Environment Engineer
Reinforced Learning · Buffalo-Niagara Falls Area · 3 days ago
EngineeringFull-time
The role Environments are where our customers’ agents learn. Each one is a stateful world with real tools, a hard task and a verifier that can’t be gamed. You’ll build them: the services behind the tools, the state that resets cleanly between episodes, and the task versions that stay reproducible months later. What you’ll own Stateful environments with working tool interfaces: terminals, editors, spreadsheets, ticketing systems, internal APIs.Snapshot and reset, so every episode starts from a known state and finishes with a checkable one.Versioning of tasks, fixtures and verifiers together, so a result from March can be reproduced in September.Episode instrumentation, so researchers can replay what an agent saw, did and was rewarded for.Turning a written task into something that runs, with task designers and domain experts. What you bring Strong backend engineering in Python or TypeScript, with containers and process isolation.You’ve built systems other people run against: APIs, sandboxes, test harnesses, simulators.You care about determinism. Flaky state bothers you.You can read a trajectory and tell whether the environment or the agent went wrong. Helpful, not required Agent frameworks, tool-calling APIs or RL training loops.A mock of a real system you built that was convincing enough to fool a model.