Reinforcement Learning Engineer Job Description Template

The reinforcement learning engineer builds systems that learn from interaction rather than from labeled data. The domain is notoriously difficult to make work reliably: reward design produces unintended behavior, results are sensitive to seeds and hyperparameters, and simulation rarely transfers cleanly to reality. The role needs both method depth and unusual experimental patience.

Typical Duties and Responsibilities

• Design and implement reinforcement learning algorithms for specific problems
• Design reward functions and analyze the behavior they actually produce
• Build simulation environments and assess their fidelity
• Run experiments across seeds and configurations to establish real effects
• Address sample efficiency and training stability
• Investigate and mitigate reward hacking and degenerate policies
• Evaluate policies safely before any real world deployment
• Work on transfer from simulation to production conditions
• Document experiments, hyperparameters and results thoroughly
• Collaborate with domain experts on problem formulation

Education

• Master’s or PhD in machine learning, computer science, robotics or a related field

Required Skills and Experience

• 3+ years working with reinforcement learning
• Deep understanding of the main algorithm families and their trade offs
• Strong Python and a deep learning framework
• Experience designing reward functions and diagnosing their failures
• Rigorous experimental practice including multiple seeds
• Simulation environment experience
• Realism about the difficulty of deployment
• Careful documentation of experimental conditions

Preferred Qualifications

• Experience with offline reinforcement learning
• Robotics or control systems background