Planning Engineer

Foundation Robotics Engineer

Foundation is developing the future of general purpose robotics and electric mobility with the goal to save lives and augment human labor.

Our mission is to create an autonomous, crewless ATV built to be truly all-terrain. Designed for commercial and defense use, it can save lives and take on heavy transport across mining, agriculture, construction, and more.

We are on the lookout for extraordinary engineers and scientists to join our team. Your previous experience in robotics isn't a prerequisite — it's your talent and determination that truly count.

We expect that many of our team members will bring diverse perspectives from various industries and fields. We are looking for individuals with a proven record of exceptional ability and a history of creating things that work.

Why We Are Hiring For This Role

• 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.

What Kind Of Person We Are Looking For

• Strong background in robotics, controls, or related fields (MSc or equivalent experience).
• Hands-on experience with motion planning algorithms.
• Proficiency in ROS2, C++, and Python.
• 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.