/roles — ROLE_284
Robotics Planning Engineer
Well-funded robotics group running driverless haul trucks at mine sites in several countries
The role
- COMP
- $145K - $175K
- EQUITY
- Competitive equity
- LOCATION
- San Francisco
- WORKPLACE
- On-site
- EXPERIENCE
- 2 - 4 years
- VISA
- None, Visa transfers
- STACK
- Python, C/C++, NumPy, Redis
- INDUSTRY
- Real Estate, AI, Robotics, Autonomous Vehicles, Transportation, B2B, Ecommerce
The company
Large, well-funded, engineering-led company applying robotics and automation to physical industries, with live divisions in food-service infrastructure and autonomous heavy-vehicle operations.
- STAGE
- public-scale
- FUNDING
- public-scale
- TEAM
- 1,000+ people
JD — the work
About the role
You would build the high-level autonomy that directs fleets of driverless haul trucks around mine sites: routing each vehicle, keeping many vehicles out of each other's way, and dispatching work, for systems already in production in several countries. As an early member of the planning group, your scope could run from single-truck motion up to site-wide fleet decisions. The business is well funded and scaling fast. The role is on-site in San Francisco, with occasional late hours during field deployments.
What you'll do
- Generate paths and trajectories that let trucks steer around unmapped obstacles without needless stops
- Sequence trucks through intersections to minimize how long vehicles wait across the whole site
- Convert the state of the site, such as where loading and dumping happen, into live per-truck assignments
- Coordinate many vehicles at once so heavy trucks never deadlock or collide and throughput stays high
- Test your algorithms in a simulator first and then on real trucks, together with the controls and systems team
What they're looking for
- 2 to 4 years of hands-on motion or path planning for robots, working in Python and C++
- Trajectory or path planning experience on real robots or self-driving vehicles
- Solid command of motion planning methods, including trajectory optimization
- Fluent numerical programming with Python and NumPy
- A bachelor's, master's, or doctorate in robotics, CS, or a closely related area
- Comfort with ambiguity, willingness to create process from nothing, and flexible hours around deployments
Nice to have
- An advanced degree (MS or PhD) focused on robotics or a nearby field
- Planning for whole fleets or for many agents at once
- Hands-on work with nonholonomic planning algorithms
- Computational geometry, such as collision checks using Shapely or GEOS