Staff Software Engineer, Environments Infrastructure

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. The Environments organization builds and maintains the infrastructure that improves Claude's capabilities through reinforcement learning, including the frameworks researchers use to build environments and the infrastructure responsible for running them. The team's mission is to productionize research.

• Design widely used APIs, frameworks, and abstractions that other engineers and researchers build on, making correct usage the default and ruling out entire classes of errors structurally
• Own the platform layers that sit beneath every environment, including the agent runtime
• Build tooling that lets environment owners understand, debug, and maintain their environments in production without needing an infrastructure engineer in the loop
• Embed with research teams on a rotational basis, work directly in their codebases without slowing down the research they support, and transfer ownership when rotating off
• Anticipate silent failure modes and prevent them structurally through type safety, well-designed invariants, targeted testing, and refactors
• Drive adoption of new frameworks across the organization, including deprecations and cutovers
• Help define engineering standards, review practices, and design patterns for a new team, and mentor researchers and engineers

• Deep expertise in Python, including static typing, safe async and concurrency patterns, and writing performant code
• Strong taste in API and framework design, with a track record of other engineers or teams adopting frameworks you have built
• Experience designing or operating stateful concurrent or distributed systems, reasoning carefully about failure, retries, idempotency, and consistency
• A habit of verification: measure before concluding, and build checks that let a system show it's correct
• Experience working productively in large, evolving, or research-style codebases you didn't originally write
• Strong written and verbal communication with collaborators of varied engineering backgrounds, and comfort with ambiguity

• Experience building infrastructure, tooling, or frameworks for machine learning research or RL workflows, and familiarity with agentic systems or LLM training pipelines
• Experience building agent frameworks, orchestration engines, or multi-agent systems, including checkpoint and restore, replay, and coordination of long-running stateful processes
• Experience using AI coding tools on code where correctness matters
• Experience building client libraries or SDKs on top of sandboxed, containerized, or remote execution platforms
• Experience with large-scale data processing, dataset lifecycle management, or data lineage systems
• Experience designing serialization schemes, plugin systems, or extensible class hierarchies
• Experience embedding with or consulting for other teams, defining code standards, or prior experience as a technical lead

Examples include designing a base RL environment abstraction, redesigning the model-tool interface for sandboxed agentic environments, designing state-sharing and recovery models for multi-agent workloads, defining failure and retry models for a sandboxed execution platform, and building tooling for diagnosing unhealthy environments in production.

Annual salary: $405,000—$605,000 USD. Minimum education: Bachelor's degree or equivalent combination of education, training, and/or experience. Location-based hybrid policy: all staff are expected to be in one of Anthropic's offices at least 25% of the time. Visa sponsorship is available.