RL Environment Engineer
Position Summary & Responsibilities
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,
Key Role Competencies & Evaluation Highlights
Assessing rigorous engineering execution, deep systems architecture ownership, and the ability to thrive under high ambiguity with minimal oversight.
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