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.