Staff Software Engineer, Learned & Hybrid Behavior Planning

You will integrate learned models into the behavior-planning stack and work with machine-learning partners on model requirements, evaluation, and deployment. You will design hybrid planning strategies, define validation and safety criteria, analyze model behavior, lead technical designs, and mentor engineers.

Responsibilities

• Lead planning-side integration of learned models into behavior planning
• Collaborate on model improvements, requirements, evaluation, and deployment
• Develop learned planning components and ML-driven planning signals
• Design strategies that balance learned components with heuristic planning systems
• Define validation, fallback, monitoring, and safety criteria
• Debug and analyze model behavior using simulation, logs, metrics, and autonomy data
• Lead technical designs and mentor engineers

Requirements

• Experience in autonomous vehicles, robotics, or a related autonomy domain
• Technical background in behavior planning, decision-making, or motion planning
• C++ proficiency
• Experience with heuristic or classical planning systems
• Experience integrating or developing learned behavior policies, behavior classification, trajectory prediction, or actor-intent models
• Ability to reason about safety, system behavior, evaluation, and deployment risk
• Cross-functional communication and technical leadership