Computer Vision Engineer

BrightAI; Palo Alto (CA); Hybrid; USD 120,000 - 160,000; Full time

Job summary

BrightAI is seeking a Computer Vision Engineer for Perception in Autonomy in Palo Alto. You will own perception for a moving drone platform, focusing on reconstruction, pose estimation, and simulation environments used to train and evaluate flight policies.

We value hands-on SLAM/VO with PyTorch, C++ and Python skills, and experience deploying perception systems on real hardware with on-site calibration and data collection.

Qualifications

• 2+ years of experience in computer vision or robotics perception in real-world environments.
• Strong understanding of multi-view geometry with ability to debug bundle adjustment.
• Hands-on SLAM, SfM, or visual-inertial odometry.
• Proficiency in PyTorch with field data experiments.
• Experience on moving platforms (drone/robot) where ground truth is costly.
• Comfort with hardware interfaces: camera sync, calibration rigs, flight logs.
• Ability to clearly document design decisions for other teams.

Responsibilities

• Own reconstruction and perception components for a moving platform, including poses and training environments.
• Develop and evaluate Gaussian splatting, photogrammetric pipelines, and georeferenced scenes from imagery.
• Work on pose estimation, RTK/GNSS/IMU fusion, and visual-inertial odometry.
• Create simulation environments for training and evaluating flight policies.
• Address change detection across reconstructions over time and define perception signals for planning.

Skills

Multiview geometry

PyTorch

SLAM / VIO

C++

Python

Robotics perception

Hardware awareness

Tools

Python

C++

ROS

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
• 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.