Forward Deployed Engineer

Faculty Science Limited; Greater London; On-site; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Unlimited Annual Leave

Private healthcare

Enhanced parental leave

Hybrid Working

Wellbeing support

Job summary

Faculty Science Limited is hiring a Machine Learning Engineer to bring production-grade ML into real-world client solutions. You will help shape scalable software architecture, define best practices for deployment and work with cross-functional teams to ensure timely delivery of high-quality ML systems.

You will operate across the full ML lifecycle, applying frameworks like TensorFlow or PyTorch, and leverage cloud platforms and container orchestration to deploy at scale.

Qualifications

• Experience with end-to-end ML project lifecycle and productionising models.
• Strong Python coding skills and software engineering practices.
• Hands-on with cloud platforms and container orchestration for scalable ML applications.
• Ability to translate complex ML concepts for non-technical stakeholders.

Responsibilities

• Build and deploy production-grade ML software, tools, and infrastructure.
• Create reusable, scalable ML solutions to accelerate delivery.
• Collaborate with engineers, data scientists and commercial leads on client challenges.
• Lead technical scoping and architectural decisions for feasibility and impact.
• Define and implement standards for deploying ML at scale.

Skills

Python

ML deployment

Cloud platforms

Software engineering best practices

Communication

Tools

TensorFlow

PyTorch

Scikit-learn

Docker

Kubernetes

AWS

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 National Security and AI Safety business unit is dedicated to advancing the responsible development and deployment of AI in support of national security and global stability. From strengthening mission-critical capabilities across national security and intelligence, to working with frontier labs to provide robust AI safety red teaming and evaluation, we work at the frontier of high-stakes, high-impact missions.
We understand that powerful AI systems bring both transformative opportunities and complex risks and we are proud to partner with Government and the biggest tech organisations in the world to ensure AI is not just transformative but is also secure, trustworthy and safe for all.
Because of the nature of the work we do with our Government clients, you may need to be eligible for UK Developed Vetting (DV) and willing to work on site with our clients from time to time.

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 as 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

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

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.