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

Baton Corporation Ltd; New York (NY); On-site; USD 110,000 - 160,000; Full time

Benefits offered by this job

Unmatched ownership and autonomy

Exposure to high-scale systems

Ability to ship fast and see impact

Job summary

Baton Corporation Ltd is seeking a Reinforcement Learning Engineer in New York to own a production trading system deploying real capital. Responsibilities include building RL-driven trading agents that enhance user participation in the memecoin ecosystem, designing reward functions, and enforcing strict risk limits. Candidates should have experience with autonomous learning systems and risk management in live scenarios. Join Baton for unmatched ownership, autonomy, and immediate impact in a fast-paced environment.

Qualifications

• Experience deploying autonomous learning systems in a production environment.
• Designed and enforced risk limits in live systems.
• Building policy evaluation loops and conducting empirical assessments.

Responsibilities

• Own and ship an RL-driven trading agent to increase trading volume.
• Design reward functions and enforce downside risk constraints.
• Build evaluation frameworks to minimize live testing dependency.

Skills

Reinforcement Learning

Risk Management

Policy Evaluation

Machine Learning

Job description

Who We Are

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.

What You’ll Do

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

Who You Are:

• 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.”

What it's like to work here

• 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

Why Join Us?

• 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.