about 6 hours ago
Responsibilities
- Develop and train diverse, conditioned policies for realistic driving behaviors.
- Lead research and implementation of advanced RL algorithms with safety metrics as primary constraints.
- Collaborate with teams to design robust reward functions and evaluation metrics.
- Contribute to the optimization of large-scale training environments for multi-agent scenarios.
- Advance neural architectures to improve spatial reasoning and interaction modeling.
- Work closely with Simulation and Planning teams to integrate models into production software.
Requirements
- Proven experience in training and deploying deep RL algorithms for real-world systems.
- Expertise in Python and PyTorch, with a strong understanding of deep learning architectures.
- MS or PhD in Computer Science, Robotics, or a related quantitative field.
- Ability to diagnose and solve challenges in RL training, such as variance management.
Benefits
- Comprehensive health insurance.
- Paid time off.
- Opportunities for performance bonuses and equity.