Machine Learning Architect

Sky UK
Ruislip
1 day ago
Create job alert

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‑gives fans control, fresh perspectives, and predictive insights during the action.


As a Lead 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.


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 sports insights or sports data‑driven ML systems, with clear ownership of technical direction, mentoring, and delivery. Deep 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. Working knowledge of modern ML techniques, including Generative AI, and how emergent models can extract insights from multi‑modal 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. Experience designing scalable, low‑latency architectures, including real‑time or near real‑time data processing (e.g., streaming systems) suitable for live or rapidly evolving sports use cases. Strong communication skills with the ability to inspire, guide, and clearly articulate complex strategies to executives, cross‑functional teams, and stakeholders.


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.


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.


#J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Architect

Machine Learning Engineer

Machine Learning - (Healthcare) - Fixed Term 12 Months - microTECH Global LTD

Artificial Intelligence / Machine Learning Engineer - 12 Month Fixed Term Contract

Artificial Intelligence / Machine Learning Engineer - 12 Month Fixed Term Contract

Artificial Intelligence / Machine Learning Engineer - 12 Month Fixed Term Contract

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.