Senior Algorithm Engineer, Reinforcement Learning

Job description

Job responsibilities

• Behavioral Modeling: Develop and train diverse, conditioned policies that simulate realistic driving behaviors to stress-test and validate our autonomous driving stack.
• Safety-Constrained Learning: Lead the research and implementation of advanced RL algorithms to ensure safety metrics are treated as primary constraints in the learning process.
• Reward & Objective Design: Collaborate with cross-functional teams to design robust reward functions and evaluation metrics that balance safety, progress, and comfort.
• Scalable Training Pipelines: Contribute to the optimization of our large-scale, high-throughput training environments to enable rapid iteration on complex multi-agent scenarios.
• Model Architecture: Advance our state-of-the-art neural architectures to improve spatial reasoning, long-horizon planning, and interaction modeling.
• Cross-Team Collaboration: Work closely with Simulation and Planning teams to integrate research-grade models into production-quality, safety-critical software.

Job requirements

• Professional RL Experience: Proven track record of training and deploying deep RL algorithms (e.g., PPO, SAC) for complex, real-world robotic or autonomous systems.
• Technical Mastery: Expertise in Python and PyTorch ; strong understanding of modern deep learning architectures and optimization techniques.
• Academic Background: MS or PhD in Computer Science, Robotics, or a related quantitative field.
• Scientific Intuition: Ability to diagnose and solve fundamental challenges in RL training, such as variance management and distribution shift.
• Safe RL Specialization: Experience with constrained optimization or safety-critical learning frameworks.
• Multi-Agent Systems: Background in MARL training stability, including self-play and decentralized execution strategies.
• Autonomous Driving Domain: Familiarity with vehicle dynamics and behavior planning, particularly for long-haul highway environments.