Baton Corporation

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

2mo ago

400000 –800000 USD / yearUSASeniorreinforcement learningsimulationdeploymentmonitoringrisk management

Build and deploy robust reinforcement learning systems for real capital trading in memecoin ecosystem.

Baton Corporation is the development company that builds and operates the entire technology stack behind pump.fun , the largest memecoin launchpad in production today. The systems are low latency, high throughput, live under constant load, and break if you get them wrong.

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.”

Unmatched ownership and autonomy
Exposure to systems operating at the edge of crypto scale
The ability to ship fast and see real-world impact immediately
If you’re motivated by responsibility, speed, and building products used by massive audiences, you’ll feel at home here.

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
We work in person
Hours can be long and unconventional
The pace is intense
Expectations are high, and impact is immediate
Working at Baton is not for everyone