Staff Software Engineer, Learned & Hybrid Behavior Planning

Location: Mountain View, CA

Department: Motion Planning

We are looking for a Staff Software Engineer to help shape how learned models are integrated into behavior planning for autonomous driving. In this role, you will sit at the intersection of Planning and Machine Learning, working closely with ML engineers and autonomy teams to bring learned components into a production autonomy stack.

This is a high-impact role for someone who understands both the practical constraints of real-world planning systems and the opportunities enabled by modern learned models. You will help shape how ML improves autonomy behavior while ensuring that new capabilities are safe, measurable, debuggable, and deployable.

What You’ll Do

• Lead Planning-side integration of learned models into the behavior planning stack.
• Collaborate closely with ML teams on model improvements, requirements, evaluation, and deployment.
• Work on learned planning components as well as other ML-driven planning signals, such as behavior classification, actor intent understanding, and data-driven decision-making.
• Design integration strategies that balance learned components with existing heuristic planning systems.
• Define validation, fallback, monitoring, and safety criteria for learned planning components.
• Debug and analyze model behavior using simulation, logs, metrics, and real-world autonomy data.
• Partner with cross-functional teams across Perception, ML, Planning, Simulation, Systems, and Safety.
• Lead technical designs and mentor other engineers.

What We’re Looking For

• Strong experience in autonomous vehicles, robotics, or a related autonomy domain.
• Deep technical background in behavior planning, decision-making, or motion planning.
• Strong software engineering skills with proficiency in C++. Python proficiency is a plus.
• Experience working 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.
• Excellent cross-functional communication and technical leadership skills.