At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a startup and the wisdom of seasoned experts, our team has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create groundbreaking solutions that propel the future of transportation. Join us and transform your ideas into reality.
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.
- 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.
- 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.
