Machine Learning Engineer

Salary: £48,000 - 88,000 per year

Requirements:

• Good understanding of computer science fundamentals, including data structures, algorithms and software design
• Practical experience with classical machine learning techniques and awareness of modern approaches such as natural language processing and deep learning
• Strong Python skills and experience with common libraries such as Pandas, scikit-learn and Jupyter
• Experience working with SQL and data pipelines to prepare and transform data for model training
• Understanding of model evaluation, monitoring and improving performance in a production environment
• Familiarity with tools and practices for deploying models, ideally including Git, CI workflows and containerisation
• Comfortable working with statistical concepts to interpret data and assess model performance
• Ability to work collaboratively, communicate clearly and deliver work to agreed outcomes

Responsibilities:

• Develop machine learning models and support their deployment into production
• Write production-quality code that is robust, efficient and maintainable
• Contribute to the implementation and improvement of pipelines, tooling and automation
• Apply good engineering standards and practices in model development
• Monitor performance and contribute to ongoing optimisation of models
• Work with colleagues to understand requirements and priorities
• Share knowledge, contribute ideas and support a collaborative team culture

Technologies:

• AI
• Git
• Support
• Jupyter
• Machine Learning
• Python
• SQL
• pandas

More:

We are Kingfisher, a team of over 74,000 passionate people bringing our brands B&Q, Screwfix, Brico Depot, Castorama and Koctas to life. Guided by our purpose, Better Homes. Better Lives. For Everyone., we believe a better world starts with better homes, and we work every day to make that a reality. This is an opportunity to make a significant impact across one of the largest retail groups in Europe as a Machine Learning Engineer supporting the delivery and operationalisation of advanced AI solutions from our Group AI team. You will work in a high-performing engineering team with a hybrid working model that blends home working with in-person collaboration, averaging around 40% of your time with the engineering team in person. We offer an inclusive environment, encourage new ideas and experimentation, and provide a competitive benefits package with opportunities to stretch and grow your career.

last updated 33 week of 2026