Senior Machine Learning Engineer

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 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.

As a Senior Machine Learning Engineer, we’ll look to you to lead development and deployment of cutting-edge AI systems for our diverse clients. You’ll design, build, and deploy scalable, production-grade ML software and infrastructure that meets rigorous operational and ethical standards.

This is an ambitious, cross-functional role requiring a blend of technical expertise, engineering leadership, and confident client-facing skills.

Leading technical scoping and architectural decisions for high-impact ML systems

Designing and building production-grade ML software, tools, and scalable infrastructure

Defining and implementing best practices and standards for deploying machine learning at scale across the business

Collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities

Acting as a trusted technical advisor to customers and partners, translating complex concepts into actionable strategies

Mentoring and developing junior engineers, actively shaping our team's engineering culture and technical depth

You understand the full ML lifecycle and have significant experience operationalising models built with frameworks like TensorFlow or PyTorch

You bring deep expertise in software engineering and strong Python skills, focusing on building robust, reusable systems

You have demonstrable hands-on experience with cloud platforms (e.g., AWS, Azure, GCP), including architecture, security, and infrastructure

You've extensive experience working with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale

You thrive in fast-paced, high-growth environments, demonstrating ownership and autonomy in driving projects to completion

You communicate exceptionally well, confidently guiding both technical teams and senior, non-technical stakeholders

Talent Team Screen (30 minutes)

Pair Programming Interview (90 minutes)

System Design Interview (90 minutes)

Commercial Interview (60 minutes)

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