Computer Vision Engineer

Auterion•Manassas, VA

About The Position

Auterion is seeking a Computer Vision Engineer to build and deploy perception systems that run in real time onboard autonomous aerial platforms. The role focuses on practical, field-tested systems rather than offline research, spanning classical computer vision, camera integration and validation, calibration, and optimization for constrained hardware. The engineer will take algorithms and cameras from bench testing to flight, analyze captured data and logs, and improve system performance through short feedback loops. The platforms support commercial and government applications including inspection, mapping, search and rescue, and security.

Requirements

Degree in Computer Science, Electrical Engineering, Robotics, or a related field, or equivalent practical experience.
Experience building computer vision systems, including exposure to resource-constrained or embedded platforms.
Working knowledge of classical computer vision, including feature detection and matching, optical flow, camera models and calibration, and multiple-view geometry.
Practical experience with cameras as hardware, including sensor and lens behavior, exposure, timing, and camera-module integration issues.
Proficiency in C++ and Python, with practical experience using OpenCV.
Evidence of taking a project from problem definition to deployment on hardware, and comfort working in the field alongside pilots and analyzing real logs.

Nice To Haves

Camera bring-up below the level of a vendor SDK, including MIPI CSI-2, USB, or GigE, and driver- or V4L2-level work.
Experience with ISP tuning, auto-exposure, or auto-gain pipelines.
Experience evaluating or retraining detection models on real captured data.
Experience with state estimation and sensor fusion, visual-inertial odometry, or visual SLAM.
Experience with ROS2, embedded inference runtimes, or aerial platforms such as PX4 or MAVLink-based systems.

Responsibilities

Implement and optimize onboard perception algorithms for real-time detection, temporal association, and state estimation of dynamic objects from a moving platform.
Integrate camera modules onto platforms and validate image quality, timing, latency, and behavior under actual flight conditions.
Build repeatable test and evaluation procedures to measure and compare camera and algorithm performance across units, builds, and iterations.
Run and improve camera calibration and sensor synchronization procedures, including multi-camera and multi-sensor setups.
Profile and optimize embedded inference pipelines against latency, throughput, and compute budgets; evaluate or retrain detection models using real captured data.
Support field test campaigns by taking algorithms and cameras from bench to flight, working on site with pilots and integrators, and improving systems based on captured data.
Own deliverables end to end, contribute to camera, lens, and sensor selection, and keep the team informed about progress and risks.