Staff Software Engineer, Learned & Hybrid Behavior Planning
Staff engineer at Kodiak Robotics integrating learned ML models into the behavior planning stack of its driverless trucking system. Day to day: leading planning-side ML integration, defining validation/safety and fallback criteria, and debugging model behavior via simulation and real autonomy data — primarily in C++ with Python a plus.
- Compensation
- $240K–$265K / yr
- Location
- Mountain View, CA
- Work setup
- Not specified
- Seniority
- Staff
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- 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++
- 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
Nice to have
- Python proficiency
Full description
Kodiak AI, Inc. (Nasdaq: KDK), founded in 2018, is a leader in Physical AI, developing driverless technology that powers machines that move. Its Kodiak Driver is a vehicle-agnostic autonomous driving system that combines AI-powered software with modular hardware. Today it operates in long-haul trucking, industrial trucking, and defense. In 2024, Kodiak launched commercial Driver-as-a-Service operations with no one in the cab, and plans to launch driverless long-haul service between Dallas and Houston by the end of 2026.
Kodiak has partnered with many of the biggest names in freight, including Bosch for scaled hardware production, AWS, and leading customers including Atlas Energy Solutions, IKEA, Bridgestone, Werner Enterprises, C.R. England, Roehl Transport, and General Dynamics Land Systems.
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.