Computer Vision Engineer

Job description

**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.