Lead Machine Learning Engineer

Lead Machine Learning Engineer

We believe in better. And we make it happen. 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.

What you’ll do:

we are seeking a highly skilled Lead Machine Learning Engineer to advance our personalised recommendation systems by developing efficient, low‑latency solutions that serve millions of users globally. The successful candidate will collaborate closely with data scientists, engineers, and product managers to design intelligent content recommendation mechanisms and drive the ongoing advancement of our Machine Learning Platform.

• Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis.
• Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large‑scale datasets.
• Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance.
• Lead the design and analysis of A/B tests and offline experiments to evaluate model efficacy and support continuous improvement.
• Engage with multidisciplinary teams to align machine learning initiatives with business objectives and user needs.
• Evaluate emerging research in machine learning, deep learning, and personalisation for potential integration within existing systems.

• Demonstrated expertise in the full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance.
• Advanced proficiency in Python and knowledge of ML libraries/frameworks (e.g., Tensor Flow, PyTorch).
• Experience using ML training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and model serving technologies (e.g., Tensorflow Serving, Triton, Torch Serve).
• Experience with high‑volume data processing and real‑time streaming architectures.
• Strong understanding of recommendation system design and personalisation algorithms.
• Familiarity with Generative AI and its applications in production settings.
• Exceptional communication and analytical problem‑solving skills.
• Proven successful experience in mentoring less experienced engineers to improve their technical skills.

On arrival you can grab a coffee or a light breakfast from the subsidised cafés or restaurants on site. You’ll settle in at your desk, check Slack, attend the team stand‑up, then dive into your first task. Lunch offers several options: the Pavilion, onsite gym, or a community meetup. The afternoon is flexible – team retrospectives, coffee breaks, or focused work.

This routine supports collaboration and continuous delivery.

• 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

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 embrace hybrid working, splitting time between unique office spaces and remote work.

Our Osterley Campus is a 10‑minute walk from Syon Lane train station. Free shuttle buses connect to Osterley, Gunnersbury, Ealing Broadway and South Ealing tube stations. On campus, you’ll find subsidised restaurants, cafés, a Waitrose, a gym, cinema and other amenities.

This role is open to all candidates. If your application is successful, we may require a criminal record check. Depending on your role and the nature of any convictions, we might need to withdraw the offer.

Mid‑Senior level

Full‑time

Engineering and Information Technology

Broadcast Media Production and Distribution