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# Lead Machine Learning Engineer

- **Company:** Aioi Nissay Dowa Europe
- **Location:** Oxford, UK (Remote available)
- **Experience:** Expert
- **Salary:** £81,322.0 - £112,488.0
- **Contract:** Permanent contract
- **Skills:** Machine Learning, Software Engineering, Machine Learning Operations
- **Published:** October 30, 2026
- **Apply:** https://www.cv-library.co.uk/job/225780409/Lead-Machine-Learning-Engineer

## About the Role

* A STEM degree or equivalent practical experience, plus 7+ years in ML engineering, software engineering, or a related technical role, including experience leading technical work across teams.
 * A strong software engineering background with real production-system delivery experience.
 * Experience managing or leading ML engineers or technical contributors in a delivery-focused setting.
 * Comfort supporting pre-sales or technical discovery work alongside commercial teams.
 * The judgement to lead solution design and assure engineering quality without being the primary hands-on contributor on every project.
 * Strong communication skills: you'll need to challenge stakeholder expectations sometimes, not just relay them.

Bonus points: experience establishing ML engineering/MLOps best practice from scratch, background in regulated or enterprise environments, or a track record of driving reuse through shared frameworks and tooling.

## Description

* Line-manage and develop ML engineers: coaching, mentoring, feedback, and input into hiring and onboarding.
 * Stay hands-on with technical delivery on selected projects, alongside your leadership responsibilities across multiple engagements.
 * Work with the Lead ML Scientist and Principal Engineer to build ML engineering best practice across Oxfida.
 * Work closely with the Project and Account Management teams to design and scope ML solutions that drive value for our customers.
 * Lead multidisciplinary teams of ML engineers, scientists, and software engineers, and support solution design so systems are robust and scalable.
 * Provide oversight and quality assurance across concurrent projects, flagging technical and delivery risks early.
 * Lead use case discovery and support pre-sales work alongside the commercial team.
 * Drive reuse of shared frameworks and standards across projects, and communicate technical risk clearly to non-technical stakeholders.