Machine Learning Engineer

Faculty; United Kingdom; On-site; GBP 60,000 - 80,000; Full time

Benefits offered by this job

Unlimited Annual Leave Policy

Private healthcare and dental

Enhanced parental leave

Family-Friendly Flexibility

Sanctus Coaching

Hybrid Working

Job summary

A leading AI consultancy in the UK seeks a Machine Learning Engineer to develop impactful AI solutions for clients. The role involves building production-grade ML software, collaborating with cross-functional teams, and operationalising models using tools like TensorFlow and PyTorch. The ideal candidate will have strong Python skills and experience with cloud services. Employees enjoy hybrid working arrangements and benefits such as unlimited annual leave and private healthcare.

Qualifications

• Experience in operationalising ML models with various frameworks.
• Solid software engineering best practices.
• Hands-on experience with cloud platforms and security.

Responsibilities

• Build and deploy production-grade ML software.
• Create scalable solutions for ML systems.
• Collaborate to solve client challenges.

Skills

Machine learning lifecycle understanding

Python programming

Cloud platforms and infrastructure

Container orchestration tools (Docker, Kubernetes)

Communication skills

Tools

Scikit-learn

TensorFlow

PyTorch

AWS

Azure

GCP

Job description

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 Defence team is focused on building and embedding human-centered AI solutions which give our nation a competitive edge in the defence sector. We collaborate with our clients to bring ethical, reliable and cutting-edge AI to high-stakes situations and maintain the balance of global powers essential to our liberty.

Because of the nature of the work we do with our Defence clients, you will need to be eligible for UK Security Clearance (SC) and willing to work up to three days per week on‑site with these customers which may require travel to locations throughout the UK.

When not required on client sites, you’ll have the flexibility to work from our London office or remotely from elsewhere within the UK.

About the role

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.

What you’ll be doing:

• 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.

Who we’re looking for:

• 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 deliver solutions.

Our Interview Process

1. Talent Team Screen (30 minutes)
2. Pair Programming Interview (90 minutes)
3. System Design Interview (90 minutes)
4. Commercial Interview (60 minutes)

Our Recruitment Ethos

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.

Some of our standout benefits:

• Unlimited Annual Leave Policy
• Private healthcare and dental
• Enhanced parental leave
• Family‑Friendly Flexibility & Flexible working
• Sanctus Coaching
• Hybrid Working (2 days in our Old Street office, London)

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.