Reinforcement Learning Engineer at MLabs

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

03 Jun, 26

Salary

0.0

Posted On

05 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Reinforcement Learning, Autonomous Agent Development, Objective Function Design, Validation Frameworks, Simulation, Offline Analysis, Risk Management, Capital Management, Heuristic Systems, Policy Evaluation, System Transition, Technical Leadership, Data Modeling, Deployment, Financial Production

Industry

IT Services and IT Consulting

Description

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. 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. Due to the high volume of applications we anticipate, we regret that we are unable to provide individual feedback to all candidates. If you do not hear back from us within 4 weeks of your application, please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish you the best in your job search. Commitment to Equality and Accessibility: At MLabs, we are committed to offer equal opportunities to all candidates. We ensure no discrimination, accessible job adverts, and providing information in accessible formats. Our goal is to foster a diverse, inclusive workplace with equal opportunities for all. If you need any reasonable adjustments during any part of the hiring process or you would like to see the job-advert in an accessible format please let us know at the earliest opportunity by emailing human-resources@mlabs.city. MLabs Ltd collects and processes the personal information you provide such as your contact details, work history, resume, and other relevant data for recruitment purposes only. This information is managed securely in accordance with MLabs Ltd’s Privacy Policy and Information Security Policy, and in compliance with applicable data protection laws. Your data may be shared only with clients and trusted partners where necessary for recruitment purposes. You may request the deletion of your data or withdraw your consent at any time by contacting legal@mlabs.city.

Responsibilities

The engineer will take end-to-end ownership of an RL-driven trading agent, managing real capital to increase trading volume and user participation within a high-velocity memecoin ecosystem. Key duties include designing reward functions, building robust validation frameworks, and safely transitioning existing heuristic systems toward learning-based approaches.