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Anthropic's Environments organization builds and maintains the infrastructure that improves Claude's capabilities through reinforcement learning. That includes the frameworks researchers use to build environments and the infrastructure responsible for running them. The team's mission is to productionize research. You'll embed with research teams, get up to speed on how they work, and design the frameworks and APIs that let them move faster, building systems the team can understand, own, and maintain themselves. Scope also includes keeping production RL runs healthy, maintainable, monitored, and easy to triage.

You'll be a strong fit if you have deep expertise in Python, a refined sense of taste for API and framework design, and good intuition for how complex systems fail, especially silently. It's a bonus if you've built and operated a stateful distributed system, such as a workflow engine, actor framework, or durable-execution runtime, where correctness depends on getting shared state and recovery right. You should be comfortable diving into messy research code, finding the abstractions that matter, and improving them incrementally while researchers continue to build on your work. You should also be comfortable using AI tools to accelerate your own development, but have an impulse towards deep verification. 

• 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

• Deep expertise in Python, including static typing, safe async and concurrency patterns, and writing performant code

• Strong taste in API and framework design, the ability to explain why an interface is right or wrong rather than just recognizing it, and a track record of other engineers or teams adopting and building on frameworks you have built

• Experience designing or operating stateful concurrent or distributed systems, and reasoning carefully about failure, retires, idempotency, and consistency

• A habit of verification: you measure before you conclude, and you build the checks that let a system show it's correct

• Experience working productively in large, evolving, or research-style codebases that you didn't originally write

• Strong written and verbal communication with collaborators of varied engineering backgrounds, and comfort with ambiguity: able to scope your own work from a loosely defined problem and drive it to a maintainable outcome

• 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, with good judgment about what to delegate and how to make the results verifiable

• 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 defining code standards adopted across teams, or prior experience as a technical lead

These are examples of the challenges the team tackles:

• Design a base RL environment abstraction that can be subclassed to support the large majority of environments built across RL

• Redesign the model-tool interface for sandboxed agentic environments so that state is guaranteed to survive serialization, making it structurally impossible to write a tool that silently loses state

• Design the state-sharing and recovery model for multi-agent workloads, so that losing a sandbox partway through a task becomes a transparent resume rather than lost work

• Define the failure and retry model for a sandboxed execution platform, distinguishing infrastructure faults from genuine task outcomes so that each is handled correctly

• Build the tooling that lets an environment owner diagnose why their environment is unhealthy in a production run, and fix it themselves