Staff Software Engineer, Environments Infrastructure

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

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. 

Key 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

Minimum qualifications

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

Preferred qualifications

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, …