Machine Learning Engineer

Location - London

Hybrid Working – 3 days per week in London

About

We have partnered with a ground-breaking, award-winning technology company building the future of sports talent identification and development. Our partner builds AI-driven tools that generate and analyse sports data, helping clubs, national federations and players unlock real-time analysis and valuable insights. Their technology is already in production and used at the very top of the game, including at global sporting events, and they are now looking for a talented AI Engineer to join a small, fast-moving team and help build the technology of tomorrow.

The Role

• Design, develop, test & deploy production-ready algorithms for sports performance analysis.
• Collaborate closely with sport scientists to translate motion and biomechanics into actionable AI/ML models.
• Set up and maintain CI/CD pipelines for ML model delivery, ensuring reliable, repeatable deployments
• Monitor deployed models for performance degradation, data drift, and system health; implement automated alerting and retraining triggers.
• Ensure high accuracy, robustness, and real-time performance of deployed models.
• Build and optimise inference pipelines that support large-scale video processing across edge and cloud platforms.
• Own model deployment infrastructure using AWS services (ECR, Lambda, S3 or equivalent) and infrastructure-as-code tooling

What You'll Need

• Proven experience in algorithm development, including image and video processing in production.
• Experience in developing and deploying ML models in real-world, high-stakes production environments
• Strong algorithm design skills, comfortable working with time-series data, optimisation problems and performance evaluation techniques
• Solid DevOps experience, CI/CD, containerisation (e.g. Docker) and cloud-based model deployment (MLOps).
• Strong Python skills with data processing and analysis libraries (e.g. Pandas, OpenCV).
• Hands-on experience with deep learning frameworks (e.g. PyTorch, TensorFlow).
• Strong grasp of software engineering fundamentals: version control, testing, code review, and documentation
• MSc or PhD in a related field.
• Experience working in a start-up or fast-paced, high-autonomy environment.