Research Engineer, RL Infrastructure and Reliability (Knowledge Work)

Details

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. 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. This role owns the reliability, observability, and infrastructure foundation that the team's research depends on, ensuring training and evaluation runs remain stable, well-instrumented, and high-quality as they grow in scale and complexity.

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 training environments and evaluation systems, focusing on high signal-to-noise: a small set of trusted metrics and alerts.
• Proactively harden environments and evaluation systems through load testing, fault injection, and stress testing at realistic scale so failures surface early.
• Act as the primary point of contact for partner training and infrastructure teams when issues arise and drive incidents to resolution.
• Reduce the operational burden on researchers so they can stay focused on research.

Requirements

Minimum Qualifications

• Highly experienced Python engineer who ships reliable, well-instrumented code trusted 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 — familiarity with SLOs, load tests, and failure injection.
• Foundational ML knowledge sufficient to understand what a training environment or evaluation is measuring and to recognize when an evaluation has become stale or gameable.
• Ability to read research code and reason about evaluation integrity.

Preferred Qualifications

• 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.

Compensation

Annual Salary: $350,000 - $850,000 USD

Logistics

• 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.
• Location-based hybrid policy: staff are expected to be in one of Anthropic's offices at least 25% of the time; some roles may require more time in office.
• Visa sponsorship: Anthropic states they do sponsor visas and will make reasonable efforts to obtain a visa for candidates they hire (they retain an immigration lawyer to help).