About the job
Why Join Faculty?
Founded in 2014, Faculty believes that artificial intelligence is a transformative technology of our era. We have partnered with over 350 global clients to enhance their performance through human-centric AI solutions. Discover our real-world impact here.
At Faculty, we prioritize innovation over trends. Our focus is on developing and implementing responsible AI that delivers measurable results. We possess exceptional technical, product, and delivery expertise, catering to a diverse clientele across sectors including government, finance, retail, energy, life sciences, and defense.
As our reputation grows, we seek individuals who share our intellectual curiosity and ambition to leave a positive impact through technology.
Join us in shaping the future of AI—where your ideas and efforts will create significant advancements.
About Our Team
The Defence team at Faculty is dedicated to creating and integrating human-centered AI solutions that provide our nation with a competitive advantage in defense. We work closely with clients to deliver ethical, dependable, and innovative AI solutions for high-stakes scenarios, helping to maintain global stability crucial for our security.
Given the nature of our work, candidates must be eligible for UK Security Clearance (SC) and should be prepared to work on-site with clients up to three days a week, which may involve travel across the UK. When not engaged on client sites, you will have the flexibility to work from our London office or remotely anywhere within the UK.
About the Role
We invite you to join us as a Machine Learning Engineer, where you will play a pivotal role in delivering tailored, impactful AI solutions for our varied clientele.
Your contributions will be vital in transitioning machine learning from theoretical frameworks to practical applications. This includes participating in scalable software architecture development and establishing best practices. Collaborating with clients and cross-functional teams, you will ensure the technical viability and timely delivery of high-quality, production-grade ML systems.

