Research Engineer, RL Infrastructure and Reliability (Knowledge Work)

Anthropic•San Francisco, CA

7h•Hybrid

About The Position

The Knowledge Work team builds the training environments and evaluations that make Claude effective at real-world professional workflows — searching, analyzing, and creating across the tools and documents knowledge workers use every day. 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's research depends on. 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. You will be the team's stable, context-rich owner for environment health and evaluation integrity, and the primary point of contact for partner teams when issues arise. Where this role focuses: While you'll work closely with researchers building new training environments, the priority for this role is the reliability those environments depend on. It'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 organization relies on.

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

Nice To Haves

5+ years of experience operating ML or distributed systems at scale
Experience building or operating RL environments, agent harnesses, or LLM evaluation frameworks
Familiarity with reward modeling, evaluation design, or detecting and mitigating reward hacking
Experience with observability stacks (metrics, tracing, structured logging) and operational dashboard tooling
Background in chaos engineering, fault injection, or large-scale load testing
Experience with data quality pipelines, drift detection, or evaluation-set curation and versioning
Familiarity with large-scale training or inference infrastructure (schedulers, multi-agent orchestration, sandboxed execution)
Prior experience as a dedicated reliability or operations owner embedded within a research team

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