Asks for

The ad says“Modern Infrastructure: Practical experience with Docker containerisation, version control (Git), and cloud platform execution (AWS).”
The ad says“Hybrid Language Skills: High proficiency in Python for prototyping, training, scripting, and deployment pipelines, combined with a practical, supporting capability to read, build, and debug existing C++ codebases (including exposure to build management tools like CMake).”
The ad says“Core Algorithmic Background: Foundational knowledge of classical computer vision (multi-view geometry, object tracking, spatial transformation) and modern deep learning architectures (object detection, semantic/instance segmentation, transformer-based vision models).”
The ad says“Hybrid Language Skills: High proficiency in Python for prototyping, training, scripting, and deployment pipelines, combined with a practical, supporting capability to read, build, and debug existing C++ codebases (including exposure to build management tools like CMake).”
The ad says“Core Algorithmic Background: Foundational knowledge of classical computer vision (multi-view geometry, object tracking, spatial transformation) and modern deep learning architectures (object detection, semantic/instance segmentation, transformer-based vision models).”
The ad says“Modern Infrastructure: Practical experience with Docker containerisation, version control (Git), and cloud platform execution (AWS).”
The ad says“Modern Infrastructure: Practical experience with Docker containerisation, version control (Git), and cloud platform execution (AWS).”
The ad says“Core Algorithmic Background: Foundational knowledge of classical computer vision (multi-view geometry, object tracking, spatial transformation) and modern deep learning architectures (object detection, semantic/instance segmentation, transformer-based vision models).”
The ad says“Core Algorithmic Background: Foundational knowledge of classical computer vision (multi-view geometry, object tracking, spatial transformation) and modern deep learning architectures (object detection, semantic/instance segmentation, transformer-based vision models).”
The ad says“Core Algorithmic Background: Foundational knowledge of classical computer vision (multi-view geometry, object tracking, spatial transformation) and modern deep learning architectures (object detection, semantic/instance segmentation, transformer-based vision models).”
The ad says“Execution Graph Optimisation: Familiarity with optimising model runtimes and inference execution graphs for real-time applications using TensorRT or ONNX Runtime (e.g., quantisation, layer fusion).”
The ad says“Hybrid Language Skills: High proficiency in Python for prototyping, training, scripting, and deployment pipelines, combined with a practical, supporting capability to read, build, and debug existing C++ codebases (including exposure to build management tools like CMake).”
The ad says“Production Model Training: Experience sourcing, structuring, training, and benchmarking deep neural networks using PyTorch or TensorFlow.”
The ad says“Core Algorithmic Background: Foundational knowledge of classical computer vision (multi-view geometry, object tracking, spatial transformation) and modern deep learning architectures (object detection, semantic/instance segmentation, transformer-based vision models).”
The ad says“Core Algorithmic Background: Foundational knowledge of classical computer vision (multi-view geometry, object tracking, spatial transformation) and modern deep learning architectures (object detection, semantic/instance segmentation, transformer-based vision models).”
The ad says“Production Model Training: Experience sourcing, structuring, training, and benchmarking deep neural networks using PyTorch or TensorFlow.”
The ad says“Execution Graph Optimisation: Familiarity with optimising model runtimes and inference execution graphs for real-time applications using TensorRT or ONNX Runtime (e.g., quantisation, layer fusion).”
The ad says“Core Algorithmic Background: Foundational knowledge of classical computer vision (multi-view geometry, object tracking, spatial transformation) and modern deep learning architectures (object detection, semantic/instance segmentation, transformer-based vision models).”

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.
Modern Cloud & Containerised Deployment: Focus on architecting and containerising Python-based cloud microservices (via Docker) as our primary, future-facing deployment model.
Desktop Applications Support: Assist in compiling cross-platform native binaries or shared libraries linking against the ONNX Runtime C++ API to support and maintain our existing Windows/macOS desktop application footprint.
Automated Data Curation: Help build intelligent, automated data-ingestion pipelines that utilise model-assisted pre-labeling to continuously clean and version high-throughput training datasets.
Interface & Boundary Design: Participate in defining clean API boundaries and interface contracts to ensure our core data science modules integrate seamlessly into downstream vertical applications.

WHAT YOU’LL NEED

Core Algorithmic Background: Foundational knowledge of classical computer vision (multi-view geometry, object tracking, spatial transformation) and modern deep learning architectures (object detection, semantic/instance segmentation, transformer-based vision models).
Production Model Training: Experience sourcing, structuring, training, and benchmarking deep neural networks using PyTorch or TensorFlow.
Hybrid Language Skills: High proficiency in Python for prototyping, training, scripting, and deployment pipelines, combined with a practical, supporting capability to read, build, and debug existing C++ codebases (including exposure to build management tools like CMake).
Execution Graph Optimisation: Familiarity with optimising model runtimes and inference execution graphs for real-time applications using TensorRT or ONNX Runtime (e.g., quantisation, layer fusion).
Modern Infrastructure: Practical experience with Docker containerisation, version control (Git), and cloud platform execution (AWS).

NICE TO HAVE

Neural Architecture Customisation: Experience modifying, adapting, or designing custom neural network components (e.g., specialised backbones, attention mechanisms, or custom loss functions) rather than just implementing standard off-the-shelf models.
Advanced Mathematical Foundations: A strong intuitive grasp or academic background in applied linear algebra and matrix calculus, particularly as it relates to 3D spatial transformations and projective geometry.
Downstream Integration: Experience or familiarity with native application development tools (Visual Studio, Qt Creator) to help ease collaboration when handing off components to vertical app teams.
Sports Video Benchmarks: Experience experimenting with or competing in open-source sports analytics datasets and challenges (e.g., SoccerNet, SportsMOT, or similar multi-object tracking and action-spotting benchmarks).
Domain Alignment: A genuine interest in sports analytics, tracking technology, or elite human performance.

WHAT YOUR SUCCESS WILL LOOK LIKE

In 6 Months' Time…

Global Impact: Your work will be actively contributing to features underpinning informed decisions made by elite coaches and professional athletes globally.
Collaborative Innovation: You will have partnered with the team to take an algorithmic feature from an abstract brief to a stable, deployable Python asset - actively bringing your own unique background, skills, and fresh ideas to the model selection and training process.
Proactive Integration: You will feel completely up to speed with our workflows and comfortable actively identifying potential pipeline improvements, while seamlessly reviewing our existing cross-platform desktop deployment workflows to help support the team's legacy footprint.

In 12 Months' Time...

Pipeline Ownership: You will reliably manage the full lifecycle of core computer vision and data science pipelines, comfortably introducing model updates to production via modern Python cloud microservices.
Data-Centric Automation: You will have collaborated on the design and deployment of an automated dataset curation pipeline, radically accelerating our internal model training and data-cleaning cycles.

WHY CATAPULT?

We have amazing people. We promise you’ll work with some of the most ambitious, intelligent people in an exciting industry, and do some of the best work of your life.
We encourage our people to engage in constructive, open, and honest communication to make Catapult extraordinary.
We work in a collaborative yet challenging environment to consistently improve our performance, which in turn impacts our customers' performance.
Our workforce spans more than 20 countries. You'll have the opportunity to work with people from multiple nationalities and cultures, and to build your global awareness.
We value improvement and development. We are challenging ourselves to continuously grow and become a high-performance company. That means we maintain a growth mindset in everything we do, and our people are always looking for ways to improve. There is an unlimited opportunity to grow, do more, and do better.

Whether you’re interested in sports or not, you’ll have the satisfaction of knowing your work is supporting some of the most successful teams and athletes on the planet!

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! We would love to have a chat and see if you could be a great addition to our team. We are building the future of sports performance. Our priority is to find the brightest talent who can add to our team culture, actively contribute, and be excited about what they do.

All offers of employment are subject to Catapult's positive prehire check. To find out more, please contact the Talent Partner for this role.