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.

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

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

Computer Vision Engineer

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Bright AI

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