Reinforcement Learning Engineer job opportunity at MLabs.

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Date2026-03-10T11:46:44.321Z bot
MLabs Reinforcement Learning Engineer
Experience: General
Pattern: full-time
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degreeGeneral
loacation New York, United States Of America
loacation New York....United States Of America

Reinforcement Learning (RL) Engineer Location:  New York (Office) On-site | Full-time Compensation: Competitive Our client is an elite development firm and a high-growth software company responsible for building the infrastructure behind the world’s largest crypto social networks and digital asset launchpads. Operating at the frontier of decentralized finance, the organization is composed of a mission-driven group of builders who prioritize speed, technical excellence, and talent density. The organization is seeking a Reinforcement Learning (RL) Engineer to take end-to-end ownership of an RL-driven trading agent. This individual will manage real capital to increase trading volume and user participation within a high-velocity memecoin ecosystem. This is a high-stakes role designed for a "single-owner" expert who can bridge the gap between sophisticated modeling and live financial production. The successful candidate will transition existing heuristic-based systems toward learning-based approaches while enforcing rigorous risk parameters in a 24/7 global market. 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. 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. Production Experience: Proven track record of deploying autonomous learning systems into production environments that directly controlled capital, pricing, traffic, or resources. Candidates must be able to demonstrate a deep understanding of system failures and subsequent remediation. 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. Work Style: Comfort with an intense, fast-paced environment where expectations are high and impact is immediate. Our client operates primarily in-person .

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