Autonomy Engineer

QAIL AI focuses on building autonomously operated AI systems that function reliably in real-world environments. The team is engaged in deep applied research, training advanced algorithms on real-time data streams. QAIL AI aims to bridge cutting edge artificial intelligence with practical deployment across physical systems and devices. We are building an autonomous ground vehicle for rough, cross-country terrain, and we need a founding engineer to own its autonomy, from the sensors to the path it drives. You will work side by side with the founders, turn a prototype that already drives into a vehicle that crosses real ground on its own, and set how our engineering team builds. What you'll own QAIL AI focuses on building autonomously operated AI systems that function reliably in real-world environments. The team is engaged in deep applied research, training advanced algorithms on real-time data streams. QAIL AI aims to bridge cutting edge artificial intelligence with practical deployment across physical systems and devices. We are building an autonomous ground vehicle for rough, cross-country terrain, and we need a founding engineer to own its autonomy, from the sensors to the path it drives. You will work side by side with the founders, turn a prototype that already drives into a vehicle that crosses real ground on its own, and set how our engineering team builds. What you'll own You own the autonomy stack end to end: how the vehicle knows where it is, what it sees, where it can drive, and how it gets there safely. Localization: fuse RTK GNSS, IMU, visual-inertial, and wheel odometry, and keep working when GNSS is jammed or spoofed. Perception and mapping: turn stereo depth (and lidar, if you make the case) into elevation and traversability maps. Tell rigid obstacles from grass the vehicle can push through, and find negative obstacles: ditches, holes, craters, drop-offs, and water. Planning: routes over terrain cost and mission constraints, local planning for a car-like vehicle with a ~2.5 m turning radius and coarse on/off drive, and recovery when it gets stuck. Obstacle avoidance and safety: stop-before-contact reflexes, e-stop, geofences, and safe behaviour when the radio link drops. Control: hold a path on slippery, uneven ground at low speed, and detect wheel slip. Operator side: teleoperation and 3D views that still work over thin radio links. Testing: a field-test program with logging and replay, simulation where it pays off, and metrics such as distance between interventions. Hardware: choose, mount and power sensors and compute; you will hold a soldering iron about as often as a keyboard. Team: set our engineering practices and help hire the next engineers. What you start with A prototype that already drives: you inherit a working stack, not a blank page. Vehicle: a converted electric off-road platform, 1.33 x 0.90 m, driven by wire through its own radio link, with no mechanical changes. Compute: an NVIDIA Jetson Orin Nano running ROS 2 Humble in Docker. Sensing: a Luxonis OAK-D Pro W stereo camera with a 9-axis IMU; RTK GNSS is next. Autonomy today: waypoint following from a browser console, and an obstacle guard that checks the path 29 times a second, 74 ms behind the camera. Nav2 is configured but not yet driving. Console: a web command centre with missions, live video and a 3D view of what the vehicle sees. Safety: a dead-man timeout, an e-stop, and a veto on forward drive when something is in the way. You have 4+ years building autonomy for real outdoor robots or vehicles, and something you worked on drove itself outdoors or off-road. Depth in at least two of state estimation, perception and mapping, or planning and control, and working knowledge of the third. Off-road know-how: elevation or traversability mapping, negative obstacles, wheel slip, degraded GNSS. Strong C++ and Python, ROS 2 and Linux; at home on edge compute such as Jetson and CUDA. Hands-on hardware skills: sensors, wiring, power budgets, debugging in the field. A bias for field testing, and the judgment to ship a safe prototype in weeks, without a detailed spec. Bonus Nav2 internals (Smac planners, regulated pure pursuit), grid_map or elevation_mapping. Visual-inertial odometry or SLAM (Basalt, OpenVINS, ORB-SLAM3, cuVSLAM), especially without GNSS. Lidar pipelines, or learned (self-supervised) traversability. Defense, agricultural, mining, planetary, or search-and-rescue robots, and time spent with end users in the field. Low-bandwidth teleoperation over mesh radios. What we offer A founding role with meaningful equity, and a salary of [range]. Ownership of the autonomy architecture from day one. Short loops: from an idea to a field test in days, with direct feedback from the people who will use the vehicle. San Francisco, with regular field testing. #J-18808-Ljbffr QAIL AI