Finetuning (SFT), Reinforcement Learning (RL), prompt improvements and synthetic data generation.</li>\n<li>Work in concert with product and infrastructure engineers to improve Figma&#39;s products through AI powered features.</li>\n<li>Collaborate closely with product managers and engineers to transform user feedback into requirements for AI systems.</li>\n<li>Build evaluation systems to measure and improve quality of AI features in Figma products.</li>\n</ul>\n<p>We&#39;d love to hear from you if you have:</p>\n<ul>\n<li>Extensive experience in building generative AI features through prompt engineering, and fine tuning models in production environments.</li>\n<li>Experience working on deep learning and generative AI frameworks like PyTorch, JAX, HuggingFace etc.</li>\n<li>Experience training LLMs with Reinforcement Learning techniques such as preference-based RL (DPO, PPO) and/or RL with verifiable rewards (RLVR) such as GRPO/DAPO.</li>\n<li>4+ years in Generative AI, and 6+ years of experience in one or more of the following areas: machine learning, natural language processing/understanding, computer vision.</li>\n<li>Strong software engineering skills with 5+ years of experience in programming languages (Python, C++, Java or R).</li>\n<li>Experience communicating and working across functions to drive solutions.</li>\n</ul>\n<p>While not required, It’s an added plus if you also have:</p>\n<ul>\n<li>Proven track record of planning multi-year roadmap in which shorter-term projects ladder to the long-term vision.</li>\n<li>Experience in mentoring/influencing senior engineers across organizations.</li>\n<li>Expertise working on large scale and distributed AI training.</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_46c30960-10d","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Figma","sameAs":"https://www.figma.com/","logo":"https://logos.yubhub.co/figma.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/figma/jobs/5707966004","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$153,000-$376,000 USD","x-skills-required":["Generative AI","Machine Learning","Artificial Intelligence","Deep Learning","PyTorch","JAX","HuggingFace","Reinforcement Learning","Natural Language Processing","Computer Vision","Python","C++","Java","R"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:49:15.355Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA • New York, NY • United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Generative AI, Machine Learning, Artificial Intelligence, Deep Learning, PyTorch, JAX, HuggingFace, Reinforcement Learning, Natural Language Processing, Computer Vision, Python, C++, Java, R","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":153000,"maxValue":376000,"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. The programs require building and fielding a resilient, software-defined spacecraft systems across numerous mission threads.</p>\n<p>We work with mission partners and customers to deploy reliable and robust capabilities on operationally relevant fielding timelines to meet complex challenges across the DOD and IC.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Develop model-driven analysis (and often the models themselves)</li>\n<li>Develop a deep understanding of transformational value of Anduril platform, autonomy, simulation, and perception products</li>\n<li>Use our advanced internal M&amp;S capabilities to give our analyses an asymmetric advantage over competitors</li>\n<li>Convey the value through life of AI-powered distributed systems over traditional industrial offerings</li>\n<li>Provide rigorous analytical products which will be used to guide hardware engineering and convince stakeholders of our value advantage</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>Currently possesses and is able to maintain an active U.S. Secret security clearance</li>\n<li>Space Mission MS&amp;A experience</li>\n<li>For example, working knowledge of constellation design, space trajectory design, and physics-level modeling across a wide variety of space missions</li>\n<li>Spacecraft &amp; Payload MS&amp;A experience, ability to perform physics-based trade-space analyses for spacecraft propulsion, power, thermal management, and payloads such as RADAR, SATCOM, and/or EOIR</li>\n<li>Outstanding communication skills to include visual presentation of complex data</li>\n<li>Strong background in orbital mechanics and space environment</li>\n<li>Proficiency with physics-math scripting (e.g., MATLAB, SIMULINK, Python+numpy, scipy)</li>\n<li>Strong engineering background, preferably in Astrodynamics, Aerospace Engineering, Dynamics and Controls Engineering, or other related engineering field</li>\n</ul>\n<p><strong>Preferred Qualifications</strong></p>\n<ul>\n<li>3+ years in a relevant Science/Engineering field</li>\n<li>Rendezvous Proximity Operations (RPO) Trajectory Design &amp; Space Payloads experience</li>\n<li>Exceptional proficiency with physics-math scripting of your choice (e.g., MATLAB, SIMULINK, Python+numpy, scipy)</li>\n<li>Proficiency with STK and Astrogator</li>\n<li>Experience with genetic algorithms, machine learning, AI, and reinforcement learning algorithms</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_5038042b-80a","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/4984579007","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$166,000-$220,000 USD","x-skills-required":["Space Mission MS&A experience","Constellation design","Space trajectory design","Physics-level modeling","Spacecraft & Payload MS&A experience","Orbital mechanics","Space environment","Physics-math scripting","MATLAB","SIMULINK","Python","numpy","scipy","Astrodynamics","Aerospace Engineering","Dynamics and Controls Engineering"],"x-skills-preferred":["Rendezvous Proximity Operations (RPO) Trajectory Design & Space Payloads experience","Genetic algorithms","Machine learning","AI","Reinforcement learning algorithms"],"datePosted":"2026-04-18T15:48:19.902Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Costa Mesa, California, United States"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Space Mission MS&A experience, Constellation design, Space trajectory design, Physics-level modeling, Spacecraft & Payload MS&A experience, Orbital mechanics, Space environment, Physics-math scripting, MATLAB, SIMULINK, Python, numpy, scipy, Astrodynamics, Aerospace Engineering, Dynamics and Controls Engineering, Rendezvous Proximity Operations (RPO) Trajectory Design & Space Payloads experience, Genetic algorithms, Machine learning, AI, Reinforcement learning algorithms","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":166000,"maxValue":220000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_41c3ee08-08e"},"title":"Optimization Software Engineer","description":"<p>We are looking for a talented mid-level Software Engineer with a strong background in optimization to join our growing team at Anduril Labs. In this role, you will be instrumental in developing advanced algorithms and software solutions to tackle complex, multi-domain optimization problems critical to national defense and Anduril&#39;s autonomous systems.</p>\n<p>The ideal candidate possesses deep expertise in classical optimization algorithms, robust Python programming skills, and a solid foundation in data modeling. Experience with developing hybrid quantum optimization solutions is a plus.</p>\n<p>You will leverage state-of-the-art, GenAI-powered development tools such as Claude Code to accelerate solution development and enhance our optimization software. This role demands creative problem-solving, a self-starter mentality, and the ability to rapidly apply algorithmic theory and mathematic modeling to practical, real-world optimization challenges.</p>\n<p>You will be designing, implementing, and deploying optimization algorithms and services that integrate seamlessly into larger defense systems, working across various platforms (on-prem, cloud, and hybrid quantum computing environments).</p>\n<p>Familiarity with modeling linear and non-linear optimization problems, rapid prototyping, integrating optimization solutions into existing architectures, leveraging APIs, and utilizing open-source tools will be crucial.</p>\n<p>If you thrive in a dynamic environment that values creative problem-solving, love writing code, excel as both an individual contributor and team player, are eager to learn, and bring a can-do attitude, this role is for you.</p>\n<p>Key Responsibilities:</p>\n<ul>\n<li>Design, develop, and implement highly efficient 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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>Responsibilities:</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>You may be a good fit if you:</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 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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This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction.</p>\n<p>You will 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>Key responsibilities include:</p>\n<ul>\n<li>Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters.</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>You may be a good fit if you:</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 candidates may have:</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 candidates need not have:</p>\n<ul>\n<li>Formal certifications or education credentials.</li>\n<li>Academic research experience or publication history.</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_b210c75f-2d9","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/5115935008","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"£260,000-£630,000 GBP","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":[],"datePosted":"2026-04-18T15:46:19.569Z","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++","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_26b9d76f-c85"},"title":"Research Engineer, Universes","description":"<p>We&#39;re looking for Research Engineers to help us build the next generation of training environments for capable and safe agentic AI.</p>\n<p>This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to research direction. You&#39;ll work on fundamental research in reinforcement learning, designing training environments and methodologies that push the state of the art, and building evaluations that measure genuine capability.</p>\n<p>Responsibilities:</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>You may be a good fit if you:</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 candidates may also have one or more of the following:</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>The annual compensation range for this role is $500,000-$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_26b9d76f-c85","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/5061517008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$500,000-$850,000 USD","x-skills-required":["Reinforcement learning","Training environments","ML stacks","Software engineering","Pair programming"],"x-skills-preferred":["Large language model training","RL environments","Simulation systems","Distributed systems"],"datePosted":"2026-04-18T15:46:02.776Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NY"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Reinforcement learning, Training environments, ML stacks, Software engineering, Pair programming, Large language model training, RL environments, Simulation systems, Distributed systems","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_214894f2-136"},"title":"Research Engineer, Virtual Collaborator (Cowork)","description":"<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.</p>\n<p>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>Responsibilities:</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</ul>\n<ul>\n<li>Designing and implementing reinforcement learning pipelines targeted at virtual collaborator use cases (productivity, organisational navigation, vertical domains)</li>\n</ul>\n<ul>\n<li>Building and scaling our data creation platform for generating high-quality, open-ended tasks with domain experts and crowdworkers</li>\n</ul>\n<ul>\n<li>Integrating real organisational data to create realistic training environments</li>\n</ul>\n<ul>\n<li>Developing robust evaluation systems that maintain quality while avoiding reward hacking</li>\n</ul>\n<ul>\n<li>Partnering directly with product teams (e.g., Cowork, claude.ai) to ensure training aligns with product features</li>\n</ul>\n<p>You may be a good fit if you:</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</ul>\n<ul>\n<li>Have 5-8 years of strong machine learning experience</li>\n</ul>\n<ul>\n<li>Thrive at the intersection of research and product, with a pragmatic approach to solving real-world problems</li>\n</ul>\n<ul>\n<li>Are comfortable with ambiguity and can balance research rigor with shipping deadlines</li>\n</ul>\n<ul>\n<li>Enjoy collaborating across multiple teams (data operations, model training, product)</li>\n</ul>\n<ul>\n<li>Can context-switch between research problems and product engineering tasks</li>\n</ul>\n<ul>\n<li>Care about making AI genuinely helpful for everyday enterprise workflows</li>\n</ul>\n<p>Strong candidates may also have experience with:</p>\n<ul>\n<li>Creating RL envs for realistic tasks</li>\n</ul>\n<ul>\n<li>Reward modelling and preventing reward hacking</li>\n</ul>\n<ul>\n<li>Building human-in-the-loop training systems or crowdsourcing platforms</li>\n</ul>\n<ul>\n<li>Working with enterprise tools and APIs (Google Workspace, Microsoft Office, Slack, etc.)</li>\n</ul>\n<ul>\n<li>Developing evaluation frameworks for open-ended tasks</li>\n</ul>\n<ul>\n<li>Domain expertise in finance, legal, or healthcare workflows</li>\n</ul>\n<ul>\n<li>Creating scalable data pipelines with quality control mechanisms</li>\n</ul>\n<ul>\n<li>Translating product requirements into technical training objectives</li>\n</ul>\n<p>Deadline to apply: None. Applications will be reviewed on a rolling basis.</p>\n<p>The annual compensation range for this role is $500,000-$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_214894f2-136","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/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 Creation Platform","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":"2026-04-18T15:44:42.622Z","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 Creation Platform, 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_01ff2381-5c4"},"title":"Member of Technical Staff - Reasoning","description":"<p><strong>Job Description</strong></p>\n<p>As a Member of Technical Staff at xAI, you will build frameworks to improve the reasoning capability, build distributed reinforcement learning systems, techniques for inference time compute (e.g. tree search and planning), and develop environments for agents.</p>\n<p>You will get exposure and will be expected to solve and take ownership of components across the entire stack.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Build robust and scalable distributed RL systems.</li>\n<li>Optimise frameworks to enable complex inference-time reasoning.</li>\n<li>Develop environments and harnesses for agents.</li>\n</ul>\n<p><strong>Basic Qualifications</strong></p>\n<ul>\n<li>Experienced with large-scale reinforcement learning systems.</li>\n<li>Designing and implementing distributed systems.</li>\n<li>Keeping up with state-of-the-art RL and inference time compute algorithms.</li>\n</ul>\n<p><strong>Interview Process</strong></p>\n<p>After submitting your application, the team reviews your CV and statement of exceptional work. If your application passes this stage, you will be invited to a 15 minute interview (“phone interview”) during which a member of our team will ask some basic questions. 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. 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.</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. Strong candidates may also have experience authoring research papers in machine learning, NLP, or AI safety, have experience with LLMs, have experience with reinforcement learning, and have experience with Kubernetes clusters and complex shared codebases.</p>\n<p>The annual compensation range for this role is $350,000-$500,000 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_95c5ac3a-e98","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/4631822008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000-$500,000 USD","x-skills-required":["machine learning","research engineering","AI safety","Python","Kubernetes","LLMs","reinforcement learning"],"x-skills-preferred":["authoring research papers","NLP","AI safety research"],"datePosted":"2026-04-18T15:43:50.095Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"machine learning, research engineering, AI safety, Python, Kubernetes, LLMs, reinforcement learning, authoring research papers, NLP, AI safety research","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_1bb1aad7-0aa"},"title":"Model Quality Software Engineer, Claude Code","description":"<p>We&#39;re looking for a Staff Software Engineer to set technical direction at the intersection of engineering and research on the Claude Code team. 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As a member of this team, you will work across the full stack of audio ML, developing audio codecs and representations, sourcing and synthesizing high-quality audio data, training large-scale speech language models and large audio diffusion models, and developing novel architectures for incorporating continuous signals into LLMs.</p>\n<p>Our team focuses primarily but not exclusively on speech, building advanced steerable systems spanning end-to-end conversational systems, speech and audio understanding models, and speech synthesis capabilities. 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Your primary responsibility will be to design and build infrastructure that enables researchers to rapidly iterate on reward signals. This includes tools for rubric development, human feedback data analysis, and reward robustness evaluation. You will also develop systems for automated quality assessment of rewards, including detection of reward hacks and other pathologies. Additionally, you will create tooling that allows researchers to easily compare different reward methodologies and understand their effects. You will collaborate with researchers to translate science requirements into platform capabilities and optimize existing systems for performance, reliability, and ease of use.</p>\n<p>You will have the opportunity to contribute directly to research projects yourself and have a direct impact on our ability to scale reward development across domains. 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As a Research Engineer, you&#39;ll touch all parts of our code and infrastructure, whether that&#39;s making the cluster more reliable for our big jobs, improving throughput and efficiency, running and designing scientific experiments, or improving our dev tooling.</p>\n<p>You&#39;ll be working on large-scale ML systems from the ground up, making safe, steerable, trustworthy systems. 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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>You may be a good fit if you:</p>\n<ul>\n<li>Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).</li>\n<li>Have worked across the stack – kernels, model code, distributed systems.</li>\n<li>Know how to balance research exploration with engineering implementation.</li>\n<li>Are passionate about AI&#39;s potential and committed to developing safe and beneficial systems.</li>\n</ul>\n<p>Strong candidates may also have:</p>\n<ul>\n<li>Experience with reinforcement learning.</li>\n<li>Experience porting ML workloads between different types of accelerators.</li>\n<li>Familiarity with LLM training methodologies.</li>\n</ul>\n<p>The annual compensation range for this role is $350,000-$850,000 USD.</p>\n<p>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. 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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_1507524b-770","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":["accelerators","ML framework programming","distributed systems","reinforcement learning","LLM training methodologies"],"x-skills-preferred":["CUDA","ROCm","Triton","Pallas","JAX","PyTorch"],"datePosted":"2026-04-18T15:42:09.925Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"accelerators, ML framework programming, distributed systems, reinforcement learning, LLM training methodologies, CUDA, ROCm, Triton, Pallas, JAX, PyTorch","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_18ae1499-b22"},"title":"Research Engineer, Discovery","description":"<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.</p>\n<p>Responsibilities:</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 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As a Reinforcement Learning Fellow, you will work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g., a paper submission).</p>\n<p><strong>What to Expect</strong></p>\n<ul>\n<li>4 months of full-time research</li>\n<li>Direct mentorship from Anthropic researchers</li>\n<li>Access to a shared workspace (in either Berkeley, California or London, UK)</li>\n<li>Connection to the broader AI safety and security research community</li>\n<li>Weekly stipend of $3,850 USD / £2,310 GBP / $4,300 CAD + benefits (these vary by country)</li>\n<li>Funding for compute (~$15k/month) and other research expenses</li>\n</ul>\n<p><strong>Responsibilities</strong></p>\n<p>As a Reinforcement Learning Fellow, you will be responsible for:</p>\n<ul>\n<li>Building model-based tools to better understand AI training data and improve training data quality</li>\n<li>Conducting research and implementing solutions in areas such as RL algorithms</li>\n<li>Collaborating with other researchers and engineers to advance the state-of-the-art in reinforcement learning</li>\n</ul>\n<p><strong>Qualifications</strong></p>\n<ul>\n<li>Fluency in Python programming</li>\n<li>Strong background in a discipline relevant to reinforcement learning (e.g., computer science, mathematics, or physics)</li>\n<li>Experience in areas of research or engineering related to reinforcement learning</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p>To participate in the Fellows program, you must have work authorization in the US, UK, or Canada and be located in that country during the program.</p>\n<p><strong>How to Apply</strong></p>\n<p>Applications and interviews are managed by Constellation, our official recruiting partner for this program. Clicking &quot;Apply here&quot; will redirect you to Constellation&#39;s application portal.</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_d4e80a65-378","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://anthropic.co/","logo":"https://logos.yubhub.co/anthropic.co.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5183052008","x-work-arrangement":"remote","x-experience-level":"entry","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","Reinforcement Learning","Machine Learning","Deep Learning","Computer Science"],"x-skills-preferred":["Software Engineering","Data Analysis","Statistics","Mathematics"],"datePosted":"2026-04-18T15:40:25.711Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK; Ontario, CAN; Remote-Friendly, United States; San Francisco, CA"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Reinforcement Learning, Machine Learning, Deep Learning, Computer Science, Software Engineering, Data Analysis, Statistics, Mathematics"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d2f5b1e5-545"},"title":"Research Scientist, Gemini Safety","description":"<p>We&#39;re seeking a versatile Research Scientist to join our Gemini Safety team. As a Research Scientist, you will apply and develop data and algorithmic cutting-edge solutions to advance our latest user-facing models. 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. The successful candidate will work on post-training research in Gemini, focusing on quality of information-seeking responses. 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. International candidates: $25/hour - $75/hour depending on factors including relevant experience, skills, education, geographic location, and qualifications.</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_306a6a6f-98c","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/5040344007","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time|part-time|contract|temporary|internship","x-salary-range":"$45/hour - $100/hour","x-skills-required":["Proprietary software","Python for crypto analysis","Rust/Solidity familiarity","Machine learning models","Quantitative finance","Financial engineering","Computer science","Statistics","Applied mathematics","Economics","Physics","Operations research","Data science"],"x-skills-preferred":["Professional experience in quantitative crypto trading","Publications or public analyses in crypto quant topics","Teaching, mentoring, or content-creation experience in crypto/quant finance","Proficiency in Python for crypto analysis","Experience with backtesting crypto strategies","Knowledge of MEV, flash loans, oracle manipulation risks, liquidation cascades, or other crypto-native phenomena","CFA, FRM, CQF, or blockchain-specific certifications","Prior involvement with LLMs, reinforcement learning, or AI evaluation in financial/crypto contexts"],"datePosted":"2026-04-18T15:32:12.526Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Proprietary software, Python for crypto analysis, Rust/Solidity familiarity, Machine learning models, Quantitative finance, Financial engineering, Computer science, Statistics, Applied mathematics, Economics, Physics, Operations research, Data science, Professional experience in quantitative crypto trading, Publications or public analyses in crypto quant topics, Teaching, mentoring, or content-creation experience in crypto/quant finance, Proficiency in Python for crypto analysis, Experience with backtesting crypto strategies, Knowledge of MEV, flash loans, oracle manipulation risks, liquidation cascades, or other crypto-native phenomena, CFA, FRM, CQF, or blockchain-specific certifications, Prior involvement with LLMs, reinforcement learning, or AI evaluation in financial/crypto contexts"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_6e817b5f-6e4"},"title":"Senior Manager, Autonomy - Tactical Behaviours","description":"<p>This position is perfect for an individual who enjoys solving complex problems across diverse domains and modalities. As a Senior Manager, Autonomy - Tactical Behaviours, you will lead technical teams and support direct projects implementing autonomy algorithms for defence platforms.</p>\n<p>Your primary responsibilities will include:</p>\n<ul>\n<li><p>Leading multidisciplinary teams across autonomy, integration, and testing by aligning technical efforts, resolving cross-functional challenges, and driving mission-focused execution.</p>\n</li>\n<li><p>Designing tactical autonomy algorithms to enable unmanned aircraft to perform complex missions across air, land, and sea domains with minimal human supervision.</p>\n</li>\n<li><p>Developing high-performance software modules that incorporate planning, decision-making, and behaviour execution strategies for dynamic and adversarial environments.</p>\n</li>\n<li><p>Implementing and testing behaviour architectures that enable multi-agent coordination, target engagement, reconnaissance, and survivability in contested scenarios.</p>\n</li>\n<li><p>Working at the intersection of classical autonomy and machine learning, blending rule-based systems with learning-based methods such as reinforcement learning to achieve robust, adaptive behaviour.</p>\n</li>\n<li><p>Collaborating with cross-functional teams including perception, planning, simulation, hardware, and flight test to ensure seamless integration of autonomy solutions on real-world platforms.</p>\n</li>\n<li><p>Deploying autonomy capabilities to real platforms and participating in field tests and flight demos, validating performance in operationally relevant conditions.</p>\n</li>\n<li><p>Analyzing mission logs and performance data to diagnose failures, optimize behaviour models, and inform iterative development.</p>\n</li>\n<li><p>Contributing to the autonomy roadmap by researching and prototyping new algorithms, identifying tactical capability gaps, and proposing novel solutions that advance Shield AI&#39;s mission.</p>\n</li>\n<li><p>Supporting defence-focused programmes and customer needs by adapting autonomy solutions to evolving mission sets, compliance requirements, and operational feedback.</p>\n</li>\n<li><p>Traveling around 10-15% of the year to different office locations, customer sites, and flight integration events.</p>\n</li>\n</ul>\n<p>As a Senior Manager, Autonomy - Tactical Behaviours, you will have the opportunity to work on cutting-edge autonomy projects and contribute to the development of intelligent systems that will shape the future of defence and security.</p>\n<p>If you are a motivated and experienced professional with a passion for autonomy and defence, we encourage you to apply for this exciting opportunity.</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_6e817b5f-6e4","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Shield AI","sameAs":"https://www.shield.ai","logo":"https://logos.yubhub.co/shield.ai.png"},"x-apply-url":"https://jobs.lever.co/shieldai/b6ba7e12-0225-4883-a5b1-0aa4c7eaa183","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$229,233 - $343,849 a year","x-skills-required":["C++","Python","Real-time operating systems (RTOS)","Motion planning","Behaviour modeling","Decision-making","Autonomous system design","Unmanned system technologies","Simulation tools and environments","Strong problem-solving skills","Excellent communication and teamwork skills"],"x-skills-preferred":["Machine learning (ML)","Reinforcement learning (RL)","Collaborative behaviours","Swarm robotics","Distributed decision-making","Tactical behaviours for unmanned systems","UCI and OMS Standards"],"datePosted":"2026-04-17T13:04:47.326Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Washington, DC / Boston, MA / San Diego, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"C++, Python, Real-time operating systems (RTOS), Motion planning, Behaviour modeling, Decision-making, Autonomous system design, Unmanned system technologies, Simulation tools and environments, Strong problem-solving skills, Excellent communication and teamwork skills, Machine learning (ML), Reinforcement learning (RL), Collaborative behaviours, Swarm robotics, Distributed decision-making, Tactical behaviours for unmanned systems, UCI and OMS Standards","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":229233,"maxValue":343849,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_58422c65-bfb"},"title":"Senior Engineer, Autonomy - Tactical Behaviors","description":"<p>This position is perfect for an individual who enjoys solving complex problems across diverse domains and modalities. As a Senior Engineer, Autonomy - Tactical Behaviors, you will design tactical autonomy algorithms to enable unmanned aircraft to perform complex missions across air, land, and sea domains with minimal human supervision.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Designing tactical autonomy algorithms to enable unmanned aircraft to perform complex missions</li>\n<li>Developing high-performance software modules that incorporate planning, decision-making, and behavior execution strategies</li>\n<li>Implementing and testing behavior architectures that enable multi-agent coordination, target engagement, reconnaissance, and survivability in contested scenarios</li>\n<li>Working at the intersection of classical autonomy and machine learning, blending rule-based systems with learning-based methods such as reinforcement learning to achieve robust, adaptive behavior</li>\n<li>Collaborating with cross-functional teams to ensure seamless integration of autonomy solutions on real-world platforms</li>\n<li>Deploying autonomy capabilities to real platforms and participating in field tests and flight demos, validating performance in operationally relevant conditions</li>\n<li>Analyzing mission logs and performance data to diagnose failures, optimize behavior models, and inform iterative development</li>\n</ul>\n<p>Required qualifications include:</p>\n<ul>\n<li>BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience</li>\n<li>Proficiency in programming languages such as C++ and Python, and familiarity with real-time operating systems (RTOS)</li>\n<li>Significant background in robotics technologies related to motion planning, behavior modeling, decision-making, or autonomous system design</li>\n<li>Significant experience with unmanned system technologies and accompanying algorithms (specifically air domain)</li>\n<li>Experience with simulation tools and environments (e.g., AFSIM, NGTS) for testing and validation</li>\n</ul>\n<p>Preferred qualifications include:</p>\n<ul>\n<li>Experience applying ML/RL techniques in autonomy pipelines</li>\n<li>Background in collaborative behaviors, swarm robotics, or distributed decision-making</li>\n<li>Familiarity with tactical behaviors for unmanned systems in DoD or government programs</li>\n</ul>\n<p>Salary: $160,000 - $240,000 a year</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_58422c65-bfb","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Shield AI","sameAs":"https://www.shield.ai","logo":"https://logos.yubhub.co/shield.ai.png"},"x-apply-url":"https://jobs.lever.co/shieldai/1af75b70-43ff-457b-858b-2935e7c8983a","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$160,000 - $240,000 a year","x-skills-required":["C++","Python","Real-time operating systems (RTOS)","Robotics technologies","Unmanned system technologies","Simulation tools and environments"],"x-skills-preferred":["Machine learning (ML)","Reinforcement learning (RL)","Collaborative behaviors","Swarm robotics","Distributed decision-making","Tactical behaviors for unmanned systems"],"datePosted":"2026-04-17T13:03:58.116Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Washington, DC / Boston, MA / San Diego, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"C++, Python, Real-time operating systems (RTOS), Robotics technologies, Unmanned system technologies, Simulation tools and environments, Machine learning (ML), Reinforcement learning (RL), Collaborative behaviors, Swarm robotics, Distributed decision-making, Tactical behaviors for unmanned systems","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":160000,"maxValue":240000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_21dd4155-13b"},"title":"Senior Staff Engineer, Autonomy - Tactical Behaviors","description":"<p>This role is perfect for an individual who enjoys solving complex problems across diverse domains and modalities. As a Senior Staff Engineer, Autonomy - Tactical Behaviors, you will design tactical autonomy algorithms to enable unmanned aircraft to perform complex missions across air, land, and sea domains with minimal human supervision.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Designing tactical autonomy algorithms to enable unmanned aircraft to perform complex missions</li>\n<li>Developing high-performance software modules that incorporate planning, decision-making, and behavior execution strategies</li>\n<li>Implementing and testing behavior architectures that enable multi-agent coordination, target engagement, reconnaissance, and survivability in contested scenarios</li>\n<li>Working at the intersection of classical autonomy and machine learning, blending rule-based systems with learning-based methods such as reinforcement learning to achieve robust, adaptive behavior</li>\n<li>Collaborating with cross-functional teams to ensure seamless integration of autonomy solutions on real-world platforms</li>\n<li>Deploying autonomy capabilities to real platforms and participating in field tests and flight demos, validating performance in operationally relevant conditions</li>\n<li>Analyzing mission logs and performance data to diagnose failures, optimize behavior models, and inform iterative development</li>\n<li>Contributing to the autonomy roadmap by researching and prototyping new algorithms, identifying tactical capability gaps, and proposing novel solutions that advance Shield AI&#39;s mission</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience</li>\n<li>Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; 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As a key member of our team, you will apply and develop cutting-edge data and algorithmic solutions to ensure Gemini models are safe, maximally helpful, and work for everyone.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Post-training/instruction tuning state-of-the-art language models, 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 and work for everyone</li>\n<li>Improving Gemini&#39;s adversarial robustness, with a focus on high-stakes abuse risks</li>\n<li>Designing and maintaining high-quality evaluation protocols to assess model behaviour gaps and headroom related to safety and fairness</li>\n<li>Developing and executing experimental plans to address known gaps or construct entirely new capabilities</li>\n</ul>\n<p>To succeed in this role, you should have a PhD in Computer Science or a related field, significant LLM post-training experience, and a track record of publications at top conferences. Experience in reward modelling and reinforcement learning for LLMs instruction tuning, long-range reinforcement learning, safety, fairness, and alignment is an advantage.</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_1e0f3b52-1ae","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/7421111","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["PhD in Computer Science or a related field","Significant LLM post-training experience","Post-training/instruction tuning state-of-the-art language models","Exploring data, reasoning, and algorithmic solutions","Improving Gemini's adversarial robustness"],"x-skills-preferred":["Reward modelling and reinforcement learning for LLMs instruction tuning","Long-range reinforcement learning","Safety, fairness, and alignment"],"datePosted":"2026-03-31T18:27:50.311Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Mountain View, California, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PhD in Computer Science or a related field, Significant LLM post-training experience, Post-training/instruction tuning state-of-the-art language models, Exploring data, reasoning, and algorithmic solutions, Improving Gemini's adversarial robustness, Reward modelling and reinforcement learning for LLMs instruction tuning, Long-range reinforcement learning, Safety, fairness, and alignment"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_7896f519-fc9"},"title":"Research Scientist, Safety and Alignment for Humanoid Robotics","description":"<p>We&#39;re seeking a Research Scientist to join our Robotics team, whose mission is to build embodied AI responsibly to benefit people in the physical world. As a Research Scientist, you will design, implement, train, and evaluate large models and algorithms for humanoid robots. Your areas of focus will include algorithmic and model development to improve a robot agent&#39;s understanding of its own embodiment and VLA capabilities, learned policies for appropriate responses around people, and responses in atypical situations such as actuator faults. You will also work on Human Robot Interaction, write software to implement research ideas, and leverage your expertise to participate in a wide variety of research, including learning from simulation, reinforcement learning, learning from demonstrations, vision-language-action models, transformers, video generation, robot control, and more.</p>\n<p>To succeed in this role, you will need a PhD in a technical field or equivalent practical experience, knowledge of the latest in large machine learning research, and experience working with real-world robots. Expertise in using large datasets with deep neural networks to make real robots useful is also an advantage.</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_7896f519-fc9","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/7576917","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$141,000 - $202,000 + bonus + equity + benefits","x-skills-required":["PhD in a technical field or equivalent practical experience","Knowledge of the latest in large machine learning research","Experience working with real-world robots","Research track record in one or more of the following topics: Humanoid Whole Body Control, Vision Language Action models; 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top Google customers.</p>\n<p>You will be instrumental in translating cutting-edge AI research into real-world products, and demonstrating the capabilities of latest generation models.</p>\n<p>We are looking for engineers with a strong track record of building and shipping software, ideally with experience in early-stage environments where they have contributed to scaling products from initial concept to production.</p>\n<p>The ideal candidate will be motivated by the opportunity to drive product &amp; business impact.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Design and develop scalable software applications leveraging generative AI models.</li>\n<li>Rapidly prototype new features and iterate based on evaluation results.</li>\n<li>Collaborate with researchers and product managers to translate research advancements into tangible product features.</li>\n<li>Optimise software performance and ensure reliability of deployed applications.</li>\n<li>Contribute to the development of best practices for building and deploying generative AI applications.</li>\n<li>Lead the architecture and development of new products &amp; features from 0 to 1.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>A bachelor degree in computer science, electrical engineering or equivalent experience.</li>\n<li>8 years of experience in software development with one or more programming languages (e.g., Python) and with data structures/algorithms.</li>\n<li>5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.</li>\n<li>5 years of experience leading ML design and optimising ML infrastructure (e.g., model deployment, evaluation, data processing, debugging, and fine-tuning), with expertise in areas such as media generation or reinforcement learning.</li>\n</ul>\n<p>Preferred Qualifications:</p>\n<ul>\n<li>Experience with generative AI models and applications.</li>\n<li>Contributions to open-source projects.</li>\n<li>Experience with front end development.</li>\n<li>Experience in rapidly developing and shipping software products in a fast-paced, customer-facing startup-like environment, demonstrating adaptability to changing priorities.</li>\n<li>Experience with cloud computing platforms and infrastructure (e.g., Google Cloud Platform, AWS, Azure).</li>\n<li>Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Hugging Face, 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_2845a78b-426","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google","sameAs":"https://www.google.com/","logo":"https://logos.yubhub.co/google.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7219900","x-work-arrangement":"onsite","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","Generative AI models","Machine learning frameworks","Cloud computing platforms","Software design and architecture"],"x-skills-preferred":["Front end development","Open-source projects","Reinforcement learning","Media generation"],"datePosted":"2026-03-31T18:24:41.159Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Singapore"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Generative AI models, Machine learning frameworks, Cloud computing platforms, Software design and architecture, Front end development, Open-source projects, Reinforcement learning, Media generation"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_ce8db8e0-917"},"title":"Research Scientist, Gemini Information Tasks","description":"<p>We are seeking a Research Scientist to drive research in Gemini for information tasks. You will work on improving Gemini&#39;s quality of information-seeking responses, focusing on helpfulness, factuality, grounding, and other aspects. This is an opportunity to explore fundamental issues in modeling and data interventions for information-seeking scenarios, with significant opportunities in shaping Google&#39;s products in this space.</p>\n<p>Responsibilities:\nResearch on post-training (e.g., RL and SFT) for information-seeking scenarios in Gemini\nResearch on novel evaluation methods for improving model quality, grounding, and factuality\nResearch on orchestration of tool calls, and improved retrieval methods, for information-seeking scenarios</p>\n<p>About You:\nTo succeed in this role, you should have a PhD in a relevant area, or an equivalent research/publication record. You should also have strong software-engineering skills in addition to a research background. Experience in reinforcement learning, post-training methods, and LLMs for information-seeking scenarios is an advantage.</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_ce8db8e0-917","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/7450900","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["RL","SFT","LLMs","post-training methods","evaluation methods"],"x-skills-preferred":["reinforcement learning","orchestration of tool calls","improved retrieval methods"],"datePosted":"2026-03-31T18:17:11.555Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York City, New York, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"RL, SFT, LLMs, post-training methods, evaluation methods, reinforcement learning, orchestration of tool calls, improved retrieval methods"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_77d71d83-115"},"title":"Research Scientist, AI-powered Scientific Discovery","description":"<p>We are seeking a Research Scientist to join our team in Montreal dedicated to AI for Scientific Discovery. 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We&#39;re looking for Research Scientists to join the Robotics team whose mission is to &#39;Build embodied AI responsibly to benefit people in the physical world.&#39;</p>\n<p>Our team is focused on ensuring safe humanoid robot actions spanning agentic reasoning, HRI scenarios, and physical safety with VLA models. As a Research Scientist, you will design, implement, train, and evaluate large models and algorithms for humanoid robots. You will make breakthroughs and unlock new humanoid safety capabilities, including algorithmic and model development to improve a robot agent&#39;s understanding of its own embodiment and VLA capabilities.</p>\n<p>You will write software to implement research ideas and iterate quickly. 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Expertise with a subset of the following topics would be an advantage: Humanoid Whole Body Control, Vision Language Action models, Motion Planning, Force Control, AI Safety, Diffusion Policies, World Models, Imitation Learning, and Reinforcement Learning.</p>\n<p>The US base salary range for this full-time position is between $141,000 - $202,000 + 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_f3d5bc25-c76","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/7576917","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$141,000 - $202,000 + bonus + equity + benefits","x-skills-required":["PhD in a technical field","Knowledge of large machine learning research","Experience working with real-world robots","Humanoid Whole Body Control","Vision Language Action models","Motion Planning","Force Control","AI Safety","Diffusion Policies","World Models","Imitation Learning","Reinforcement Learning"],"x-skills-preferred":[],"datePosted":"2026-03-16T14:41:46.344Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York City, New York, US"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PhD in a technical field, Knowledge of large machine learning research, Experience working with real-world robots, Humanoid Whole Body Control, Vision Language Action models, Motion Planning, Force Control, AI Safety, Diffusion Policies, World Models, Imitation Learning, Reinforcement Learning","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":141000,"maxValue":202000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_a48bc0a6-719"},"title":"Research Scientist, Gemini Safety","description":"<p>Job Title: Research Scientist, Gemini Safety</p>\n<p>We&#39;re looking for a versatile Research Scientist to join our Gemini Safety team at Google DeepMind. As a Research Scientist, you will be responsible for applying and developing data and algorithmic cutting-edge solutions to advance the safety and fairness behavior of our latest user-facing models.</p>\n<p>The Gemini Safety team is accountable for the safety and fairness behavior of GDM&#39;s latest Gemini models. Our team focuses 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:</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>About You:</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, RL/DL, EMNLP, AAAI, UAI</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>Experience with JAX</li>\n</ul>\n<p>At Google DeepMind, we value diversity of experience, knowledge, backgrounds, and perspectives and harness these qualities to create extraordinary impact. 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Our search engine is entirely invented in-house utilizing transformers and generative LLMs, and we use its core and personalization capabilities to power everything from search itself to recommendations to shopping agents.</p>\n<p><strong>About You</strong></p>\n<p>As the Product Manager for Searchandizing, you will lead a cross-functional team to transform powerful merchandising tools into a guided, intuitive workflow that empowers customers to curate shopping experiences according to business goals. You are a user experience advocate and empathetic problem solver who can step into the shoes of e-commerce merchandisers to translate complex business needs into ROI-focused solutions.</p>\n<p><strong>Job Description</strong></p>\n<p>Constructor is the only platform that delivers reinforcement-learning-based product discovery that delivers the right product to the right person at the right time in the right context. However, our customers need to feel in control. They need to adjust shopping experience on top of Constructor algorithms to support their business goals, brand positioning, and marketing strategies. 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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>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. 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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. 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. 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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. 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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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"}}}]}