Kingfisher plc

machine learning engineer for home improvement

In short

This is a lead machine learning engineer role managing a team of ML engineers at Kingfisher, a home improvement retail group (B&Q, Screwfix, Brico Depot, Castorama, Koctas). It combines hands-on ML architecture and coding work with line management, coaching, and stakeholder communication, delivering production ML systems end-to-end. Requires proven engineering leadership experience, deep hands-on ML expertise, strong Python/SQL skills, and cloud deployment experience; GCP experience is a plus. Hybrid role based in London with about 40% in-person collaboration.

About
Kingfisher is a home improvement group whose brands include B&Q, Screwfix, Brico Depot, Castorama, and Koctas. It works to make better homes and better lives for everyone.
Responsibilities
•Own the technical direction of the workstream, making decisions on model architecture, solution design, and engineering approach
•Contribute to the codebase by writing and reviewing production code alongside the team
•Manage and develop a team of machine learning engineers, including line management, coaching, performance development, and workstream planning
•Set and uphold engineering quality standards, ensuring end-to-end delivery from experimentation through to production
•Collaborate with technology, product, and data colleagues to shape solutions and influence supporting platforms and infrastructure
•Translate business requirements into clearly scoped technical solutions and communicate recommendations and results to non-technical stakeholders
•Champion good engineering and ML practices across the Group AI function, contributing to knowledge sharing and continuous improvement
•Support data leadership in developing a strong data culture and promote the team internally and externally
Requirements
•Proven experience leading engineering teams, with direct line management responsibility and a track record of developing engineers
•Deep hands-on machine learning expertise, including designing, building, and critically evaluating models and making architectural decisions
•Strong software engineering background and experience delivering end-to-end production ML systems
•Willingness to remain an active technical contributor
•Strong proficiency in Python and SQL across the full ML development lifecycle
•Experience deploying and operating ML services on cloud platforms
•Excellent communication and stakeholder management skills, including engaging senior non-technical stakeholders and advocating for technical decisions
Nice to have: experience with GCP
Conditions
•Hybrid work model; on average, 40% of the Engineering team's time involves in-person collaboration
•Competitive benefits package and opportunities for career development

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