Robotics Planning Engineer - San Francisco, CA

We're looking for a Robotics Planning Engineer to develop the high-level autonomy systems that coordinate fleets of autonomous haul trucks in mining environments. You'll work on path planning, multi-vehicle coordination, and dispatch systems that operate at the site level — deciding where trucks go, when they go, and how they interact with each other.

• →Design and implement motion planning algorithms for non-holonomic vehicles
• →Develop multi-agent coordination systems that prevent deadlocks and collisions
• →Build simulation and visualization tools for validating planning algorithms
• →Optimize planning algorithms for real-time performance in production environments
• →Collaborate with controls engineers to ensure planned paths are executable
• →Debug fleet-level issues using logged data and replay tools
• →Travel note: This role requires periodic travel to customer sites (up to 5%)
• →Schedule note: Some schedule flexibility may be required during deployments

• Motion planning — A robust stack, from path planning to trajectory optimization, to generate smooth and safe trajectories for 200+ ton trucks to follow.
• Coordination planning — Systems to simultaneously coordinate the motion of multiple vehicles with intersecting trajectories to avoid collision and maximize throughput.
• Fleet planning — Algorithms that dynamically translate the site-wide state, like loading and dumping locations, to actively managed assignments for each truck.

• BS/MS/PhD in Robotics, Computer Science, or related field
• 2+ years of professional (non-internship) software development experience
• Strong foundation in motion planning algorithms
• Experience with computational geometry (collision detection, polygon operations)
• Proficiency in Python and NumPy for numerical computing
• Understanding of vehicle kinematics and nonholonomic constraints
• Ability to analyze algorithm complexity and optimize for real-time performance

• Experience with multi-agent coordination or scheduling algorithms
• Familiarity with Dubins/Reeds-Shepp curves for non-holonomic planning
• Background in trajectory optimization (DCBF, MPC-based planners)
• Experience with graph algorithms (Dijkstra, heuristic search)
• Knowledge of GEOS, Shapely or other computational geometry libraries
• Experience with fleet management or dispatch systems
• Familiarity with Redis, ZeroMQ, or similar infrastructure
• Familiarity with modern ML techniques for planning problems

• Languages: Python (primary), C++ (performance-critical modules)
• Libraries: NumPy, Shapely, Numba, SciPy
• Testing: Simulation replay, config-driven scenario testing

This role is based in our San Francisco office location. As a company driven by innovation and continuous change, close collaboration is essential. We’re constantly reimagining our industry, creating new products, and refining our processes, and we do our best work together. That’s why all of our office-based teams work onsite, five days a week. 

The base salary range for this role is $182,000 - $230,000 per year.

Actual compensation will be determined on an individual basis and may vary depending on experience, skills, and qualifications.

Base salary is just one part of your total rewards package. You may also be eligible for equity awards and an annual performance-based bonus.

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