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 are actively changing the sporting industry. Since 2006, our solutions have been leading the way in sports performance software, science, and data, 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 across various leagues. We provide the information they need to optimise athletes’ health, game-day readiness, and performance, as well as in-game tactics.

We are looking for an enthusiastic, inquisitive, full-lifecycle Computer Vision Engineer to join our centralised, multi-disciplinary Data Science team. This role is designed for a unique technical practitioner who enjoys owning the complete lifecycle of a feature. Working collaboratively with various stakeholders, you will help translate product briefs into algorithmic solutions, train deep learning models, engineer algorithmic pipelines, and deliver optimised, production-ready deployable artefacts 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.
• Algorithm & Model Development: Design, train, and evaluate deep learning architectures alongside classical computer vision pipelines.
• Geometric Computer Vision: Develop robust mathematical pipelines for camera calibration, homography estimation, and coordinate mapping.
• Modern Cloud & Containerised Deployment: Focus on architecting and containerising Python-based cloud microservices.
• Desktop Applications Support: Assist in compiling cross-platform native binaries or shared libraries.
• Automated Data Curation: Help build intelligent, automated data-ingestion pipelines.
• Interface & Boundary Design: Participate in defining clean API boundaries and interface contracts.

What You’ll Need

• Core Algorithmic Background: Foundational knowledge of classical computer vision and modern deep learning architectures.
• Production Model Training: Experience sourcing, structuring, training, and benchmarking deep neural networks.
• Hybrid Language Skills: High proficiency in Python and practical capability to read, build, and debug existing C++ codebases.
• Execution Graph Optimisation: Familiarity with optimising model runtimes and inference execution graphs.
• Modern Infrastructure: Practical experience with Docker containerisation, version control, and cloud platform execution.

NICE TO HAVE

• Neural Architecture Customisation: Experience modifying or designing custom neural network components.
• Advanced Mathematical Foundations: A strong intuitive grasp or academic background in applied linear algebra and matrix calculus.
• Downstream Integration: Experience with native application development tools.
• Sports Video Benchmarks: Experience experimenting with open-source sports analytics datasets.
• Domain Alignment: A genuine interest in sports analytics, tracking technology, or elite human performance.

What Your Success Will Look Like

• In 6 Months' Time: Your work will be contributing to features underpinning informed decisions made by elite coaches and professional athletes globally.
• In 12 Months' Time: You will reliably manage the full lifecycle of core computer vision and data science pipelines.

WHY CATAPULT?

• We have amazing people and promise you’ll work with some of the most ambitious, intelligent people in an exciting industry.
• We encourage constructive, open, and honest communication.
• We work in a collaborative yet challenging environment to consistently improve our performance.
• Our workforce spans more than 20 countries, providing opportunities to work with diverse cultures.
• We value improvement and development, maintaining a growth mindset in everything we do.

Research shows that while men apply for jobs when they meet an average of 60% of the criteria, women and other marginalised groups tend only to apply when they check every box. So if you have what it takes, but don't meet every single point in our job ad, please still get in touch!