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Aioi R&D Lab – Oxford, an AI R&D company based in Oxford, invites a Senior Machine Learning Scientist to lead a growing ML team while contributing technically on projects. You will shape ML engineering practice across the Lab and stay closely involved in delivery, mentoring engineers and guiding architecture decisions.
This is a permanent, hybrid role with opportunities to influence multiple client engagements.
Aioi R&D Lab – Oxford is an AI R&D company based in Oxford, on a mission to harness AI to understand, predict, and manage risk, helping build a safer, more resilient society.
We sit at the intersection of academia and industry, working with Oxford's professors, researchers, and graduates alongside commercial spinouts and partner companies, to turn frontier research into AI that actually ships rather than just gets published.
Our work spans applied AI for insurance and adjacent industries, including supply chains, nature, autonomous driving, and the emerging challenges nobody's solved yet, alongside deep research of our own into agentic AI, privacy‑preserving technologies, trustworthy AI, complex systems modelling, and quantum computing.
We build AI products and solutions for insurers, businesses, and public‑sector organisations worldwide, and run innovative research projects that push these technologies further, helping people make better decisions in an uncertain world.
Contract: Permanent
Location: Oxford, hybrid preferred, though we'd consider fully remote for the right person
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.
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.