Computer Vision Engineer

Catapult is building the future of sports performance technology, with a mission to Unleash the Potential of every athlete and team on earth. We don't just work in the sporting industry; we are actively changing it.  Since 2006, our solutions have been leading the way in sports performance software, science, and data, in a world where 1% can literally mean the difference between winning and losing.

We work with over 5,000+ teams around the world, empowering coaches, managers and trainers in premier teams in the NFL, NBA, NHL, MLS, EPL, AFL, NRL, NCAA and more. We provide the information they need to optimise athletes’ health, game-day readiness, and performance, as well as in-game tactics.  

Catapult is a sports technology company that empowers professional teams to make data-driven decisions. We deliver health, performance, video, and AI insights from the locker room to competitive environments, ensuring every decision is an opportunity to gain an advantage, sharpen performance, and build lasting success. 

WE WANT PEOPLE WHO ARE PASSIONATE ABOUT DELIVERING INNOVATIVE SOLUTIONS TO COMPLEX PROBLEMS

We are looking for an enthusiastic, inquisitive, full-lifecycle Computer Vision Engineer to join our centralised, multi-disciplinary Data Science team.

Our mandate is to drive platform innovation and cross-vertical reusability. Based in our London office, this role is designed for a unique technical practitioner who enjoys owning the complete lifecycle of a feature. Working collaboratively with various stakeholders, you won’t be isolated to just model training or just infrastructure configuration; you will help translate product briefs into algorithmic solutions, train deep learning models, engineer algorithmic pipelines, and deliver optimised, production-ready deployable artifacts that power analytics used by professional sports teams and elite athletes around the world.

WHAT YOU’LL DO 

• End-to-End Pipeline Contribution: Collaborate with senior data scientists, computer vision engineers and vertical teams to translate product requirements into practical computer vision solutions, helping design the pipeline from raw video ingestion to production inference.
• Algorithm & Model Development: Design, train, and evaluate deep learning architectures alongside classical computer vision pipelines (e.g., feature tracking, optical flow, and spatial filtering via OpenCV).
• Geometric Computer Vision: Develop robust mathematical pipelines for camera calibration, homography estimation, and coordinate mapping to ensure model spatial outputs are accurate and stable.