Machine Learning Engineer (Recommendation)

Job Description 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, Sky Q 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.

• What You'll Be Doing
• Model

Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis.

• Data
• Pipeline

Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets.

• Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance.
• Experimentation: Lead the design and analysis of A/B tests and offline experiments to evaluate model efficacy and support continuous improvement.
• Cross-Functional Collaboration: Engage with multidisciplinary teams to align machine learning initiatives with business objectives and user needs.
• Research & Innovation: Evaluate emerging research in machine learning, deep learning, and personalisation for potential integration within existing systems.
• What You'll Bring
• Demonstrated expertise in the full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance.
• Proficiency in Python and knowledge of ML libraries/frameworks (e. g., Tensor Flow, Py Torch).
• Experience using ML Training frameworks (e. g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. 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.
• Good communication and analytical problem-solving skills.
• 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
• Demonstrated expertise in the full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance.
• Proficiency in Python and knowledge of ML libraries/frameworks (e. g., Tensor Flow, Py Torch).
• Experience using ML Training frameworks (e. g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. 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.
• Good communication and analytical problem-solving skills.
• 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
• A Typical Day at the Office

When you come in, you can grab a coffee or a bit of breakfast from one of the many (subsidised) cafés or restaurants on site.

Settle in at your desk, have a quick look at Slack to see what's happening in the tech communities, then catch up with everyone at the team stand-up.

After that, you'll join your team and pick the first task to get cracking on.

At lunchtime, you've got a few choices: head to The Pavilion for a bite with the team, pop to the onsite gym for a quick workout, or join in with a lunchtime community meetup - whatever suits you.

Once you're back, you'll carry on working with your team on your current feature.

Later in the afternoon, the team might fancy a quick coffee break before wrapping up the day with a team retrospective.

When you come in, you can grab a coffee or a bit of breakfast from one of the many (subsidised) cafés or restaurants on site.

Settle in at your desk, have a quick look at Slack to see what's happening in the tech communities, then catch up with everyone at the team stand-up.

After that, you'll join your team and pick the first task to get cracking on.

At lunchtime, you've got a few choices: head to The Pavilion for a bite with the team, pop to the onsite gym for a quick workout, or join in with a lunchtime community meetup - whatever suits you.

Once you're back, you'll carry on working with your team on your current feature.

Later in the afternoon, the team might fancy a quick coffee break before wrapping up the day with a team retrospective.

Global OTT Technology

Our team develops and supports market-leading video streaming services, underpinned by state-of-the-art engineering principles.

We do this at huge scale: for over 50 million customers globally, spanning NBCUniversal Peacock in the US and Sky, NOW and Sky Showtime across Europe.

No matter the device, the time or the place, we make sure that our diverse audiences can easily find and enjoy whatever they want to watch, choosing from the world's best entertainment, news and sport.

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 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