Anthropic’s Environments organization builds and maintains the infrastructure that improves Claude’s capabilities through reinforcement learning. The role involves designing APIs, frameworks, and abstractions for research teams, building tooling to help manage production RL runs, embedding with research teams, ensuring reliability, adopting new frameworks, and mentoring engineers and researchers across the organization.
Staff Software Engineer, Environments Infrastructure en Anthropic
Presencial - San Francisco, CA
Más vacantes en AnthropicSalary
USD 405,000 - 605,000
Requirements
Skills
- 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 adoption by other engineers or teams
- Experience designing or operating stateful concurrent or distributed systems, reasoning about failure, retries, idempotency, and consistency
- Habit of verification: building checks and measurements to ensure system correctness
- Experience working productively in large, evolving, or research-style codebases that were not originally written
- Strong written and verbal communication skills, ability to handle ambiguity and drive work from loosely defined problems to maintainable outcomes
- Experience building infrastructure, tooling, or frameworks for ML research or RL workflows, familiarity with agentic systems or LLM training pipelines
- Experience building agent frameworks, orchestration engines, or multi-agent systems, including checkpoint and restore, replay, coordination of long-running stateful processes
- Experience using AI coding tools on code where correctness matters, good judgment on delegation and verifiability
- 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 used across an organization
- Experience embedding with or consulting for other teams and handing off systems for others to own, or prior technical lead experience
Responsibilities
- 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 the 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 you rotate off
- Anticipate silent failure modes and prevent them structurally through type safety, well-designed invariants, targeted testing, and refactors that reduce the room for correctness issues
- Drive adoption of new frameworks across the organization, including deprecations and cutovers
- Help define the engineering standards, review practices, and design patterns for a new team, and mentor researchers and engineers in adopting them
Technologies
PythonStatic typingAsynchronous programmingConcurrency patternsDistributed systemsWorkflow enginesActor frameworksDurable execution runtimesReinforcement learningLarge language model training pipelinesAI coding toolsSandboxed execution platformsLarge-scale data processingDataset lifecycle managementData lineage systemsSerialization schemesPlugin systems
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