Research Engineer, RL Infrastructure (Knowledge Work)
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole.
What this role actually needs.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Responsibilities: - Serve as the dedicated reliability owner for the Knowledge Work training environments, providing continuity of context and reducing the operational overhead of rotating ownership - Own a clean, canonical set of evaluation tools and processes for Knowledge Work capabilities, including the process used for model releases - 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 - 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 - Act as the primary point of contact for partner training and infrastructure teams when issues in our environments arise, and drive incidents to resolution - Reduce the operational burden on researchers so they can stay focused on research Requirements: - Highly experienced Python engineer who ships reliable, well-instrumented code that teammates trust in production - Demonstrated experience operating ML or distributed systems at scale, including significant on-call and incident-response experience - Strong SRE or production-engineering mindset — reaching for SLOs, load tests, and failure injection before reaching for more dashboards - Foundational ML knowledge sufficient to understand what a training environment or evaluation is actually measuring, and recognize when an evaluation has become stale or gameable - Able to read research code and reason evaluation integrity Company context: Anthropic is an AI safety company building Claude, a frontier-model assistant for developers, enterprises, and consumers.
Day-to-day expectations
Anthropic lists these responsibilities for the Research Engineer, RL Infrastructure (Knowledge Work) role.
- Serve as the dedicated reliability owner for the Knowledge Work training environments, providing continuity of context and reducing the operational overhead of rotating ownership
- Own a clean, canonical set of evaluation tools and processes for Knowledge Work capabilities, including the process used for model releases
- 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
- 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
- Act as the primary point of contact for partner training and infrastructure teams when issues in our environments arise, and drive incidents to resolution
- Reduce the operational burden on researchers so they can stay focused on research
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- Highly experienced Python engineer who ships reliable, well-instrumented code that teammates trust in production
- Demonstrated experience operating ML or distributed systems at scale, including significant on-call and incident-response experience
- Strong SRE or production-engineering mindset — reaching for SLOs, load tests, and failure injection before reaching for more dashboards
- Foundational ML knowledge sufficient to understand what a training environment or evaluation is actually measuring, and recognize when an evaluation has become stale or gameable
- Able to read research code and reason evaluation integrity
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Current extracted signal: Open to TN, H-1B, and OPT candidates already in the United States. UpJobz treats this as a search signal, not legal advice, and links visa-sensitive roles back to the relevant visa hub where possible.
Location read
On-site roles in San Francisco should be compared against commute, local salary bands, and nearby employer demand.
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Source: job-boards.greenhouse.io · Source ID: 5197337008 · Confidence: 97/100 · Last checked: May 7, 2026
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