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Claude writes a lot of the code we commit, which means the bottleneck isn&#39;t typing , it&#39;s judgment, taste, and the ability to react to what researchers need next.\\n\\nYou&#39;ll iterate on data collection strategies to distill the knowledge of thousands of human experts around the world into our models, and you&#39;ll do it in a loop that closes in hours and days, not quarters or months.\\n\\nAnthropic&#39;s Reinforcement Learning organization leads the research and development that trains Claude to be capable, reliable, and safe. We&#39;ve contributed to every Claude model, with significant impact on the autonomy and coding capabilities of our most advanced models.\\n\\nOur work spans teaching models to use computers effectively, advancing code generation through RL, pioneering fundamental RL research for large language models, and building the scalable training methodologies behind our frontier production models.\\n\\nThe RL org is organized around four goals: solving the science of long-horizon tasks and continual learning, scaling RL data and environments to be comprehensive and diverse, automating software engineering end-to-end, and training the frontier production model.\\n\\nOur engineering teams build the environments, evaluation systems, data pipelines, and tooling that make all of this possible , from realistic agentic training environments and scalable code data generation to human data collection platforms and production training operations.\\n\\n### Responsibilities\\n\\n<em>   Build and extend web platforms for RL environment creation, management, and quality review , including environment configuration, versioning, and validation workflows\\n</em>   Develop vendor-facing interfaces and tooling that let external partners create, submit, and iterate on training environments with minimal friction\\n<em>   Design and implement platforms for human data collection at scale, including labeling workflows, quality assurance systems, and feedback mechanisms that surface reward signal integrity issues early\\n</em>   Build evaluation dashboards and observability UIs that give researchers real-time insight into environment quality, training run health, and reward hacking\\n<em>   Create backend services and APIs that connect environment authoring tools, data collection systems, and RL training infrastructure\\n</em>   Build and expand scalable code data generation pipelines, producing diverse programming tasks with robust reward signals across languages and difficulty levels\\n<em>   Develop onboarding automation and documentation tooling so new vendors and internal users ramp up in hours, not weeks\\n</em>   Partner closely with RL researchers, data operations, and vendor management to translate ambiguous requirements into well-scoped, well-designed products\\n\\n### Requirements\\n\\n<em>   Strong software engineering fundamentals and real full-stack range , you&#39;re comfortable owning a surface from database schema to frontend\\n</em>   Proficient in Python and a modern web stack (React, TypeScript, or similar)\\n<em>   Track record of shipping systems that solved a hard problem, not just shipped on time , e.g. you built the thing that made your team 10x faster, or the internal tool nobody thought was possible\\n</em>   Operate with high agency: you identify what needs to be done and drive it forward without waiting for a ticket\\n<em>   Found yourself wondering &quot;why isn&#39;t this moving faster?&quot; in previous roles , and then have done something about it\\n</em>   Care about UX and can build interfaces that are intuitive for both technical researchers and non-technical labelers\\n<em>   Communicate clearly with researchers, operations teams, and engineers, and can turn vague asks into well-scoped work\\n</em>   Thrive in a fast-moving environment where priorities shift, Claude is your pair programmer, and the next problem is often one nobody has solved before\\n<em>   Care about Anthropic&#39;s mission to build safe, beneficial AI and want your work to contribute directly to it\\n\\n### Nice to Have\\n\\n</em>   Built data collection, labeling, or annotation platforms , ideally ones that had to scale across many vendors or many task types\\n<em>   Background building multi-tenant platforms with role-based access, audit trails, and vendor management workflows\\n</em>   Experience with cloud infrastructure (GCP or AWS), Docker, and CI/CD pipelines\\n<em>   Familiarity with LLM training, fine-tuning, or evaluation workflows\\n</em>   Experience with async Python (Trio, asyncio) or high-throughput API design\\n<em>   Background in dashboards, monitoring, or observability tooling\\n</em>   Experience working directly with external vendors or partners on technical integrations\\n<em>   A background that isn&#39;t a straight line , e.g. math or physics into SWE, competitive programming, research into engineering, or a side project that outgrew its scope\\n\\n### Representative Projects\\n\\n</em>   Building a unified platform for human data collection that integrates labeling workflows, vendor management, and QA for complex agentic tasks\\n<em>   Developing vendor onboarding automation that handles Docker registry access, API token management, and environment validation\\n</em>   Creating evaluation and observability dashboards that catch reward hacks, measure environment difficulty, and give real-time feedback during production training\\n<em>   Building environment quality review workflows that let researchers browse, grade, and provide feedback on training environments\\n</em>   Developing automated environment quality pipelines that validate correctness and difficulty calibration before environments hit production training\\n*   Building internal tools for browsing and analyzing training run results, environment statistics, and data collection progress</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_b2637f59-e14","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/5186067008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$300,000-$405,000 USD","x-skills-required":["Python","Modern web stack","React","TypeScript","Strong software engineering fundamentals","Full-stack range","Database schema","Frontend","Cloud infrastructure","Docker","CI/CD pipelines","LLM training","Fine-tuning","Evaluation workflows","Async Python","High-throughput API design","Dashboards","Monitoring","Observability tooling"],"x-skills-preferred":["Data collection","Labeling","Annotation platforms","Multi-tenant platforms","Role-based access","Audit trails","Vendor management workflows"],"datePosted":"2026-04-18T15:54:27.784Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Modern web stack, React, TypeScript, Strong software engineering fundamentals, Full-stack range, Database schema, Frontend, Cloud infrastructure, Docker, CI/CD pipelines, LLM training, Fine-tuning, Evaluation workflows, Async Python, High-throughput API design, Dashboards, Monitoring, Observability tooling, Data collection, Labeling, Annotation platforms, Multi-tenant platforms, Role-based access, Audit trails, Vendor management workflows","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_76fd624c-e23"},"title":"Full-Stack Software Engineer, Reinforcement Learning","description":"<p>As a Full-Stack Software Engineer in RL, you&#39;ll build the platforms, tools, and interfaces that power environment creation, data collection, and training observability. The quality of Claude&#39;s next generation depends on the quality of the data we train it on , and the systems you build are what make that data possible. You&#39;ll own product surfaces end-to-end , from backend services and APIs to the web UIs that researchers, external vendors, and thousands of data labelers use every day. You don&#39;t need a background in ML research. What matters is that you can take an ambiguous, high-stakes problem and ship a polished, reliable product against it, fast.</p>\n<p>This team moves very quickly. Claude writes a lot of the code we commit, which means the bottleneck isn&#39;t typing , it&#39;s judgment, taste, and the ability to react to what researchers need next. You&#39;ll iterate on data collection strategies to distill the knowledge of thousands of human experts around the world into our models, and you&#39;ll do it in a loop that closes in hours and days, not quarters or months.</p>\n<p>Our work spans teaching models to use computers effectively, advancing code generation through RL, pioneering fundamental RL research for large language models, and building the scalable training methodologies behind our frontier production models. The RL org is organized around four goals: solving the science of long-horizon tasks and continual learning, scaling RL data and environments to be comprehensive and diverse, automating software engineering end-to-end, and training the frontier production model.</p>\n<p>Our engineering teams build the environments, evaluation systems, data pipelines, and tooling that make all of this possible , from realistic agentic training environments and scalable code data generation to human data collection platforms and production training operations.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Build and extend web platforms for RL environment creation, management, and quality review , including environment configuration, versioning, and validation workflows</li>\n<li>Develop vendor-facing interfaces and tooling that let external partners create, submit, and iterate on training environments with minimal friction</li>\n<li>Design and implement platforms for human data collection at scale, including labeling workflows, quality assurance systems, and feedback mechanisms that surface reward signal integrity issues early</li>\n<li>Build evaluation dashboards and observability UIs that give researchers real-time insight into environment quality, training run health, and reward hacking</li>\n<li>Create backend services and APIs that connect environment authoring tools, data collection systems, and RL training infrastructure</li>\n<li>Build and expand scalable code data generation pipelines, producing diverse programming tasks with robust reward signals across languages and difficulty levels</li>\n<li>Develop onboarding automation and documentation tooling so new vendors and internal users ramp up in hours, not weeks</li>\n<li>Partner closely with RL researchers, data operations, and vendor management to translate ambiguous requirements into well-scoped, well-designed products</li>\n</ul>\n<p>You May Be a Good Fit If You:</p>\n<ul>\n<li>Have strong software engineering fundamentals and real full-stack range , you&#39;re comfortable owning a surface from database schema to frontend</li>\n<li>Are proficient in Python and a modern web stack (React, TypeScript, or similar)</li>\n<li>Have a track record of shipping systems that solved a hard problem, not just shipped on time , e.g. you built the thing that made your team 10x faster, or the internal tool nobody thought was possible</li>\n<li>Operate with high agency: you identify what needs to be done and drive it forward without waiting for a ticket</li>\n<li>Have found yourself wondering &quot;why isn&#39;t this moving faster?&quot; in previous roles , and then have done something about it</li>\n<li>Care about UX and can build interfaces that are intuitive for both technical researchers and non-technical labelers</li>\n<li>Communicate clearly with researchers, operations teams, and engineers, and can turn vague asks into well-scoped work</li>\n<li>Thrive in a fast-moving environment where priorities shift, Claude is your pair programmer, and the next problem is often one nobody has solved before</li>\n<li>Care about Anthropic&#39;s mission to build safe, beneficial AI and want your work to contribute directly to it</li>\n</ul>\n<p>Strong Candidates May Also Have:</p>\n<ul>\n<li>Built data collection, labeling, or annotation platforms , ideally ones that had to scale across many vendors or many task types</li>\n<li>Background building multi-tenant platforms with role-based access, audit trails, and vendor management workflows</li>\n<li>Experience with cloud infrastructure (GCP or AWS), Docker, and CI/CD pipelines</li>\n<li>Familiarity with LLM training, fine-tuning, or evaluation workflows</li>\n<li>Experience with async Python (Trio, asyncio) or high-throughput API design</li>\n<li>Background in dashboards, monitoring, or observability tooling</li>\n<li>Experience working directly with external vendors or partners on technical integrations</li>\n<li>A background that isn&#39;t a straight line , e.g. math or physics into SWE, competitive programming, research into engineering, or a side project that outgrew its scope</li>\n</ul>\n<p>Representative Projects:</p>\n<ul>\n<li>Building a unified platform for human data collection that integrates labeling workflows, vendor management, and QA for complex agentic tasks</li>\n<li>Developing vendor onboarding automation that handles Docker registry access, API token management, and environment validation</li>\n<li>Creating evaluation and observability dashboards that catch reward hacks, measure environment difficulty, and give real-time feedback during production training</li>\n<li>Building environment quality review workflows that let researchers browse, grade, and provide feedback on training environments</li>\n<li>Developing automated environment quality pipelines that validate correctness and difficulty calibration before environments hit production training</li>\n<li>Building internal tools for browsing and analyzing training run results, environment statistics, and data collection progress</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_76fd624c-e23","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/5186067008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$300,000-$405,000 USD","x-skills-required":["Python","Modern web stack","React","TypeScript","Cloud infrastructure","Docker","CI/CD pipelines","LLM training","Fine-tuning","Evaluation workflows","Async Python","High-throughput API design","Dashboards","Monitoring","Observability tooling"],"x-skills-preferred":["Data collection","Labeling","Annotation","Multi-tenant platforms","Role-based access","Audit trails","Vendor management workflows"],"datePosted":"2026-04-18T15:39:16.596Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Modern web stack, React, TypeScript, Cloud infrastructure, Docker, CI/CD pipelines, LLM training, Fine-tuning, Evaluation workflows, Async Python, High-throughput API design, Dashboards, Monitoring, Observability tooling, Data collection, Labeling, Annotation, Multi-tenant platforms, Role-based access, Audit trails, Vendor management workflows","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_582f2e18-5a0"},"title":"Procurement Operations Leader","description":"<p><strong>Procurement Operations Leader</strong></p>\n<p><strong>Location</strong></p>\n<p>San Francisco</p>\n<p><strong>Employment Type</strong></p>\n<p>Full time</p>\n<p><strong>Location Type</strong></p>\n<p>Hybrid</p>\n<p><strong>Department</strong></p>\n<p>Finance</p>\n<p><strong>Compensation</strong></p>\n<ul>\n<li>$234K – $295K • Offers Equity</li>\n</ul>\n<p>The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.</p>\n<p><strong>Benefits</strong></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><strong>About the Team</strong></p>\n<p>The Procurement Operations team runs the engine that turns intent into spend at OpenAI.</p>\n<p>We sit between the people who need things (research, product, engineering, operations) and the systems that record, pay for, and report on them. Our job is to make it easy for teams to buy what they need while ensuring every dollar lands in the right place — on the right supplier, on the right PO, with the right data — so Finance, Legal, and Security can trust the outcome.</p>\n<p>As OpenAI scales, this team is also where AI, automation, and self-service come to life. We design workflows that let more volume move touchlessly, while embedding controls and audit trails directly into the systems — so speed and rigor grow together. This team works side-by-side with Accounts Payable, Strategic Sourcing, Legal, Security, and Finance Systems to ensure the company can move fast without losing financial integrity or control.</p>\n<p><strong>About the Role</strong></p>\n<p>The Procurement Operations Leader owns the operational backbone of how OpenAI turns requests into committed, controlled, and payable spend. This role sits at the center of our purchasing lifecycle — ensuring that every request, PO, and supplier record is complete, policy-aligned, and ready to scale through automation, BPO support, and AI-assisted workflows already in production.</p>\n<p>You’ll be responsible for the health of the intake-to-PO engine: how work enters the system, how it gets validated, how it routes, and how it lands in our financial systems. That includes strengthening the controls that keep us safe, the data that makes us fast, and the workflows that allow AI and self-service to do more of the work.</p>\n<p>This is a player-coach role. You’ll design the systems and guardrails that enable scale, and you’ll also step into the queue when something breaks, when volume spikes, or when a complex case needs hands-on leadership.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Own the intake-to-PO operating model, including request validation, routing, PO creation, change management, and supplier master data.</li>\n</ul>\n<ul>\n<li>Ensure every request and PO is complete, policy-aligned, and audit-ready, with clean metadata that supports invoice matching and reporting.</li>\n</ul>\n<ul>\n<li>Increase first-pass invoice match rates by improving upstream data quality and exception logic.</li>\n</ul>\n<ul>\n<li>Reduce P2P cycle time through automation, self-service, and disciplined queue management.</li>\n</ul>\n<ul>\n<li>Codify approval rules, exception handling, and segregation of duties directly into Zip, Oracle, and connected systems.</li>\n</ul>\n<ul>\n<li>Expand and refine AI-assisted triage, validation, and routing so more volume moves through the system with less manual touchpoints.</li>\n</ul>\n<ul>\n<li>Build and maintain dashboards and operational metrics to track intake health, cycle time, exception rates, and control performance.</li>\n</ul>\n<ul>\n<li>Partner with Legal, Security, TPRM, and Finance Systems to ensure controls and policies are reflected in how work actually flows.</li>\n</ul>\n<ul>\n<li>Serve as the escalation point when operational breakdowns occur — and lead the resolution through to a clean outcome.</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>10+ years of experience running procurement, P2P, or intake-driven operations in fast-scaling environments.</li>\n</ul>\n<ul>\n<li>A strong understanding of how upstream intake and metadata quality drive downstream invoice accuracy, audit readiness, and financial clarity.</li>\n</ul>\n<ul>\n<li>A passion for transforming messy, manual processes into structured, scalable, automated systems.</li>\n</ul>\n<ul>\n<li>Leveraged AI/automation to improve quality, speed, and scale.</li>\n</ul>\n<ul>\n<li>Used data and metrics to spot risk, bottlenecks, and opportunities to simplify.</li>\n</ul>\n<ul>\n<li>A strong grasp of controls, segregation of duties, and compliance requirements across Procurement, Legal, and Finance.</li>\n</ul>\n<ul>\n<li>Comfort being a technical leader and a player-coach.</li>\n</ul>\n<p><strong>What We Offer</strong></p>\n<ul>\n<li>Competitive salary and bonus structure</li>\n</ul>\n<ul>\n<li>Comprehensive benefits package</li>\n</ul>\n<ul>\n<li>Opportunities for professional growth and development</li>\n</ul>\n<ul>\n<li>Collaborative and dynamic work environment</li>\n</ul>\n<ul>\n<li>Recognition and rewards for outstanding performance</li>\n</ul>\n<p><strong>How to Apply</strong></p>\n<p>If you are a motivated and experienced professional looking for a new challenge, please submit your application, including your resume and a cover letter, to [insert contact information]. 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