Planning Engineer

We like to be frank and honest about who we are, so that people can decide for themselves if this is a culture they resonate with. Please read more about our culture here https://foundation.bot/culture.

• You deeply believe that this is the most important mission for humanity and needs to happen yesterday.
• You are highly technical - regardless of the role you are in. We are building technology; you need to understand technology well.
• You care about aesthetics and design inside out. If it's not the best product ever, it bothers you, and you need to “fix” it.
• You don't need someone to motivate you; you get things done.

• Set short and long term direction for planning architecture and priorities. 
• Design and implement motion planning algorithms for off-road ATV navigation.
• Integrate waypoints, obstacle avoidance, and terrain constraints into safe, drivable paths.
• Tune and validate control parameters for robust performance on rough, off-road terrain.
• Design test scenarios and evaluate planning algorithms in both simulation and field environments.
• Set real-time replanning policies and continuity rules so behavior remains stable as conditions change.
• Generate speed profiles from curvature, visibility, slope, and surface quality; respect slow/no-go zones and grade limits.
• Collaborate with vehicle engineering to create an integrated system, including sensor/compute selection and integration.
• Implement state-of-the-art approaches to trajectory planning, route planning, and real-time control. 

• Strong background in robotics, controls, or related fields (MSc or equivalent experience).
• Bachelor’s Degree or Master’s Degree candidate in Computer Science, Math, Electrical.
• Hands-on experience with motion planning algorithms.
• Proficiency in ROS2, C++, Python, internal middleware, and point cloud / occupancy representations, map / cost representations.
• Solid grasp of vehicle dynamics and path-following control.
• Comfort with real-world testing and debugging on robotic platforms.
• Experience integrating autonomy stacks on real robotic vehicles (not just simulation).
• Strong problem-solving skills with a bias toward practical, field-ready solutions over theory.
• Understanding of ADAS/AD systems and vehicle dynamics is a plus

• The annual compensation is expected to be between $175,000 - $300,000. Exact compensation may vary based on skills and experience.