The RoleThe Head of Autonomy owns Teleo's autonomy stack end to end and leads the team that builds it. You will lead the engineering team across perception, simulation, controls, path planning, fleet orchestration, learning-based methods (RL, imitation learning, and VLA-style models), and MLOps.
This is a senior player-coach role. You will set the technical direction, make the architecture calls, and stay close enough to the code and the field data to review designs in depth. You will also hire, grow, and run the team, and be accountable for autonomy performance on customer deployments across several machine types.
The core technical problem is taking a stack that works today in supervised autonomy on real job sites and scaling it: more machine types, more sites, more material-manipulation tasks, and fewer operator interventions per hour, without compromising safety.
Core ResponsibilitiesTechnical leadership- Own the autonomy architecture from sensors to actuation: perception, localization, world modeling, planning, control, and the interfaces between them
- Set the roadmap for moving from hand-engineered components to learned ones (imitation learning, RL, VLA-style policies), and decide where classical methods should stay
- Drive a composable, skill-based approach to material manipulation (loading, pushing, scooping, dumping, digging) that transfers across machine types
- Build the simulation and data engine: operator data capture, sim-to-real validation, closed-loop evaluation, and regression testing tied to field metrics
- Scale autonomy to multi-machine fleets working alongside remote operators, including task allocation and coordination on shared sites
- Shorten the time to bring autonomy up on a new vehicle model, working closely with hardware and vehicle integration
- Define safety cases and release gates for autonomy software, in partnership with safety and compliance
- Work with the hardware and software teams to make compute, sensor, and onboard performance trade-offs for the Teleo product and fleet
People leadership- Manage the autonomy team directly, including technical leads for each area
- Hire and retain strong engineers; grow the team as deployments scale
- Run planning, prioritization, and execution for the autonomy org; set clear goals and hold a high bar on code and review quality
- Coach engineers on technical depth and career growth; build leads where the team needs them
- Represent autonomy to executive leadership, customers, and partners, and translate field needs into engineering priorities
The TeamYou will lead engineers across the following areas. Sizes will shift as priorities change.
- Perception: camera and lidar fusion, off-road segmentation, detection and tracking, localization and mapping, auto-labeling
- Simulation: machine and terrain simulation, material interaction, scenario generation, sim-to-real validation
- Controls: system identification, MPC, learned and hybrid controllers across tracked and wheeled platforms
- Path planning: motion and task planning for dozers, loaders, excavators, and skid steers on unstructured sites
- Fleet orchestration: multi-machine coordination, task allocation, and the interface to remote operators
- Learning-based autonomy: RL, imitation learning from operator data, and VLA-style models for material manipulation
- MLOps: data pipelines, training infrastructure, model evaluation, and deployment to the fleet
Requirements- M.S. or Ph.D. in Robotics, Computer Science, Electrical or Mechanical Engineering, or a related field, or equivalent experience
- 10+ years building autonomy or robotics software, with at least 5 years managing engineering teams, including managers or technical leads
- Shipped autonomy that runs on physical robots or vehicles in real operating conditions, not only in simulation
- Hands-on depth in at least two of: perception, planning, controls, simulation, or learning-based control, and working fluency across the full stack
- Practical experience with learning-based methods (imitation learning, RL, or large pretrained models) and a clear view of where they beat classical approaches and where they don't
- Built or run data and evaluation infrastructure that tied model changes to field performance
- Strong C++ and Python; able to review code and designs at a senior engineer's level
- Experience deploying autonomy and ML models on embedded compute (NVIDIA Jetson-class or similar)
- Track record of hiring and developing strong engineers
- Comfortable working on-site in Palo Alto and spending time in the field on job sites and test areas
- Must be a U.S. person (U.S. citizen or lawful permanent resident) due to export control requirements
Preferred Qualifications- Autonomy for off-road, construction, mining, agriculture, or defense ground vehicles
- Hydraulic machines or articulated manipulators, including system identification and control of hydraulic actuators
- Behavior cloning from human operator data and sim-to-real transfer for contact-rich tasks
- Multi-robot coordination or fleet management systems
- Supervised autonomy or teleoperation systems, and designing for human intervention
- Functional safety (ISO 13849, IEC 61508, ISO 25119 or similar) and safety cases for autonomous systems
Bonus Points- Took an autonomy product from prototype to multi-site commercial deployment
- Led autonomy work on defense programs
- Scaled a team through a period of fast growth
$250,000 - $300,000 a year
- Took an autonomy product from prototype to multi-site commercial deployment
- Led autonomy work on defense programs
- Scaled a team through a period of fast growth
