Lead Machine Learning 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.

About the team

Our Energy, Transition and Environment business unit is pioneering meaningful change in the clean energy revolution. Our vision is to accelerate the transition to net-zero emissions and drive efficiencies for a new era of utility companies.

We believe that the responsible, and intelligent, deployment of AI is critical to the success of this mission. We partner with a wide range of clients - from major energy operators, to GreenTech startups, and national infrastructure providers - to build solutions which return measurable impact and move us towards a smarter, cleaner, and more sustainable world.

About the role

Join us as a Lead Machine Learning Engineer to spearhead the technical direction and delivery of complex, innovative AI projects. You will act as a technical expert, applying your skills across various projects from AI strategy to client-side deployments, while ensuring architectural decisions are sound and reliable.

This role demands a balance of deep technical expertise and strong leadership, focusing on driving innovation, fostering team growth, and building reusable solutions across the organisation. If you're ready to manage high-risk projects and deliver practical, innovative outcomes, this is your chance to shape our future.

#LI-PRIO

What you'll be doing

• Setting the technical direction for complex ML projects, balancing trade-offs, and guiding team priorities.

• Designing, implementing, and maintaining reliable, scalable ML/software systems and justifying key architectural decisions.

• Defining project problems, developing roadmaps, and overseeing delivery across multiple workstreams in often ill-defined, high-risk environments.

• Driving the development of shared resources and libraries across the organisation and guiding other engineers in contributing to them.

• Leading hiring processes, making informed selection decisions, and mentoring multiple individuals to foster team growth.

• Proactively developing and executing recommendations for adopting new technologies and changing our ways of working to stay ahead of the competition.

• Acting as a technical expert and coach for customers, accurately estimating large work-streams and defending rationale to stakeholders.

Who we're looking for

• You are a technical expert among your peers, capable of going deep on particular topics and demonstrating breadth of knowledge to solve almost any problem.

• You possess strong Python skills and practical experience operationalising models using frameworks like Scikit-learn, TensorFlow, or PyTorch.

• You are an expert in at least one major Cloud Solution Provider (e.g., Azure, GCP, AWS) and have led teams to build full-stack web applications.

• You have hands-on experience with containerisation tools like Docker and orchestration via Kubernetes.

• You can successfully manage and coach a team of engineers, setting team-wide development goals to improve client delivery.

• You find novel, clever solutions for project delivery and take ownership for successful project outcomes.

• You're an excellent communicator who can proactively help customers achieve their goals and guide both technical teams and non-technical stakeholders.

The Interview Process

Talent Team Screen (30 minutes)
Introduction to the role (45 minutes)
Pair Programming Interview (90 minutes)
System Design Interview (90 minutes)
Commercial & Leadership Interview (60 minutes)

Our Recruitment Ethos<