Machine Learning Engineer, Faculty

About the Role

Our Defence team is focused on building and embedding human-centered AI solutions which give our nation a competitive edge in the defence sector. We collaborate with our clients to bring ethical, reliable and cutting-edge AI to high-stakes situations and maintain the balance of global powers essential to our liberty. Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients. You will be instrumental in bringing machine learning out of the lab and into the real world, contributing to scalable software architecture and defining best practices. Working with clients, and cross-functional teams, you'll ensure technical feasibility and timely delivery of high-quality, production-grade ML systems.

What the job involves

• Building and deploying production-grade ML software, tools, and infrastructure
• Creating reusable, scalable solutions that accelerate the delivery of ML systems
• Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges
• Leading technical scoping and architectural decisions to ensure project feasibility and impact
• Defining and implementing [Employer hidden]’s standards for deploying machine learning at scale
• Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders

Who you are

• Because of the nature of the work we do with our Defence clients, you will need to be eligible for UK Security Clearance (SC) and willing to work up to three days per week on site with these customers, which may require travel to locations outside of our London base
• You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch
• You possess strong Python skills and solid experience in software engineering best practices
• You bring hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security
• You've worked with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale
• You are comfortable with core ML concepts, including probability, statistics, and common learning techniques
• You're an excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders
• You thrive in a fast-paced environment, and enjoy the autonomy to own scope, solve and delivery solutions

Application process

• Talent Team Screen (30 minutes)
• Pair Programming Interview (90 minutes)
• System Design Interview (90 minutes)
• Commercial Interview (60 minutes)