Computer Vision Engineer - Perception for Autonomy
LocationCalifornia, Palo Alto
Work modehybrid
Typefull-time
DepartmentEngineering
Company size1+ people
First seen1w ago
Last seen4d ago
Computer Vision Engineer — Perception for Autonomy
Location: [Palo Alto / hybrid]
The role:
About the company
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:
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Reconstruction — Gaussian splatting and photogrammetric pipelines producing metrically accurate, georeferenced scenes from drone imagery
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Pose and state estimation — bundle adjustment, RTK/GNSS and IMU fusion, visual-inertial odometry, multi-camera calibration
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Simulation for autonomy — turning reconstructions into training and evaluation environments for flight policies, and characterizing where sim diverges from reality
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Change detection across reconstructions separated by weeks or months
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Perception in the loop — defining what reconstruction and detection deliver to planning, and what happens when the estimate degrades
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Detection and auto-labeling models running on the aircraft under real latency and power budgets
What we need:
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2+ years in computer vision or robotics perception, with systems that ran outside a lab
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Solid multi-view geometry — you can reason about what your estimator is doing and debug a bundle adjustment that won’t converge
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Hands-on SLAM, SfM, or visual-inertial odometry
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Strong PyTorch; real experience training and debugging models on field data that doesn’t look like the benchmark
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Have worked on a moving platform — drone, vehicle, or robot — where ground truth is expensive and failures happen on site
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Comfortable at the hardware boundary: camera sync, calibration rigs, reading flight logs
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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
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Writes clearly enough that another team can act on your design doc
Strong signals:
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3DGS or NeRF, especially large outdoor scenes
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Reconstruction-backed simulation for robot training
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Sim-to-real transfer or learned dynamics
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ROS/ROS2, PX4/ArduPilot exposure
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C++ alongside Python
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Thermal, depth, or lidar fusion
How we work:
Benefits
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.