Robotics Planning Engineer

Pronto • San Francisco, CA

1d

About The Position

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.
What You'll Build
Dynamic Path Planning — combinatoric planners that generate kinematically-feasible paths for 200+ ton trucks navigating dynamic zones with obstacle avoidance
Intersection Management — Multi-agent coordination systems that sequence trucks through shared road segments safely and efficiently
Fleet Dispatch — Assignment algorithms that route trucks to dump locations, manage queuing, and optimize throughput
Trail Processing — Path post-processing, smoothing, and validation pipelines that convert high-level routes into executable trajectories
What You’ll Build (—TT)
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.

Requirements

• 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

Nice To Haves

• Experience with multi-agent coordination or scheduling algorithms
• Familiarity with Dubins/Reeds-Shepp curves for nonholonomic 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

Responsibilities

• Design and implement motion planning algorithms for nonholonomic 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 51-100 employees