Machine Learning Engineer

You’ll work alongside machine learning engineers, data scientists and technical teams to design, build and deploy scalable ML systems - with the autonomy to shape technical approaches and engineering best practice. What you’ll be doing

• Build and deploy production-grade machine learning models, systems and infrastructure.
• Take ML solutions through the full lifecycle, from experimentation and prototyping through to deployment, monitoring and iteration.
• Work with frameworks such as PyTorch, TensorFlow and Scikit-learn.
• Develop scalable ML pipelines, tooling and reusable components that accelerate the delivery of AI systems.
• Make architectural and technical decisions around ML systems, infrastructure and deployment.
• Work closely with data scientists and engineers to turn research and models into reliable production systems.
• Apply strong software engineering practices to machine learning codebases and infrastructure.
• Help define best practices for deploying and operating ML at scale.
• Work directly with clients, translating complex ML concepts into practical technical solutions.
• At senior levels, provide technical leadership and help shape ML engineering approaches across projects.

• Strong hands-on experience as a Machine Learning Engineer, ML Software Engineer or similar.
• Strong Python skills and experience building production ML systems.
• Experience taking machine learning models from development into production.
• Practical experience with PyTorch, TensorFlow, Scikit-learn or similar ML frameworks.
• Good understanding of core ML concepts, including statistics, probability and machine learning techniques.
• Experience with cloud platforms such as AWS, Azure or GCP.
• Experience with Docker and Kubernetes or similar containerisation/orchestration technologies.
• Strong understanding of software engineering practices, system design and scalable architecture.
• Ability to work closely with data scientists, engineers and non-technical stakeholders.
• For Senior/Lead/Principal levels, we’re looking for increasing levels of technical ownership, architectural decision-making and leadership.

You’ll be working on high-impact ML problems rather than simply building applications around AI. The team operates at the intersection of machine learning, national security and AI safety, giving you the opportunity to work on challenging problems where production ML can make a genuine difference.

There’s also significant scope to influence how machine learning is engineered, deployed and scaled, particularly at Senior, Lead and Principal levels.