Senior Computer Vision Engineer

About the role

Overview

We are looking for a Senior Computer Vision Engineer to build and deploy perception systems that run onboard autonomous aerial platforms. This is not offline research. Our algorithms run in real time on embedded computers, in the air, in conditions we do not control.

You will work across the full stack of the problem: classical computer vision and deep learning, camera and sensor selection, calibration, model training and evaluation, and optimization for constrained hardware. You will take your work to the field, fly it, look at the logs, and improve it. Our platforms serve both commercial and government customers across applications including inspection, mapping, search and rescue and security.

If you like hard real world problems and short feedback loops between an idea and a flight test, we want to talk with you.

Responsibilities and Duties

- Design, implement and optimize onboard perception algorithms for real time detection, temporal association and state estimation of dynamic objects from a moving platform.

- Train, retrain and evaluate neural networks for detection and classification, including dataset curation, labelling strategy and evaluation methodology.

- Optimize models and pipelines for embedded inference, working to explicit latency, throughput and compute budgets.

- Develop and maintain camera calibration and sensor synchronization procedures, including multi camera and multi sensor setups.

- Combine visual measurements with inertial and platform state data using filtering and estimation techniques for robust performance under egomotion.

- Plan and run field test campaigns. Take algorithms from bench to flight, work with pilots and integrators on site, and close the loop on real captured data.

- Build repeatable evaluation methods so that performance and accuracy are measurable across every iteration rather than judged by eye.

- Provide input into camera, lens, sensor and compute selection, and evaluate hardware options against real performance data.

- Take full ownership of computer vision deliverables. Disambiguate requirements, drive alignment across teams, keep stakeholders informed and see work through to the customer.

- Mentor and coach other engineers, and raise the bar on product quality.

Qualifications and Skills

- Degree in Computer Science, Electrical Engineering, Robotics or a related field, or equivalent practical experience.

- Strong hands on experience deploying computer vision systems on resource constrained platforms, ideally robotic or aerial.

- Solid grounding in classical computer vision: feature detection and matching, optical flow, camera models and calibration, multiple view geometry.

- Practical experience training and deploying convolutional neural networks for detection or classification, including the unglamorous parts: data collection, labelling quality, retraining loops and honest evaluation.