Platform Engineer

Remote: yes Posted: 2026-03-09

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 https://faculty.ai/impact.

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.

As an engineer in the Applied AI Platform Team, you will provide the internal delivery platform that makes world-class AI possible.

You will take ownership of a collection of Cloud infrastructure, tools, templates, and processes that technical teams use to launch and accelerate projects.

Working closely with the Applied AI team, you’ll be building and maintaining the data science, MLOps, and deployment tooling that empowers our team of over 100 Data Scientists and Machine Learning Engineers.

You will encourage engineering best practices, research new technologies, design and build new infrastructure and promote ways of working that enables us to transition from complex exploration to full-stack, production-grade machine learning products.

- Taking ownership of our existing deployment and MLOps tooling to ensure our software delivery remains a significant lever for quality and reliability.

- Contributing to the continuous evolution of our technology stack, from building new features in our notebook development environments to refining model monitoring systems.

- Collaborating with a small, fast-moving team of customer-facing technologists to design and build the infrastructure our delivery teams need to succeed.