3 months ago
Houston, TX, USA or San Francisco, CA, USASenior
Responsibilities
- Develop and train conditioned policies that model realistic driving behaviors for autonomous-driving stack validation.
- Research and implement reinforcement-learning algorithms with safety metrics as primary constraints.
- Design reward functions and evaluation metrics balancing safety, progress, and comfort.
- Optimize large-scale, high-throughput training environments for complex multi-agent scenarios.
- Advance neural architectures for spatial reasoning, long-horizon planning, and interaction modeling.
- Integrate research-grade models into production-quality safety-critical software with Simulation and Planning teams.
Requirements
- Proven experience training and deploying deep reinforcement-learning algorithms such as PPO and SAC for complex real-world robotic or autonomous systems.
- Expertise in Python and PyTorch, with a strong understanding of modern deep-learning architectures and optimization techniques.
- MS or PhD in Computer Science, Robotics, or a related quantitative field.
- Ability to diagnose and solve reinforcement-learning challenges including variance management and distribution shift.
- Experience with constrained optimization or safety-critical learning frameworks is preferred.
- Experience with multi-agent reinforcement-learning stability, self-play, and decentralized execution is preferred.
- Familiarity with vehicle dynamics and behavior planning for long-haul highway environments is preferred.
Benefits
- Comprehensive health insurance and paid time off.
- Opportunity to work on autonomous trucking technology.
- Performance bonuses and equity opportunities are mentioned.
- Salary is competitive based on experience, but no base-pay amount is specified.
Categories
ML EngineeringRobotics
About Bot Auto
Bot Auto develops and operates Level 4 autonomous trucks, selling freight capacity as Transportation-as-a-Service to shippers rather than licensing technology. The Houston-based, privately held company (founded 2023) owns the trucks and runs end-to-end operations, pairing fleet operators with autonomy engineers to deploy long-haul routes. Its platform spans autonomy software, vehicle controls, sensors, compute, and fleet operations with a documented safety case for commercial freight service.