Job for Experts
Reinforced Learning Engineer
About the Role
You will own and ship an RL-driven trading agent that uses real capital to increase trading volume and user participation in a memecoin ecosystem. You will design reward functions and policies aligned with product goals while enforcing strict downside risk constraints. You will build evaluation and validation frameworks (simulation and offline analysis) to minimize live sequential testing. You will safely transition an existing heuristic-based production system toward learning-based approaches. You will take end-to-end ownership as the sole RL expert, covering data, modeling, deployment, monitoring, and safeguards.
Requirements
- Has put an autonomous learning system into production that directly controlled capital, pricing, traffic, or resources and can explain failures and fixes
- Has personally designed and enforced hard risk limits (capital caps, loss bounds, circuit breakers) in a live system
- Has built a policy evaluation loop from scratch (simulators, replay, counterfactuals, shadow deployments) before trusting live rollout
- Can make and defend empirical tradeoffs (e.g. heuristic > RL, bandit > deep RL)
- Has operated as the single owner of a complex ML system in a small team without dedicated research or infra orgs
Responsibilities
- Own and ship an RL-driven trading agent using real capital
- Design reward functions and policies aligned with product goals
- Enforce strict downside risk constraints such as capital caps and circuit breakers
- Build evaluation and validation frameworks including simulation and offline analysis
- Safely transition heuristic-based production systems to learning-based approaches
- Take end-to-end ownership from data and modeling through deployment, monitoring, and safeguards