RL Environment Engineer

About the role

ABOUT REPLAY

We are building the first-ever bank for enterprise data. There are over 35 million businesses in the US alone, many of which are sitting on an asset they don’t know they have. At Replay, we help businesses monetize their historical operating data by licensing it to the frontier AI and data labs for model training. Replay sits at the intersection of advancing the frontier of AI, while helping companies generate millions in growth capital without having to give up equity, take on debt, or lay off employees.

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 RL Environment Engineers who are looking to take real-world, enterprise data and convert it into model progression. You’ll recreate the entire world of businesses across industries — crafting the highest quality environments and evals while working directly with researchers to understand the capabilities they’re looking to hillclimb.

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

- 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

- Ability to turn ambiguous goals from researchers into well-scoped, working systems

- A high-ownership, action-oriented mindset: you take problems from idea to shipped.

- Startup experience, a taste for broad ownership and the ability to do well when requirements are evolving

- Clear written and verbal communication with researchers, engineers, and non-technical partners