Forward Deployed Engineer

Why Faculty?

We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we’ve worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here.

We don’t chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence.

Our business, and reputation, is growing fast and we’re always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology.

AI is an epoch-defining technology, join a company where you’ll be empowered to envision its most powerful applications, and to make them happen.

Our National Security and AI Safety business unit is dedicated to advancing the responsible development and deployment of AI in support of national security and global stability. From strengthening mission-critical capabilities across national security and intelligence, to working with frontier labs to provide robust AI safety red teaming and evaluation, we work at the frontier of high-stakes, high-impact missions.
We understand that powerful AI systems bring both transformative opportunities and complex risks and we are proud to partner with Government and the biggest tech organisations in the world to ensure AI is not just transformative but is also secure, trustworthy and safe for all.
Because of the nature of the work we do with our Government clients, you may need to be eligible for UK Developed Vetting (DV) and willing to work on site with our clients from time to time.

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.

• 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 Faculty’s standards for deploying machine learning at scale.
• Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders.

• You understand the full machine learning lifecycle and have experience ope rationalising models built with frameworks like Scikit-learn, Tensor Flow, 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 as 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