Lead Machine Learning Engineer, Aioi R&D Lab

• 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

Desirable

• This is a genuine dual role: real line management and delivery leadership across the Lab's ML engineering function, plus a meaningful hands-on contribution to technical delivery yourself. You'll manage a growing team of ML Engineers and Scientists, shape ML engineering practice across the Lab, and stay close enough to the work to actually lead it technically, not just report on it
• 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 the Lab
• 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