- 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.
- 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).
