Computer Vision Engineer (Sports Analytics)

Computer Vision Engineer (Sports Analytics) 🎾🏈🏏🏂🏑⚽

Location: Remote (UK-based) — occasional travel for key workshops

🌟 Candidates must have proven sports analytics experience (match analysis, player metrics, tactical insights) as well as strong computer vision and deep learning skills for this one please

We are looking for a talented Computer Vision Engineer to join an elite sports technology company focused on performance and fan engagement. The team builds products that turn real-world sports data into actionable insights for athletes, coaches and fans.

In this role, you will design, build and deploy computer vision systems that extract reliable information from video and sensor streams to create production-grade sports analytics features.

This includes model development, evaluation, optimization for real-time or near-real-time performance and robust deployment into live products.

Key Responsibilities:

• Build CV/ML pipelines for sports analytics tasks such as detection, segmentation, pose estimation, tracking, action recognition, ball/player tracking, or 3D reconstruction
• Develop and maintain data pipelines: collection, labelling strategies, quality checks, dataset versioning, and experiment tracking
• Train, tune, and evaluate models with strong statistical rigour and clear metrics (accuracy, latency, stability, drift)
• Optimise models for deployment (quantization, pruning, TensorRT/ONNX, batching/streaming, GPU utilisation)
• Collaborate with product, design, and engineering teams to integrate models into user-facing features and services
• Monitor models in production: performance dashboards, alerts, retraining triggers, and incident response
• Contribute to technical direction, including architectural choices, tooling, standards, and best practices
• Produce clear technical documentation and communicate trade-offs to non-specialists

Essential Skills & Experience:

• Demonstrable sports analytics experience (professional, academic, personal projects, or hobbyist) such as match analysis, player tracking, event tagging, tactical analysis, or building tools using sports data/video
• Strong practical experience in computer vision and deep learning, with evidence of shipped systems or robust prototypes
• Excellent Python skills, plus solid software engineering fundamentals (testing, CI/CD, code review)
• Experience with PyTorch (preferred) or TensorFlow; familiarity with OpenCV and modern CV tooling
• Strong understanding of CV fundamentals (geometry, camera models, multi-view, filtering) relevant to the role
• Experience deploying ML to production (APIs/services, edge or cloud inference, containerisation)
• Comfortable working with GPUs and performance profiling/optimization

Nice to Have:

• Football or basketball-specific analytics experience or other elite-sport performance analysis
• Experience with pose models (2D/3D), IMU fusion or biomechanics-related estimation problems
• C++ for performance-critical components
• Experience with AWS/Azure/GCP and MLOps tooling (MLflow/W&B, feature stores, model registries)
• Knowledge of real-time systems, streaming pipelines and low-latency inference

If you are passionate about sports, computer vision, and building products that make an impact, we want to hear from you!

Please note, this role unfortunately does not offer sponsorship.