Applied Machine Learning Lead

Sky; Grays; On-site; GBP 70,000 - 90,000; Full time

Job summary

A leading media company in the United Kingdom is seeking a highly skilled Lead Machine Learning Engineer. The role focuses on advancing personalised recommendation systems by developing efficient, low-latency solutions for millions of users globally. Key responsibilities include model development, data pipeline engineering, and production deployment. Candidates should have extensive experience in machine learning, along with a proven ability to work collaboratively within tech-focused teams. This position offers a chance to innovate and optimize in a fast-paced environment.

Qualifications

• Extensive experience in machine learning and data engineering.
• Proven ability to develop efficient machine learning models.
• Strong skills in deploying and maintaining ML systems.

Responsibilities

• Design, train, and optimize machine learning models.
• Construct and maintain scalable data pipelines.
• Deploy and supervise ML models in production environments.

Skills

Machine Learning

Data Pipeline Engineering

Model Development

Collaborative Teamwork

Job description

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, 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. 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.

• 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 environm...