Principal Machine Learning Engineer (Live Sports Insights)
14 hours ago
London
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. What you’ll do: You’ll be the technical lead for a critical ML domain (e.g., live sports insights and personalisation, real-time ranking, computer vision for multi-angle video, or streaming inference). Expect to influence roadmaps, architecture, and platform evolution—not just single models—while mentoring engineers and data scientists and raising the bar across teams. 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. What you'll bring 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. Proven technical leadership experience including mentoring and guiding Senior and Mid-Level Data Scientists both in their day-to-day work and career development. Experience of working in a fast-changing environment is vital demonstrating adaptability and ability to support the team through times of uncertainty, pivoting as necessary. Nice to have Understanding 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 and 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 are a Disability Confident Employer, and welcome and encourage applications from all candidates. We will look to ensure a fair and consistent experience for all, and will make reasonable adjustments to support you where appropriate. Please flag any adjustments you need to your recruiter as early as you can. 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 free shuttle buses that run to and from Osterley, Gunnersbury, Ealing Broadway and South Ealing tube stations. There are also plenty of bike shelters and showers. On campus, you’ll find 13 subsidised restaurants, cafes, and a Waitrose. You can keep in shape at our subsidised gym, catch the latest shows and movies at our cinema, get your car washed, and even get pampered at our beauty salon. We'd love to hear from you Inventive, forward-thinking minds come together to work in Tech, Product and Data at Sky. It’s a place where you can explore what if, how far, and what next. But better doesn’t stop at what we do, it’s how we do it, too. We embrace each other’s differences. We support our community and contribute to a sustainable future for our business and the planet. If you believe in better, we’ll back you all the way. Just so you know: if your application is successful, we’ll ask you to complete a criminal record check. And depending on the role you have applied for and the nature of any convictions you may have, we might have to withdraw the offer.
154 Mile End Road, E1 4LJ, London
