Baton Corporation
Reinforcement Learning Engineer ($400k - $800k salary)
About this role
Baton Corporation seeks a Reinforcement Learning Engineer to own and deploy a production RL-based trading agent that directly manages real capital in their memecoin launchpad ecosystem. You'll design reward functions, build robust evaluation frameworks, and safely transition from heuristic systems to learning-based approaches while enforcing strict risk controls.
What you'll do
- Own end-to-end development and deployment of an RL trading agent with real capital allocation
- Design reward functions and policies that balance product goals with strict downside risk constraints
- Build evaluation frameworks (simulation, offline analysis, shadow deployments) to validate systems before live rollout
- Safely migrate existing heuristic-based production systems toward learning-based approaches
- Implement and enforce hard risk limits including capital caps, loss bounds, and circuit breakers
- Monitor, debug, and maintain the trading system in production with minimal supporting infrastructure
What they're looking for
- Reinforcement learning design and implementation
- Production ML systems and deployment
- Risk management and constraint enforcement
- Trading systems and financial risk controls
- Simulation and offline policy evaluation
- Python and systems-level programming
- Full-stack ML ownership (data through monitoring)
- Pragmatic decision-making under uncertainty
Benefits
- Competitive salary: $400k–$800k
- Complete technical ownership and autonomy
- Exposure to high-scale crypto infrastructure
- Immediate real-world impact on live trading systems
- Opportunity to ship fast in an intense, high-impact environment
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Baton Corporation
Baton Corporation builds pump.fun, a memecoin launchpad platform, and is hiring full stack engineers to develop production features across their technology stack and reinforcement learning engineers to design and deploy trading agents that manage capital within their ecosystem.
View all jobs at Baton CorporationLikely interview questions
- Walk us through a production RL or autonomous learning system you deployed that directly controlled capital, pricing, or resources. What broke, and how did you fix it?
- Describe a time you designed and enforced hard risk constraints (capital caps, loss bounds, circuit breakers) in a live system. How did you balance learning with safety?