Sunset RL Environment Engineer at Replay responsible for designing and building production-grade reinforcement learning environments based on real enterprise workflows and data. Collaborates with frontier AI labs to improve model capabilities.
Responsibilities
Replay is hiring an RL Environment Engineer who has already built production-grade RL
environments and wants to point that craft at real enterprise data to shape our datasets into the training environments frontier AI labs use to make their models better. You'll turn real business workflows and real-world data into realistic, verifiable environments, with the tasks, tools, and graders to match, and work directly with lab researchers to understand which capabilities they want to improve. This is a hands-on engineering role with a high degree of ownership.
Design and build production-grade RL environments grounded in real enterprise workflows, from task spec through to a stable, versioned release
Write reward functions and programmatic verifiers that score outcomes reliably, and harden them against reward hacking and shortcut solutions
Build high-fidelity evaluation environments with seeded failure modes that expose subtle regressions, and reliable model and agent harnesses with bounded behaviour and explicit output verification
Turn Replay's de-identified data into realistic state, tools, files and context for an agent, and keep environments reproducible, observable and safe to roll out, with clear evidence of what changed between versions
Work directly with frontier lab researchers to understand the capability gaps they want to close, and design task suites that expose and train against them
Measure task difficulty and solvability by running frontier models against environments, then tune them until they give a usable training signal
Ship quickly, and help set the standards and patterns for how Replay builds tasks and environments
Required aptitude
3-5+ years of ML / software engineering experience shipping production systems
Personally built production-grade RL environments that were used in real training or evaluation, not only prototypes or research demos
Qualification
Required
3-5+ years of ML / software engineering experience shipping production systems
Experience personally building production-grade RL environments
