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# Computer Vision Engineer

- **Company:** Catapult Sports
- **Location:** UK
- **Contract:** Permanent contract
- **Skills:** Microsoft Windows, Application Programming Interfaces (APIs), Amazon Web Services, Software Applications, Artificial Neural Networks, Computer Vision, C++ (Programming Language), Mobile Application Development, Cloud Engineering, CMake, Software Debugging, Integrated Development Environments, Python (Programming Language), Microsoft Visual Studio, Object Detection, OpenCV, Open Source Technology, Tensorflow, Scripting, Cloud Platform System, Real Time Systems, Pytorch, Delivery Pipeline, Deep Learning, Git, Build Management, Containerization, Core Data, Attention Mechanisms, ONNX (Open Neural Network Exchange) Format, TensorRT, GPT, Software Version Control, Docker, Microservices
- **Published:** October 15, 2026
- **Apply:** https://www.totaljobs.com/job/computer-vision-engineer/catapult-sports-job108169617

## About the Role

* 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)., * 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.

## Description

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., * 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., * 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)., * 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.