Senior ML Engineer

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

Job summary

A leading media and telecommunications company in the UK seeks a highly skilled Lead Machine Learning Engineer to enhance personalized recommendation systems for millions of users. You will design and optimize machine learning models, construct scalable data pipelines, and oversee model deployment. The ideal candidate has proven experience in machine learning, strong programming skills, and a collaborative spirit. This position offers the chance to work on innovative projects within a dynamic team environment.

Qualifications

• Extensive experience with machine learning frameworks and libraries.
• Strong programming skills in Python or equivalent languages.
• Proven ability to work in collaborative teams.

Responsibilities

• Design, train, and optimise machine learning models.
• Construct and maintain robust data pipelines.
• Deploy and supervise ML models in production.

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...