RL Environment Engineer

ABOUT REPLAY

At its core, Replay was founded to help founders. We started by supporting startups through shutting down, but we've since expanded into unlocking a new revenue stream for all types of businesses.

In 2025, we earned a secret: the data every company generates on a daily basis (collaboration, communication, building) is some of the most valuable training data in the world. Public and synthetic can only get frontier models so far, so the next generation of model progression depends on real, proprietary data grounded in how actual businesses operate. We're a primary source of it, partnering directly with the frontier AI labs building what's next.

WHY JOIN REPLAY NOW:

- We've scaled from $0 to a multi-eight figures run rate in a matter of months - We've raised from top-tier investors (Floodgate, Afore, Ludlow, Hustle Fund, etc.) - We're small enough that you'll carry outsized responsibility and grow as fast as the company does - You'll partner and build for the fastest and most important companies in the world - You'll be a part of building a massive, category-defining business from the ground floor

THE ROLE

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.

WHAT YOU'LL DO

- 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