Responsibilities
- Design and implement classical or ML motion planners for fallback and minimal-risk maneuvers
- Build planners that operate reliably under degraded perception, partial observability, and system faults
- Define and execute safe, deterministic vehicle motions such as controlled slow-downs, pull-overs, and safe stops
- Use large-scale simulation and real-world data to evaluate planner behavior and guide parameter tuning
- Develop metrics, analysis tools, and dashboards to understand planner performance at scale
- Collaborate closely with behavior prediction, perception, controls, safety, and remote assistance teams
- Contribute to a reusable fallback platform used across trucking and other autonomy programs
Requirements
- 5+ years of experience in motion planning for autonomous vehicles or robotics
- Strong foundation in robotic motion planning algorithms and trajectory generation (optimization-, search-, or rule-based)
- Experience building deterministic, safety-critical planning systems
- A data-driven mindset for large-scale evaluation, debugging, and tuning of planning behavior
- Proficiency in C++ and experience working in real-time systems
- Strong systems thinking and cross-functional collaboration skills
Nice to Have
- Experience designing minimal-risk maneuvers (MRM) or emergency handling behaviors
- Familiarity with AV safety concepts, ODD constraints, or safety-case-driven development
- Experience using ML techniques for parameter tuning, calibration, or offline optimization
- Experience working with degraded sensors, uncertainty, or human-in-the-loop systems
- Background in simulation frameworks or large-scale log analysis
Compensation
Base salary range: $151,000 - $240,000 USD annually. Includes equity, comprehensive health/dental/vision insurance, 401k with employer match, learning/wellness stipends, and paid time off.