Reinforcement Learning Engineer ($400k - $800k salary)

As our Reinforcement Learning Engineer, you will own a production trading system that directly deploys real capital. This is not a research role - it's about building learning systems that are robust, measurable, and safe under real-world constraints.

- Own and ship an RL-driven trading agent using real capital to increase trading volume and user participation in a memecoin ecosystem

- Design reward functions and policies aligned with product goals while enforcing strict downside risk constraints

- Build evaluation and validation frameworks (simulation, offline analysis) to minimize reliance on live sequential testing

- Safely transition an existing heuristic-based production system toward learning-based approaches

- Take end-to-end ownership and technical leadership as the sole RL expert, from data and modeling through deployment, monitoring, and safeguards

- You have previously put an autonomous learning system into production that directly controlled capital, pricing, traffic, or resources and can explain what broke and how they fixed it

- Have personally designed and enforced hard risk limits (capital caps, loss bounds, circuit breakers) in a live system, not just talked about "risk-aware objectives.

- Have built a policy evaluation loop from scratch (simulators, replay, counterfactuals, shadow deployments) before trusting live rollout.

- Can make and defend uncomfortable tradeoffs (e.g. heuristic > RL, bandit > deep RL) based on empirical results instead of ideology

- Have operated as the single owner of a complex ML system in a small team, with no safety net of research orgs, infra teams, or "ML platforms."