Reinforced Learning Engineer

Reinforcement Learning (RL) Engineer

Location: New York (Office)

Compensation: $600K - $800K

We are a team of elite builders and high-growth software developers moving at the speed of the most active markets in the world. As core contributors to one of the largest crypto social networks globally, we are redefining the intersection of social engagement and decentralized finance.

We are seeking a battle-tested Reinforcement Learning Engineer to take end-to-end ownership of an RL-driven trading agent. This agent will deploy real capital within a high-velocity ecosystem to drive volume and participation. This is not a research role; you will be the sole RL expert responsible for transitioning our existing heuristic systems into sophisticated, learning-based models. You will be expected to move fast, enforce strict risk guardrails, and ship code that directly impacts the global crypto economy.

Requirements

We are looking for an autonomous operator who has "skin in the game" experience. You should be comfortable working without a massive research organization or dedicated ML infrastructure team.

• Production RL Experience: You have previously put an autonomous learning system into production that directly controlled capital, pricing, traffic, or resources. You can clearly articulate what failed in production and how you fixed it.
• Risk Management: You have personally designed and enforced hard risk limits (capital caps, loss bounds, circuit breakers) in live systems. You understand the difference between a "risk-aware objective" and a hard-coded safety net.
• Evaluation Frameworks: You have built policy evaluation loops from scratch—including simulators, replay buffers, counterfactuals, or shadow deployments—prior to live rollout.
• Pragmatic Decision Making: You can defend uncomfortable tradeoffs (e.g., choosing a simple heuristic over deep RL) based on empirical results rather than academic ideology.
• Technical Independence: You are comfortable being the single owner of a complex ML system within a small team, managing everything from data and modeling to deployment and monitoring.

Requirements

• Ship RL Agents: Own the development and deployment of RL-driven trading agents using real capital to increase ecosystem volume.
• Policy Design: Design reward functions and policies that align with aggressive product goals while maintaining strict downside risk constraints.
• System Transition: Safely migrate existing heuristic-based production systems toward learning-based approaches without interrupting live operations.
• Validation & Simulation: Build robust offline evaluation and validation frameworks to minimize the need for risky, live sequential testing.
• Technical Leadership: Act as the internal authority on RL, ensuring the technical excellence, safety, and scalability of our automated trading infrastructure.

Benefits

• High-Stakes Environment: Work on one of the most successful and high-traffic platforms in the crypto space.
• True Ownership: Lead a critical technical vertical with zero red tape and immediate feedback loops from the market.
• Elite Talent: Collaborate with a lean, fast-moving team of world-class developers and builders.

Compensation: We offer a competitive package including:

• Competitive Base Salary.
• High-upside Equity/Token package.

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 [email protected].

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