Staff Software Engineer, Learned & Hybrid Behavior Planning

Kodiak•Mountain View, CA

6h•$240,000 - $265,000•Hybrid

About The Position

Kodiak Robotics, Inc., founded in 2018, is a leader in autonomous ground transportation, aiming for a safer and more efficient future. The company has developed an AI-powered technology stack for commercial trucking and the public sector, currently delivering freight daily across the southern United States. In 2024, Kodiak became the first to publicly announce delivering a driverless semi-truck to a customer and is also developing autonomous capabilities for the U.S. Department of Defense. We are seeking a Staff Software Engineer to shape the integration of learned models into behavior planning for autonomous driving. This role is at the intersection of Planning and Machine Learning, requiring close collaboration with ML engineers and autonomy teams to integrate learned components into a production autonomy stack. This is a high-impact role for an individual who understands both the practical constraints of real-world planning systems and the opportunities presented by modern learned models. You will influence how ML enhances autonomy behavior while ensuring new capabilities are safe, measurable, debuggable, and deployable.

Requirements

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 Haves

Python proficiency is a plus.

Responsibilities

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.