Machine Learning Engineer - Reinforcement Learning

Jobgether • United States

Remote

This Job is No Longer Active This position is no longer accepting applications

AI Summary

Design, train, and deploy production-ready reinforcement learning systems for complex decision-making challenges. Own the full lifecycle of RL solutions from research to deployment, including scalable infrastructure and safety mechanisms. Requires advanced ML expertise, strong Python engineering skills, and experience with RL algorithms and simulation environments.

Key Highlights

Build advanced RL systems solving complex decision-making problems

Full lifecycle ownership from research to production deployment

Experience with RLHF, DPO, and large-scale model optimization

Key Responsibilities

Design and implement RL solutions for sequential decision-making problems

Develop and optimize simulation environments for large-scale agent training

Implement and assess modern RL algorithms including policy gradient and actor-critic approaches

Design reward functions aligning model behavior with performance goals and safety requirements

Build scalable RL infrastructure including distributed training systems and replay mechanisms

Establish evaluation frameworks for robustness testing and adversarial scenarios

Develop safety mechanisms such as policy constraints and human oversight workflows

Collaborate with research, engineering, and product teams to deliver RL applications

Monitor deployed models for performance drift and reliability issues

Document technical approaches and system architecture

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Technical Skills Required

Python Reinforcement Learning Simulation Environments

Benefits & Perks

Fully remote position within the United States

Competitive annual salary range of $100,000-$150,000

Opportunity to work on advanced AI and RL initiatives

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Nice to Have

Experience with multi-agent reinforcement learning

Hierarchical RL

Robotics or autonomous systems

DPO techniques

Job Description

This role offers the opportunity to build advanced reinforcement learning systems that solve complex decision-making challenges beyond traditional machine learning approaches.You will design, train, and deploy RL-based models that create measurable impact in real-world applications.The position combines deep research knowledge with hands-on engineering, requiring the ability to move solutions from experimentation into production.You will work on scalable training pipelines, simulation environments, reward modeling, and policy optimization.The role provides exposure to cutting-edge AI techniques, including reinforcement learning from human feedback and large-scale model optimization.

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