Reinforcement Learning Engineer

Key Responsibilities

• Design and implement reinforcement learning solutions for sequential decision-making problems in real and simulated environments
• Develop, calibrate, and maintain simulation environments suitable for large-scale agent training
• Implement and evaluate modern RL algorithms including policy gradient, actor-critic, off-policy, and offline RL methods
• Engineer reward functions and shaping strategies that align agent behavior with desired outcomes and safety constraints
• Apply offline RL and imitation learning techniques where exploration is costly or unsafe
• Use RLHF, DPO, and related techniques for fine-tuning large language models when relevant
• Engineer scalable training infrastructure for distributed RL, including efficient experience collection and replay systems
• Optimize training stability and sample efficiency through algorithmic and engineering improvements
• Design rigorous evaluation protocols, including out-of-distribution and adversarial test cases
• Implement safety mechanisms such as constraint enforcement, conservative policies, and human-in-the-loop oversight
• Collaborate with applied scientists and product teams to identify high-value RL use cases
• Maintain monitoring and alerting systems for deployed policies and models in production
• Document methodology, design decisions, and operational characteristics for internal stakeholders
• Stay current with RL research and translate promising techniques into production-ready solutions

Required Qualifications

• Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent applied experience
• Six or more years of combined RL research and engineering experience
• Strong proficiency in Python and modern deep learning frameworks
• Hands-on experience with at least one major RL library or in-house RL stack
• Solid understanding of probability, optimization, and the theoretical foundations of RL
• Experience designing and tuning reward functions in non-trivial environments
• Familiarity with simulation environments and large-scale experience collection
• Experience training neural network policies on GPU clusters
• Strong written and verbal communication skills
• Track record of shipping or publishing impactful RL work

Preferred Qualifications

• Experience with RLHF for large language models
• Familiarity with multi-agent RL or hierarchical RL
• Exposure to robotics, control systems, or autonomous driving
• Publications in RL or related research venues
• Open-source contributions to RL libraries or environments