Principal Machine Learning Engineer (Live Sports Insights)

Responsibilities

• Lead the end-to-end development of AI solutions using Computer Vision, Machine Learning, Generative AI, and data science to enable capabilities such as automated sports metadata generation and detection of key events in live content and data streams.
• Generate actionable insights for player performance, contextual statistics, and injury risk by designing models with embedded responsible and ethical AI principles from design through deployment.
• Integrate model driven insights into personalisation engines, tailoring recommendations based on favourite teams, players, match context, and other signals while ensuring transparency, fairness, and appropriate use of data.
• Define advanced experimental designs, lead A/B testing, develop and maintain metrics and dashboards, establish robust MLOps practices, and own end-to-end productionisation from data ingestion through deployment and ongoing model monitoring.
• Design, architect, and operate low latency, highly reliable cloud based AI systems for live sports scenarios, ensuring resilient performance during peak traffic, responsible model behaviour in real time, and an optimal balance between cost, latency, and production scale performance.

Requirements

• Proven extensive lead level engineering experience delivering data-driven ML systems, with clear ownership of technical direction, mentoring, and delivery.
• Working knowledge of modern ML techniques, including Generative AI, and how emergent models can extract insights from multimodal sports data (e.g., numerical, spatial, video, or metadata).
• Advanced Python expertise with strong hands-on use of ML/DL frameworks (e.g., PyTorch , TensorFlow), including taking models from experimentation into production model serving.
• End-to-end MLOps experience, including CI/CD for ML, experiment tracking, model registries, drift detection, automated retraining, and infrastructure as code practices.
• U nderstanding of sports data, including hands-on experience working with event data, tracking data, or other high-volume sports datasets, and converting these into actionable analytical or predictive insights.
• Being a Sports Fan - we immerse ourselves in Sport so having a passion for sport an d a desire to push the sports experience to the next level is a real bonus.
• The rewards
• There's one thing people can't stop talking about when it comes to #LifeAtSky : the perks. Here's a taster:
• Sky Q, for the TV you love all in one place
• The magic of Sky Glass at an exclusive rate
• A generous pension package
• Private healthcare
• Discounted mobile and broadband
• A wide range of Sky VIP rewards and experiences
• Inclusion & how you'll work
• We've embraced hybrid working and split our time between unique office spaces and the convenience of working from home. You'll find out more about what hybrid working looks like for your role later on in the recruitment process.
• Your office space
• Osterley
• Our Osterley Campus is a 10-minute walk from Syon Lane train station. Or you can hop on one of our

Benefits

Additional Information

We believe in better. And we make it happen. Better content. Better products. And better careers. Working in Tech, Product or Data at Sky is about building the next and the new. From broadband to broadcast, streaming to mobile, SkyQ to Sky Glass, we never stand still. We optimise and innovate. We turn big ideas into the products, content and services millions of people love. And we do it all right here at Sky. Join us to rethink how sports are experienced. Our AI-driven platform powers immersive, personalised live sports-giving fans control, fresh perspectives, and predictive insights during the action. As a Principal Machine Learning Engineer , you'll shape the technical strategy and delivery of production ML systems that transform raw sports data and live video into real-time insights and personalised experiences for millions of fans. For this role we offer the hybrid working approach with 2 days a week onsite in Osterley office .

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