About this role
Salary: £48,000 - 88,000 per year
Requirements: Good understanding of computer science fundamentals, including data structures, algorithms and software designPractical experience with classical machine learning techniques and awareness of modern approaches such as natural language processing and deep learningStrong Python skills and experience with common libraries such as Pandas, scikit-learn and JupyterExperience working with SQL and data pipelines to prepare and transform data for model trainingUnderstanding of model evaluation, monitoring and improving performance in a production environmentFamiliarity with tools and practices for deploying models, ideally including Git, CI workflows and containerisationComfortable working with statistical concepts to interpret data and assess model performanceAbility to work collaboratively, communicate clearly and deliver work to agreed outcomes Responsibilities: Develop machine learning models and support their deployment into productionWrite production-quality code that is robust, efficient and maintainableContribute to the implementation and improvement of pipelines, tooling and automationApply good engineering standards and practices in model developmentMonitor performance and contribute to ongoing optimisation of modelsWork with colleagues to understand requirements and prioritiesShare knowledge, contribute ideas and support a collaborative team culture Technologies: AIGitSupportJupyterMachine LearningPythonSQLpandas 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 32 week of 2026