Computer Vision Engineer

Location: Palo Alto, California

Department: Engineering

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.