Head of Autonomy

Teleo; Iowa; On-site; USD 180,000 - 280,000; Full time

Job summary

Teleo seeks a senior Head of Autonomy to own the end-to-end autonomy stack and lead the engineering team across perception, simulation, planning, controls, and fleet orchestration. You’ll set technical direction, review designs, and ensure safety in customer deployments on multiple machine types.

You will hire, grow, and run the team, partner with hardware and field teams, and drive learnings from real deployments back into the stack to improve performance and reliability.

Qualifications

• 10+ years building autonomy or robotics software
• 5+ years managing engineering teams
• Hands-on depth in at least two areas of the stack
• Strong C++ and Python, code review at senior level
• U.S. person due to export control requirements
• Shipped autonomy on physical robots or vehicles

Responsibilities

• Own the autonomy stack end-to-end and set technical direction
• Lead and hire the engineering team across perception, simulation, planning, controls, and fleet orchestration
• Review designs and stay close to code and field data
• Coordinate safety cases and release gates with safety/compliance
• Collaborate with hardware and vehicle integration and field deployment teams
• Represent autonomy to executive leadership, customers, and partners

Skills

Leadership

Robotics

Autonomy

C++

Python

MLOps

Education

MS/PhD in Robotics, CS, EE or ME

Tools

NVIDIA Jetson-class

Job description

• The 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
• 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
• 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
• You 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

Track record of hiring and developing strong engineers Preferred Qualifications Experience deploying autonomy and ML models on embedded compute ("NVIDIA Jetson-class or similar") Took an autonomy product from prototype to multi-site commercial deployment Bonus Points Behavior cloning from human operator data and sim-to-real transfer for contact-rich tasks 10+ years building autonomy or robotics software, with at least 5 years managing engineering teams, including managers or technical leads 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 Multi-robot coordination or fleet management systems Functional safety (ISO 13849, IEC 61508, ISO 25119 or similar) and safety cases for autonomous systems Must be a U.S. person (U.S. citizen or lawful permanent resident) due to export control requirements Hands-on depth in at least two of: perception, planning, controls, simulation, or learning-based control, and working fluency across the full stack Strong C++ and Python; able to review code and designs at a senior engineer’s level Built or run data and evaluation infrastructure that tied model changes to field performance Comfortable working on-site in Palo Alto and spending time in the field on job sites and test areas Shipped autonomy that runs on physical robots or vehicles in real operating conditions, not only in simulation M.S. or Ph.D. in Robotics, Computer Science, Electrical or Mechanical Engineering, or a related field, or equivalent experience Scaled a team through a period of fast growth Autonomy for off-road, construction, mining, agriculture, or defense ground vehicles Led autonomy work on defense programs Hydraulic machines or articulated manipulators, including system identification and control of hydraulic actuators Supervised autonomy or teleoperation systems, and designing for human intervention