About The RoleYou will design and ship the algorithms that decide how our trucks move. This spans classical motion planning and behavior, a growing learning-based prediction and planning pipeline, and the online mapping and map-building layer that planning depends on. You will work end to end - from problem framing and algorithm design through implementation, on-vehicle validation, and the cases that come back from the road. This is a hands-on engineering role for someone who wants their work driving real freight on public highways.
What You'll Do- Develop and improve motion planning and behavior algorithms for highway and surface-street driving.
- Build the learning-based pipeline for prediction and planning: data curation, model design and training, and integration on-vehicle.
- Advance online mapping and map-building, and the real-time map signals that planning consumes.
- Turn road and simulation cases into root-cause analysis, algorithmic fixes, and regression coverage that prevents recurrence.
- Collaborate across perception, control, simulation, and operations to take features from design through validated release.
Required Qualifications- B.S. or M.S. in Computer Science, Robotics, Electrical Engineering, Applied Math, or a related field, or equivalent practical experience.
- 2+ years building production algorithms in robotics, autonomous vehicles, or a comparable real-time system.
- Strong C++ and Python; comfortable owning performance-sensitive code that runs on-vehicle.
- Solid foundation in at least one of: motion planning and optimization, prediction/behavior modeling, or mapping.
- A track record of shipping algorithms that ran on real hardware or in production, not only in research or simulation.
Preferred Qualifications- Experience with learning-based components in a planning or prediction stack (sequence/trajectory models, imitation or reinforcement learning, model training and deployment pipelines, GPU inference in a real-time loop).
- Experience with online/HD mapping, reference-line or lane-graph generation.
- Familiarity with the planning-perception interface: uncertainty representation, occlusion reasoning, safety and liability evaluation, and agent modeling.
- Experience operating in a safety-critical or heavily validated software environment.
- Background in autonomous trucking or highway-speed autonomy.
