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Leads strategic relationships with AI frontier labs, hyperscalers, and government entities. Designs AI safety transformation programs, translates complex evaluation and governance concepts into client strategy, oversees multidisciplinary delivery teams, develops commercial proposals, and promotes thought leadership on AI safety, auditing, risk evaluation, and industry standards.

As a Machine Learning Engineer, you will develop software solutions using machine learning, collaborate with clients, and deliver impactful projects working within cross-functional teams.

The Senior Research Scientist will conduct high-impact AI safety research, lead evaluations and red-teaming projects, and support academic publication efforts in applied AI safety.

Design, build and deploy production-grade ML systems for retail and consumer clients. Lead technical scoping, architecture and best practices, collaborate with data scientists and engineers, operationalise models, and advise customers to deliver scalable, secure ML solutions that drive business outcomes.

As the Technical Director of AI Safety, you'll lead AI safety initiatives, oversee R&D, mentor teams, and drive innovations, representing Faculty in global AI safety discussions.

Lead organisational ML engineering strategy and architecture for high-stakes programmes. Own end-to-end delivery, drive adoption of platforms and technologies, coach senior engineering talent, shape executive client engagements, and lead technical pre-sales to win flagship AI projects.

Lead and develop product and service design within Applied AI, deliver high-impact client engagements, build design capability, define skill frameworks, and partner with business development and executive stakeholders.

The role offers veterans a one-week unpaid placement to learn about AI and data science in a leading UK tech company.

As a Senior Machine Learning Engineer, you will lead the development of AI systems, design production-grade ML software, and mentor junior engineers while engaging with clients on technical strategies.

Lead design, build and deploy production-grade ML systems and infrastructure. Make architecture decisions, implement best practices for ML at scale, advise clients, collaborate cross-functionally, and mentor junior engineers while meeting operational and ethical standards.