Head of Autonomy

Teleo, a Havoc company, is a robotics company that transforms construction heavy equipment, including loaders, dozers, excavators, and trucks, into autonomous robots for commercial and defense applications. Our technology enables a single operator to supervise and control multiple machines simultaneously, delivering significant productivity gains while improving operator safety and comfort. Teleo was founded by a team of experienced technology leaders who previously led the development of Lyft's Self-Driving Car program and Google Street View. Teleo recently announced its merger with Havoc AI, a fast-growing defense technology company developing coordinated fleets of autonomous maritime vessels. This is a unique opportunity to join a team building technology with real-world impact. You will work on cutting-edge 100,000-pound autonomous robots and engineer complex systems at the intersection of hardware, software, robotics, and AI.

The Role 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.

Core Responsibilities Technical 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 Team 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

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

Teleo is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. All qualified people are encouraged to apply.