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.

Bringing medicine to patients is complex, expensive and high-risk. Faculty’s Life Science’s team is concentrated on building AI solutions which optimise the research and commercialisation of life-changing therapies.

We partner with major pharma firms, academic research centres and MedTech start-ups to design and deliver solutions which address critical healthcare challenges, and help to democratise health for all.

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 operationalising models built with frameworks like Scikit-learn, TensorFlow, 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 at 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

1. Talent Team Screen (30 minutes)

2. Pair Programming Interview (90 minutes)

3. System Design Interview (90 minutes)

4. Commercial Interview (60 minutes)

We aim to grow the best team - not the most similar one. We know that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We’re united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations.

If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part-time hours.

A note on AI: we're happy for you to use it for research and interview prep, but please don't use it to generate answers during live interviews. We also use an AI note-taker (Metaview) in interviews so interviewers can stay present (which you can opt out of just let us know,) and every application is reviewed by a human, never decided by AI.