Computer Vision Engineer - Perception for Autonomy

Computer Vision Engineer — Perception for Autonomy Location: [Palo Alto / hybrid] The role: We fly drones that inspect real infrastructure. That means reconstructing sites accurately enough to detect change over time, and giving the autonomy stack a picture of the world it can actually act on. You'll own perception for a moving platform — reconstruction, pose, and the simulated environments we use to train and evaluate flight behavior. You'll work closely with the autonomy side without owning the flight controller. What you'll work on: • Reconstruction — Gaussian splatting and photogrammetric pipelines producing metrically accurate, georeferenced scenes from drone imagery • Pose and state estimation — bundle adjustment, RTK/GNSS and IMU fusion, visual-inertial odometry, multi-camera calibration • Simulation for autonomy — turning reconstructions into training and evaluation environments for flight policies, and characterizing where sim diverges from reality • Change detection across reconstructions separated by weeks or months • Perception in the loop — defining what reconstruction and detection deliver to planning, and what happens when the estimate degrades • Detection and auto-labeling models running on the aircraft under real latency and power budgets What we need: • 2+ years in computer vision or robotics perception, with systems that ran outside a lab • Solid multi-view geometry — you can reason about what your estimator is doing and debug a bundle adjustment that won't converge • Hands-on SLAM, SfM, or visual-inertial odometry • Strong PyTorch; real experience training and debugging models on field data that doesn't look like the benchmark • Have worked on a moving platform — drone, vehicle, or robot — where ground truth is expensive and failures happen on site • Comfortable at the hardware boundary: camera sync, calibration rigs, reading flight logs • Enough robotics literacy to talk to the autonomy team — you know what a planner needs from perception and why latency and failure modes matter to it • Writes clearly enough that another team can act on your design doc Strong signals: • 3DGS or NeRF, especially large outdoor scenes • Reconstruction-backed simulation for robot training • Sim-to-real transfer or learned dynamics • ROS/ROS2, PX4/ArduPilot exposure • C++ alongside Python • Thermal, depth, or lidar fusion How we work: Small team, high autonomy, short path from prototype to field trial. Direct access to real aircraft and real customer sites. We hire people who go find the failure themselves.