Reinforcement Learning Engineer
The Reinforcement Learning (RL) Engineer will take end-to-end ownership of an RL-driven trading agent that utilizes real capital to drive ecosystem engagement within a high-velocity memecoin ecosystem.
Key Responsibilities
Autonomous Agent Development: Own the design, shipment, and iteration of an RL-driven trading agent that utilizes real capital to drive ecosystem engagement.
Objective Function Design: Design reward functions and policies that align strictly with product goals while implementing and enforcing absolute downside risk constraints.
Validation Frameworks: Build robust evaluation and validation frameworks, including simulation and offline analysis, to minimize reliance on live sequential testing.
System Transition: Manage the safe transition of existing heuristic-based production systems toward advanced learning-based approaches.
Technical Leadership: Serve as the sole RL expert within a small, high-caliber team, maintaining responsibility for the entire lifecycle—from data modeling and deployment to monitoring and safety safeguards.
Requirements
Production Experience: Proven track record of deploying autonomous learning systems into production environments that directly controlled capital, pricing, traffic, or resources.
Risk Management: Hands-on experience designing and enforcing hard risk limits, such as capital caps, loss bounds, and circuit breakers, within a live financial or resource-based system.
Evaluation Loop Mastery: Experience building policy evaluation loops from scratch, including simulators, replay, counterfactuals, and shadow deployments, prior to live rollout.
Empirical Judgment: Ability to make and defend pragmatic technical tradeoffs (e.g., opting for heuristics over RL or bandits over deep RL) based on empirical results rather than theoretical preference.
Operational Independence: Demonstrated experience as the primary owner of a complex ML system within a lean environment, operating without the support of dedicated research organizations or external ML platforms.
Interview Process
Recruiter / HR Call: Initial screening to discuss professional background, risk management philosophy, and cultural alignment.
Technical Interview: A deep-dive assessment into RL architecture, simulation frameworks, and live production experience.
Final Interview: A strategic discussion with leadership focusing on mission alignment, role expectations, and long-term objectives.
Benefits
High-Stakes Autonomy: Unmatched ownership over an RL agent managing real-world capital and massive user traffic.
Scale Exposure: Direct involvement with systems operating at the absolute edge of crypto and financial technology scale.
Elite Talent Density: Opportunity to collaborate with a mission-driven group of engineers who value first-principles thinking.
Immediate Impact: The ability to ship fast and see real-world results and market reactions instantly.
Compensation
A competitive package including Base Salary plus Equity/Tokens.