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YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_4d924e95-bdd"},"title":"Research Engineer, RL Infrastructure and Reliability (Knowledge Work)","description":"<p><strong>About the role</strong></p>\n<p>The Knowledge Work team builds the training environments and evaluations that make Claude effective at real-world professional workflows , searching, analysing, and creating across the tools and documents knowledge workers use every day.</p>\n<p>As that work scales, the systems behind it need to be as rigorous as the research itself. We are looking for a Research Engineer to own the reliability, observability, and infrastructure foundation that the team&#39;s research depends on.</p>\n<p>You will be responsible for ensuring our training and evaluation runs remain stable, well-instrumented, and high-quality as they grow in scale and complexity. A core part of this role is shifting reliability work from reactive to proactive: hardening systems, stress-testing at realistic scale, and building the observability and tooling that surface problems early , so researchers can stay focused on research rather than incident response.</p>\n<p>You will be the team&#39;s stable, context-rich owner for environment health and evaluation integrity, and the primary point of contact for partner teams when issues arise.</p>\n<p>While you&#39;ll work closely with researchers building new training environments, the priority for this role is the reliability those environments depend on. It&#39;s best suited to an engineer who finds real ownership and impact in making critical systems dependable, and in being the person behind trustworthy evaluation results the entire organisation relies on.</p>\n<p><strong>Key Responsibilities:</strong></p>\n<ul>\n<li>Serve as the dedicated reliability owner for the Knowledge Work training environments, providing continuity of context and reducing the operational overhead of rotating ownership</li>\n<li>Own a clean, canonical set of evaluation tools and processes for Knowledge Work capabilities, including the process used for model releases</li>\n<li>Build and automate observability, dashboards, and operational tooling for our training environments and evaluation systems, with an emphasis on high signal-to-noise: a small set of trusted metrics and alerts rather than sprawling instrumentation</li>\n<li>Proactively harden environments and evaluation systems through load testing, fault injection, and stress testing at realistic scale, so failures surface early rather than during critical training work</li>\n<li>Act as the primary point of contact for partner training and infrastructure teams when issues in our environments arise, and drive incidents to resolution</li>\n<li>Reduce the operational burden on researchers so they can stay focused on research</li>\n</ul>\n<p><strong>Minimum Qualifications:</strong></p>\n<ul>\n<li>Highly experienced Python engineer who ships reliable, well-instrumented code that teammates trust in production</li>\n<li>Demonstrated experience operating ML or distributed systems at scale, including significant on-call and incident-response experience</li>\n<li>Strong SRE or production-engineering mindset , reaching for SLOs, load tests, and failure injection before reaching for more dashboards</li>\n<li>Foundational ML knowledge sufficient to understand what a training environment or evaluation is actually measuring, and recognise when an evaluation has become stale or gameable</li>\n<li>Able to read research code and reason evaluation integrity</li>\n</ul>\n<p><strong>Preferred Qualifications:</strong></p>\n<ul>\n<li>5+ years of experience operating ML or distributed systems at scale</li>\n<li>Experience building or operating RL environments, agent harnesses, or LLM evaluation frameworks</li>\n<li>Familiarity with reward modelling, evaluation design, or detecting and mitigating reward hacking</li>\n<li>Experience with observability stacks (metrics, tracing, structured logging) and operational dashboard tooling</li>\n<li>Background in chaos engineering, fault injection, or large-scale load testing</li>\n<li>Experience with data quality pipelines, drift detection, or evaluation-set curation and versioning</li>\n<li>Familiarity with large-scale training or inference infrastructure (schedulers, multi-agent orchestration, sandboxed execution)</li>\n<li>Prior experience as a dedicated reliability or operations owner embedded within a research team</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p>Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren’t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p>\n<p><strong>How we’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.</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, including a comprehensive health insurance package, 401(k) matching, and generous paid time off.</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_4d924e95-bdd","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/5197337008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000-$850,000 USD","x-skills-required":["Python","ML","Distributed Systems","SRE","Production-Engineering","Observability","Dashboards","Operational Tooling","Load Testing","Fault Injection","Stress Testing","Reward Modelling","Evaluation Design","Data Quality Pipelines","Drift Detection","Evaluation-Set Curation","Versioning","Large-Scale Training","Inference Infrastructure","Schedulers","Multi-Agent Orchestration","Sandboxed Execution"],"x-skills-preferred":["RL Environments","Agent Harnesses","LLM Evaluation Frameworks","Chaos Engineering","Structured Logging","Dashboard Tooling"],"datePosted":"2026-04-24T13:11:33.535Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, ML, Distributed Systems, SRE, Production-Engineering, Observability, Dashboards, Operational Tooling, Load Testing, Fault Injection, Stress Testing, Reward Modelling, Evaluation Design, Data Quality Pipelines, Drift Detection, Evaluation-Set Curation, Versioning, Large-Scale Training, Inference Infrastructure, Schedulers, Multi-Agent Orchestration, Sandboxed Execution, RL Environments, Agent Harnesses, LLM Evaluation Frameworks, Chaos Engineering, Structured Logging, Dashboard Tooling","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_c2419ec4-6fb"},"title":"Research Engineer, RL Infrastructure and Reliability (Knowledge Work)","description":"<p><strong>About the role</strong></p>\n<p>The Knowledge Work team builds the training environments and evaluations that make Claude effective at real-world professional workflows , searching, analysing, and creating across the tools and documents knowledge workers use every day.</p>\n<p>As that work scales, the systems behind it need to be as rigorous as the research itself. We are looking for a Research Engineer to own the reliability, observability, and infrastructure foundation that the team&#39;s research depends on.</p>\n<p>You will be responsible for ensuring our training and evaluation runs remain stable, well-instrumented, and high-quality as they grow in scale and complexity.</p>\n<p>A core part of this role is shifting reliability work from reactive to proactive: hardening systems, stress-testing at realistic scale, and building the observability and tooling that surface problems early , so researchers can stay focused on research rather than incident response.</p>\n<p>You will be the team&#39;s stable, context-rich owner for environment health and evaluation integrity, and the primary point of contact for partner teams when issues arise.</p>\n<p><strong>Key Responsibilities:</strong></p>\n<ul>\n<li>Serve as the dedicated reliability owner for the Knowledge Work training environments, providing continuity of context and reducing the operational overhead of rotating ownership</li>\n<li>Own a clean, canonical set of evaluation tools and processes for Knowledge Work capabilities, including the process used for model releases</li>\n<li>Build and automate observability, dashboards, and operational tooling for our training environments and evaluation systems, with an emphasis on high signal-to-noise: a small set of trusted metrics and alerts rather than sprawling instrumentation</li>\n<li>Proactively harden environments and evaluation systems through load testing, fault injection, and stress testing at realistic scale, so failures surface early rather than during critical training work</li>\n<li>Act as the primary point of contact for partner training and infrastructure teams when issues in our environments arise, and drive incidents to resolution</li>\n<li>Reduce the operational burden on researchers so they can stay focused on research</li>\n</ul>\n<p><strong>Minimum Qualifications:</strong></p>\n<ul>\n<li>Highly experienced Python engineer who ships reliable, well-instrumented code that teammates trust in production</li>\n<li>Demonstrated experience operating ML or distributed systems at scale, including significant on-call and incident-response experience</li>\n<li>Strong SRE or production-engineering mindset , reaching for SLOs, load tests, and failure injection before reaching for more dashboards</li>\n<li>Foundational ML knowledge sufficient to understand what a training environment or evaluation is actually measuring, and recognise when an evaluation has become stale or gameable</li>\n<li>Able to read research code and reason evaluation integrity</li>\n</ul>\n<p><strong>Preferred Qualifications:</strong></p>\n<ul>\n<li>5+ years of experience operating ML or distributed systems at scale</li>\n<li>Experience building or operating RL environments, agent harnesses, or LLM evaluation frameworks</li>\n<li>Familiarity with reward modelling, evaluation design, or detecting and mitigating reward hacking</li>\n<li>Experience with observability stacks (metrics, tracing, structured logging) and operational dashboard tooling</li>\n<li>Background in chaos engineering, fault injection, or large-scale load testing</li>\n<li>Experience with data quality pipelines, drift detection, or evaluation-set curation and versioning</li>\n<li>Familiarity with large-scale training or inference infrastructure (schedulers, multi-agent orchestration, sandboxed execution)</li>\n<li>Prior experience as a dedicated reliability or operations owner embedded within a research team</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p>Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren’t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p>\n<p><strong>How we’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.</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.</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_c2419ec4-6fb","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/5197337008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000-$850,000 USD","x-skills-required":["Python","ML","Distributed Systems","SRE","Production-Engineering","Observability","Dashboards","Operational Tooling","Load Testing","Fault Injection","Stress Testing","Reliability","Infrastructure Foundation","Evaluation Integrity"],"x-skills-preferred":["RL Environments","Agent Harnesses","LLM Evaluation Frameworks","Reward Modelling","Evaluation Design","Chaos Engineering","Data Quality Pipelines","Drift Detection","Evaluation-Set Curation","Versioning","Large-Scale Training","Inference Infrastructure","Schedulers","Multi-Agent Orchestration","Sandboxed Execution"],"datePosted":"2026-04-24T12:16:31.677Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, ML, Distributed Systems, SRE, Production-Engineering, Observability, Dashboards, Operational Tooling, Load Testing, Fault Injection, Stress Testing, Reliability, Infrastructure Foundation, Evaluation Integrity, RL Environments, Agent Harnesses, LLM Evaluation Frameworks, Reward Modelling, Evaluation Design, Chaos Engineering, Data Quality Pipelines, Drift Detection, Evaluation-Set Curation, Versioning, Large-Scale Training, Inference Infrastructure, Schedulers, Multi-Agent Orchestration, Sandboxed Execution","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_9d0181ff-f94"},"title":"Site Reliability Engineer","description":"<p><strong>About the role</strong></p>\n<p>Gamma&#39;s infrastructure needs to be rock-solid for millions of daily users while enabling our engineering teams to ship fast. You&#39;ll own the operational health of our full backend platform, building automation and tooling that improves reliability and partnering with engineering to design systems that are observable, resilient, and easy to operate. Your work directly impacts every Gamma user&#39;s experience.</p>\n<p>This is a high-impact role where you&#39;ll balance reliability with velocity, knowing when to move fast and when to prioritize stability. You&#39;ll lead incident response, drive systemic improvements, and help shape how Gamma scales to serve its next 100 million users.</p>\n<p>Our team has a strong in-office culture and works in person 4–5 days per week in San Francisco. We love working together to stay creative and connected, with flexibility to work from home when focus matters most.</p>\n<p><strong>What you&#39;ll do</strong></p>\n<ul>\n<li>Own the reliability, availability, and performance of Gamma&#39;s production systems across our AWS infrastructure</li>\n<li>Build observability infrastructure from the ground up: metrics, logging, tracing, and alerting that give the team genuine visibility into system health before users feel the impact</li>\n<li>Design and ship automation that reduces toil, makes deployments safer, and gets us back on our feet faster when things go wrong</li>\n<li>Lead incident response and blameless post-mortems, then follow through on the systemic fixes that keep the same issues from coming back</li>\n<li>Partner with engineering teams on architecture reviews, SLO and SLI design, and reliability best practices that scale with the product</li>\n<li>Manage and optimize our compute, networking, databases, and managed services</li>\n</ul>\n<p><strong>What you&#39;ll bring</strong></p>\n<ul>\n<li>5+ years in site reliability engineering, DevOps, or systems engineering with deep, hands-on AWS expertise</li>\n<li>Strong programming skills in Python, Go, or TypeScript/Node.js, applied to building real tools and automation</li>\n<li>Solid experience with infrastructure-as-code (Terraform, CloudFormation) and end-to-end observability solutions</li>\n<li>Track record of making systems meaningfully more reliable through automation, smarter monitoring, and architectural improvements</li>\n<li>Deep understanding of networking, distributed systems, containerization (Docker, Kubernetes), and database performance at scale</li>\n<li>Sharp incident management instincts and the debugging skills to navigate complex production failures</li>\n<li>Experience scaling SaaS products to millions of users, or background with Kafka, chaos engineering, or service mesh technologies (Nice to have)</li>\n<li>AWS certifications, or experience with security and compliance frameworks like SOC 2 or ISO 27001 (Nice to have)</li>\n</ul>\n<p><strong>Compensation range:</strong></p>\n<p>The base salary for this full-time position, which spans multiple internal levels depending on qualifications, ranges between $230K - $310K plus benefits &amp; equity.</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_9d0181ff-f94","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Gamma","sameAs":"https://gamma.com","logo":"https://logos.yubhub.co/gamma.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/gamma/365c8133-e9c1-4bcb-b8f1-975d96115503","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"Full time","x-salary-range":"$230K - $310K","x-skills-required":["AWS","Python","Go","TypeScript/Node.js","Terraform","CloudFormation","observability solutions","infrastructure-as-code","DevOps","site reliability engineering","systems engineering","networking","distributed systems","containerization","database performance"],"x-skills-preferred":["Kafka","chaos engineering","service mesh technologies","AWS certifications","security and compliance frameworks"],"datePosted":"2026-04-24T12:15:30.768Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"AWS, Python, Go, TypeScript/Node.js, Terraform, CloudFormation, observability solutions, infrastructure-as-code, DevOps, site reliability engineering, systems engineering, networking, distributed systems, containerization, database performance, Kafka, chaos engineering, service mesh technologies, AWS certifications, security and compliance frameworks","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":230000,"maxValue":310000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_3b419874-946"},"title":"Senior Production Engineer","description":"<p>Production Engineering ensures CoreWeave&#39;s cloud delivers world-class reliability, performance, and operational excellence. We are hiring a Senior Production Engineer to take direct, hands-on ownership of critical tooling that drives reliability and delivery success.</p>\n<p>In this role, you will work broadly across the cloud stack designing, implementing, deploying, and operating systems that improve delivery velocity, service availability, and operational safety. You’ll be responsible for leading end-to-end technical projects, maintaining long-lived systems the team owns, and strengthening our operational foundations through durable engineering investments.</p>\n<p>This is a role for someone who enjoys building, debugging, and operating production systems. You will collaborate closely with service owners, but your primary impact comes from the reliability, quality, and maturity of the systems you deliver and maintain over time.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Take hands-on ownership of critical systems and frameworks, driving their architecture, implementation, and long-term evolution.</li>\n<li>Lead end-to-end delivery of engineering projects that improve availability, scalability, operational automation, and failure recovery.</li>\n<li>Build and maintain observability, alerting, automated remediation, and resilience testing for the systems you support.</li>\n<li>Participate in incident response as a subject-matter expert; drive deep root-cause investigations and implement lasting fixes.</li>\n<li>Improve runbooks, sources of truth, deployment workflows, and operational tooling to harden production readiness.</li>\n<li>Eliminate single points of failure and reduce operational toil through automation, refactors, and system redesigns.</li>\n<li>Ship production code regularly in Python, Go, or similar languages, and participate in on-call rotations.</li>\n<li>Maintain and mature long-term projects and frameworks owned by the team, ensuring they remain reliable, well-instrumented, and easy to operate.</li>\n<li>Collaborate with platform teams to ensure new features and services integrate cleanly with our reliability best-practices and tooling.</li>\n</ul>\n<p><strong>What You’ve Worked On (Minimum Qualifications)</strong></p>\n<ul>\n<li>7+ years of engineering experience building and operating distributed systems or cloud platforms.</li>\n<li>Demonstrated ability to debug complex production issues end-to-end, across services, infrastructure layers, and automation.</li>\n<li>Strong programming or scripting ability (Python, Go, or similar), with experience shipping and operating production services and tools.</li>\n<li>Deep knowledge of cloud-native technologies and distributed system patterns, particularly Kubernetes.</li>\n<li>Experience with modern observability stacks: metrics, tracing, structured logs, SLOs/SLIs, and incident lifecycle practices.</li>\n<li>A track record of successfully delivering hands-on reliability improvements through engineering execution.</li>\n</ul>\n<p><strong>Preferred Qualifications</strong></p>\n<ul>\n<li>Experience building internal tooling, frameworks, or automation that supports high-availability cloud operations.</li>\n<li>Familiarity with DR/BCP, service tiering, capacity planning, or chaos engineering.</li>\n<li>Background operating or building large-scale AI or GPU-accelerated infrastructure.</li>\n<li>Experience maintaining multi-year ownership of foundational production systems.</li>\n</ul>\n<p><strong>Why CoreWeave</strong></p>\n<p>At CoreWeave, we work hard, have fun, and move fast. You’ll join a team that values curiosity, ownership, and creative problem-solving. Production Engineering sits at the intersection of reliability and AI infrastructure, building systems that enable the world’s most powerful AI cloud.</p>\n<p><strong>Core Values</strong></p>\n<ul>\n<li>Be Curious at Your Core</li>\n<li>Act Like an Owner</li>\n<li>Empower Employees</li>\n<li>Deliver Best-in-Class Client Experiences</li>\n<li>Achieve More Together</li>\n</ul>\n<p>We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization&#39;s growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!</p>\n<p><strong>Compensation</strong></p>\n<p>The base salary range for this role is 160,000 to 214,000 SGD. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).</p>\n<p><strong>What We Offer</strong></p>\n<p>The range we’ve posted represents the typical compensation range for this role. To determine actual compensation, we review the market rate for each candidate which can include a variety of factors. These include qualifications, experience, interview performance, and location.</p>\n<p>In addition to a competitive salary, we offer a variety of benefits to support your needs, including:</p>\n<ul>\n<li>Medical, dental, and vision insurance - 100% paid for by CoreWeave</li>\n<li>Company-paid Life Insurance</li>\n<li>Voluntary supplemental life insurance</li>\n<li>Short and long-term disability insurance</li>\n<li>Flexible Spending Account</li>\n<li>Health Savings Account</li>\n<li>Tuition Reimbursement</li>\n<li>Ability to Participate in Employee Stock Purchase Program (ESPP)</li>\n<li>Mental Wellness Benefits through Spring Health</li>\n<li>Family-Forming support provided by Carrot</li>\n<li>Paid Parental Leave</li>\n<li>Flexible, full-service childcare support with Kinside</li>\n<li>401(k) with a generous employer match</li>\n<li>Flexible PTO</li>\n<li>Catered lunch each day in our office and data center locations</li>\n<li>A casual work environment</li>\n<li>A work culture focused on innovative disruption</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_3b419874-946","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/4675297006","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"160,000 to 214,000 SGD","x-skills-required":["cloud computing","distributed systems","Kubernetes","observability stacks","metrics","tracing","structured logs","SLOs/SLIs","incident lifecycle practices","Python","Go","engineering experience"],"x-skills-preferred":["internal tooling","frameworks","automation","DR/BCP","service tiering","capacity planning","chaos engineering","large-scale AI","GPU-accelerated infrastructure"],"datePosted":"2026-04-24T12:14:03.335Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Singapore"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"cloud computing, distributed systems, Kubernetes, observability stacks, metrics, tracing, structured logs, SLOs/SLIs, incident lifecycle practices, Python, Go, engineering experience, internal tooling, frameworks, automation, DR/BCP, service tiering, capacity planning, chaos engineering, large-scale AI, GPU-accelerated infrastructure","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":160000,"maxValue":214000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_6f3a053e-c43"},"title":"Staff Software Engineer, AI Reliability Engineering","description":"<p>We&#39;re seeking a Staff Software Engineer to join our AI Reliability Engineering team. 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You will support the reliability of safeguard model serving, which is critical for both site reliability and Anthropic&#39;s safety commitments.</p>\n<p>If you&#39;re committed to creating reliable, interpretable, and steerable AI systems, and you&#39;re passionate about working on complex technical problems, we&#39;d love to hear from you.</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_6f3a053e-c43","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/5101169008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"€235.000-€295.000 EUR","x-skills-required":["distributed systems","infrastructure","reliability","Service Level Objectives","monitoring","observability","incident response","high-availability serving infrastructure","cloud providers"],"x-skills-preferred":["SRE","Production Engineer","chaos engineering","systematic resilience testing","AI-specific observability tools and frameworks","ML hardware accelerators","RDMA","InfiniBand"],"datePosted":"2026-04-18T15:53:59.220Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Dublin, IE"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"distributed systems, infrastructure, reliability, Service Level Objectives, monitoring, observability, incident response, high-availability serving infrastructure, cloud providers, SRE, Production Engineer, chaos engineering, systematic resilience testing, AI-specific observability tools and frameworks, ML hardware accelerators, RDMA, InfiniBand"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_709b405a-48b"},"title":"Staff / Senior Software Engineer, AI Reliability","description":"<p>We&#39;re seeking a Staff / Senior Software Engineer, AI Reliability to join our team. 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We value impact and believe that the highest-impact AI research will be big science. We work as a single cohesive team on just a few large-scale research efforts and value communication skills.</p>\n<p>If you&#39;re interested in this role, please submit an application even if you don&#39;t believe you meet every single qualification. 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Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025.</p>\n<p><strong>About the Role</strong></p>\n<p>Production Engineering ensures CoreWeave’s cloud delivers world-class reliability, performance, and operational excellence. We are hiring a Senior Production Engineer to take direct, hands-on ownership of critical tooling that drives reliability and delivery success.</p>\n<p>In this role, you will work broadly across the cloud stack designing, implementing, deploying, and operating systems that improve delivery velocity, service availability, and operational safety. You’ll be responsible for leading end-to-end technical projects, maintaining long-lived systems the team owns, and strengthening our operational foundations through durable engineering investments.</p>\n<p>This is a role for someone who enjoys building, debugging, and operating production systems. You will collaborate closely with service owners, but your primary impact comes from the reliability, quality, and maturity of the systems you deliver and maintain over time.</p>\n<p><strong>What You’ll Do</strong></p>\n<ul>\n<li>Take hands-on ownership of critical systems and frameworks, driving their architecture, implementation, and long-term evolution.</li>\n</ul>\n<ul>\n<li>Lead end-to-end delivery of engineering projects that improve availability, scalability, operational automation, and failure recovery.</li>\n</ul>\n<ul>\n<li>Build and maintain observability, alerting, automated remediation, and resilience testing for the systems you support.</li>\n</ul>\n<ul>\n<li>Participate in incident response as a subject-matter expert; drive deep root-cause investigations and implement lasting fixes.</li>\n</ul>\n<ul>\n<li>Improve runbooks, sources of truth, deployment workflows, and operational tooling to harden production readiness.</li>\n</ul>\n<ul>\n<li>Eliminate single points of failure and reduce operational toil through automation, refactors, and system redesigns.</li>\n</ul>\n<ul>\n<li>Ship production code regularly in Python, Go, or similar languages, and participate in on-call rotations.</li>\n</ul>\n<ul>\n<li>Maintain and mature long-term projects and frameworks owned by the team, ensuring they remain reliable, well-instrumented, and easy to operate.</li>\n</ul>\n<ul>\n<li>Collaborate with platform teams to ensure new features and services integrate cleanly with our reliability best-practices and tooling.</li>\n</ul>\n<p><strong>What You’ve Worked On (Minimum Qualifications)</strong></p>\n<ul>\n<li>7+ years of engineering experience building and operating distributed systems or cloud platforms.</li>\n</ul>\n<ul>\n<li>Demonstrated ability to debug complex production issues end-to-end, across services, infrastructure layers, and automation.</li>\n</ul>\n<ul>\n<li>Strong programming or scripting ability (Python, Go, or similar), with experience shipping and operating production services and tools.</li>\n</ul>\n<ul>\n<li>Deep knowledge of cloud-native technologies and distributed system patterns, particularly Kubernetes.</li>\n</ul>\n<ul>\n<li>Experience with modern observability stacks: metrics, tracing, structured logs, SLOs/SLIs, and incident lifecycle practices.</li>\n</ul>\n<ul>\n<li>A track record of successfully delivering hands-on reliability improvements through engineering execution.</li>\n</ul>\n<p><strong>Preferred Qualifications</strong></p>\n<ul>\n<li>Experience building internal tooling, frameworks, or automation that supports high-availability cloud operations.</li>\n</ul>\n<ul>\n<li>Familiarity with DR/BCP, service tiering, capacity planning, or chaos engineering.</li>\n</ul>\n<ul>\n<li>Background operating or building large-scale AI or GPU-accelerated infrastructure.</li>\n</ul>\n<ul>\n<li>Experience maintaining multi-year ownership of foundational production systems.</li>\n</ul>\n<p>Why CoreWeave?</p>\n<p>At CoreWeave, we work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:</p>\n<ul>\n<li>Be Curious at Your Core</li>\n</ul>\n<ul>\n<li>Act Like an Owner</li>\n</ul>\n<ul>\n<li>Empower Employees</li>\n</ul>\n<ul>\n<li>Deliver Best-in-Class Client Experiences</li>\n</ul>\n<ul>\n<li>Achieve More Together</li>\n</ul>\n<p>We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization&#39;s growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!</p>\n<p>The base salary range for this role is $139,000 to $204,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).</p>\n<p>What We Offer</p>\n<p>The range we’ve posted represents the typical compensation range for this role. To determine actual compensation, we review the market rate for each candidate which can include a variety of factors. These include qualifications, experience, interview performance, and location.</p>\n<p>In addition to a competitive salary, we offer a variety of benefits to support your needs, including:</p>\n<ul>\n<li>Medical, dental, and vision insurance - 100% paid for by CoreWeave</li>\n</ul>\n<ul>\n<li>Company-paid Life Insurance</li>\n</ul>\n<ul>\n<li>Voluntary supplemental life insurance</li>\n</ul>\n<ul>\n<li>Short and long-term disability insurance</li>\n</ul>\n<ul>\n<li>Flexible Spending Account</li>\n</ul>\n<ul>\n<li>Health Savings Account</li>\n</ul>\n<ul>\n<li>Tuition Reimbursement</li>\n</ul>\n<ul>\n<li>Ability to Participate in Employee Stock Purchase Program (ESPP)</li>\n</ul>\n<ul>\n<li>Mental Wellness Benefits through Spring Health</li>\n</ul>\n<ul>\n<li>Family-Forming support provided by Carrot</li>\n</ul>\n<ul>\n<li>Paid Parental Leave</li>\n</ul>\n<ul>\n<li>Flexible, full-service childcare support with Kinside</li>\n</ul>\n<ul>\n<li>401(k) with a generous employer match</li>\n</ul>\n<ul>\n<li>Flexible PTO</li>\n</ul>\n<ul>\n<li>Catered lunch each day in our office and data center locations</li>\n</ul>\n<ul>\n<li>A casual work environment</li>\n</ul>\n<ul>\n<li>A work culture focused on innovative disruption</li>\n</ul>\n<p>Our Workplace</p>\n<p>While we prioritize a hybrid work environment, remote work may be considered for candidates located more than 30 miles from an office, based on role requirements for specialized skill sets. 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You will ship production code, participate in on-call rotations as needed, and mentor engineers on platform ownership, operational design, and sustainable production practices.</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_6a24f057-4f1","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/4644302006","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$188,000 to $275,000","x-skills-required":["distributed systems","cloud platforms","Kubernetes","observability","incident practices","metrics","tracing","structured logs","SLIs/SLOs","PIRs"],"x-skills-preferred":["foundational internal platforms","service tiering","disaster recovery","chaos engineering","structured resilience programs"],"datePosted":"2026-04-18T15:50:55.257Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Livingston, NJ / New York, NY / Sunnyvale, CA / Bellevue, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"distributed systems, cloud platforms, Kubernetes, observability, incident practices, metrics, tracing, structured logs, SLIs/SLOs, PIRs, foundational internal platforms, service tiering, disaster recovery, chaos engineering, structured resilience programs","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":188000,"maxValue":275000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_bf25e8de-318"},"title":"Director of Engineering (Data Infrastructure)","description":"<p>Job Title: Director of Engineering (Data Infrastructure)</p>\n<p>Location: Bengaluru, India</p>\n<p>We&#39;re looking for a seasoned Director of Engineering to lead our data infrastructure organization in Bengaluru. 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As a member of this team, you will be responsible for designing, implementing, and maintaining systems and tools that support flow-level observability, payments reliability, and scalability.</p>\n<p>Your primary focus will be on building and managing large-scale platforms to improve the availability of our Payments platform for internal and external stakeholders. You will collaborate closely with other Payments engineering teams and Infra teams to ensure services are instrumented, scalable, and resilient to support our growing business.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Designing, implementing, and maintaining systems and tools at a platform level that support flow-level observability, payments reliability, and scalability.</li>\n<li>Identifying and driving improvements to increase the Payments Availability, Observability, and Resiliency of Airbnb Payments.</li>\n<li>Developing observability standards/framework for new product readiness to ensure service reliability in SOA and distributed systems.</li>\n<li>Building domain expertise to achieve scalability by understanding the nuances of Payments across processing, compliance, and infra.</li>\n<li>Driving large-scale migration and adoption projects on Observability &amp; Reliability by cross-collaborating with various Payments teams.</li>\n<li>Leading initiatives that promote a culture of reliability throughout the organization by improving incident management platforms and instrumentation.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>7+ years of experience in back-end software development focusing on large-scale distributed systems.</li>\n<li>BE/B.Tech in Computer Science or a related technical field.</li>\n<li>Strong software development skills in one or more languages such as Java, Python, Kotlin, Scala, or Ruby on Rails.</li>\n<li>Experience in building intelligent AI agents and systems powered by Large Language Models is a plus.</li>\n<li>Evidence of exposure to architectural patterns of a large, high-scale web application (e.g., well-designed APIs, high-volume data pipelines, efficient algorithms).</li>\n<li>Familiarity with cloud platforms like AWS or Google Cloud Platform.</li>\n<li>Deep understanding of software development best practices, including version control, automated testing, CI/CD, and code reviews.</li>\n<li>Experience in incident management, monitoring, alerting, and root cause analysis.</li>\n<li>Effective leadership and communication skills to coordinate cross-functional teams during large-scale projects.</li>\n<li>Experience with initiatives across auto-scaling, self-healing mechanisms, chaos engineering, performance optimization techniques will be a plus.</li>\n<li>Previous experience in AI/ML will also be a plus.</li>\n</ul>\n<p>If you are a strong problem solver and have worked in a team that is on-call for production systems before, we encourage you to apply.</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_0a80aec8-c25","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Airbnb","sameAs":"https://www.airbnb.com/","logo":"https://logos.yubhub.co/airbnb.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/airbnb/jobs/7613550","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Java","Python","Kotlin","Scala","Ruby on Rails","Cloud platforms","Software development best practices","Incident management","Monitoring","Alerting","Root cause analysis"],"x-skills-preferred":["AI/ML","Auto-scaling","Self-healing mechanisms","Chaos engineering","Performance optimization techniques"],"datePosted":"2026-04-18T15:43:32.370Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Bangalore, India"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Java, Python, Kotlin, Scala, Ruby on Rails, Cloud platforms, Software development best practices, Incident management, Monitoring, Alerting, Root cause analysis, AI/ML, Auto-scaling, Self-healing mechanisms, Chaos engineering, Performance optimization techniques"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_73ff6f07-c0e"},"title":"Staff Software Engineer, AI Reliability Engineering","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>Claude has your back. AIRE has Claude&#39;s. Help us keep Claude reliable for everyone who depends on it.</p>\n<p>AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.</p>\n<p>Reliability here is an emergent phenomenon that transcends any single team&#39;s boundaries, so someone has to zoom out and look at the whole picture. That&#39;s us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity.</li>\n<li>Design and implement monitoring and observability systems across the token path.</li>\n<li>Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers</li>\n<li>Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.</li>\n<li>Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic&#39;s safety commitments.</li>\n</ul>\n<p><strong>You may be a good fit if you</strong></p>\n<ul>\n<li>Have strong distributed systems, infrastructure, or reliability backgrounds -- we&#39;re looking for reliability-minded software engineers and SREs.</li>\n<li>Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don&#39;t have deep expertise yet.</li>\n<li>Think holistically about how systems compose and where the seams are.</li>\n<li>Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions.</li>\n<li>Care about users and feel ownership over outcomes, even for systems you don&#39;t own.</li>\n<li>Have excellent communication and collaboration skills -- you&#39;ll be partnering across the entire company.</li>\n<li>Bring diverse experience -- the team&#39;s strength comes from people who&#39;ve built product stacks, scaled databases, run massive distributed systems, and everything in between.</li>\n</ul>\n<p><strong>Strong candidates may also</strong></p>\n<ul>\n<li>Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems</li>\n<li>Have experience operating large-scale model serving or training infrastructure (&gt;1000 GPUs).</li>\n<li>Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).</li>\n<li>Understand ML-specific networking optimizations like RDMA and InfiniBand.</li>\n<li>Have expertise in AI-specific observability tools and frameworks.</li>\n<li>Have experience with chaos engineering and systematic resilience testing.</li>\n<li>Have contributed to open-source infrastructure or ML tooling.</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. 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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>AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.</p>\n<p>Reliability here is an emergent phenomenon that transcends any single team&#39;s boundaries, so someone has to zoom out and look at the whole picture. That&#39;s us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.</p>\n<p>Claude has your back. AIRE has Claude&#39;s. Help us keep Claude reliable for everyone who depends on it.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity.</li>\n</ul>\n<ul>\n<li>Design and implement monitoring and observability systems across the token path.</li>\n</ul>\n<ul>\n<li>Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers</li>\n</ul>\n<ul>\n<li>Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.</li>\n</ul>\n<ul>\n<li>Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic&#39;s safety commitments.</li>\n</ul>\n<p><strong>You may be a good fit if you:</strong></p>\n<ul>\n<li>Have strong distributed systems, infrastructure, or reliability backgrounds -- we&#39;re looking for reliability-minded software engineers and SREs.</li>\n</ul>\n<ul>\n<li>Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don&#39;t have deep expertise yet.</li>\n</ul>\n<ul>\n<li>Think holistically about how systems compose and where the seams are.</li>\n</ul>\n<ul>\n<li>Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions.</li>\n</ul>\n<ul>\n<li>Care about users and feel ownership over outcomes, even for systems you don&#39;t own.</li>\n</ul>\n<ul>\n<li>Have excellent communication and collaboration skills -- you&#39;ll be partnering across the entire company.</li>\n</ul>\n<ul>\n<li>Bring diverse experience -- the team&#39;s strength comes from people who&#39;ve built product stacks, scaled databases, run massive distributed systems, and everything in between.</li>\n</ul>\n<p><strong>Strong candidates may also:</strong></p>\n<ul>\n<li>Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems</li>\n</ul>\n<ul>\n<li>Have experience operating large-scale model serving or training infrastructure (&gt;1000 GPUs).</li>\n</ul>\n<ul>\n<li>Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).</li>\n</ul>\n<ul>\n<li>Understand ML-specific networking optimizations like RDMA and InfiniBand.</li>\n</ul>\n<ul>\n<li>Have expertise in AI-specific observability tools and frameworks.</li>\n</ul>\n<ul>\n<li>Have experience with chaos engineering and systematic resilience testing.</li>\n</ul>\n<ul>\n<li>Have contributed to open-source infrastructure or ML tooling.</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.</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. 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