Lead Machine Learning Engineer

Faculty
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer, AI

Lead Machine Learning Engineer, Gen AI

Lead Software Engineer - Agentic AI/Machine Learning

About Faculty

At Faculty, we transform organisational performance through safe, impactful and human‑centric AI.


With more than a decade of experience, we provide over 350 global customers with software, bespoke AI consultancy, and Fellows from our award‑winning Fellowship programme.


Our expert team brings together leaders from across government, academia and global tech giants to solve the biggest challenges in applied AI.


Should you join us, you’ll have the chance to work with, and learn from, some of the brilliant minds who are bringing Frontier AI to the frontlines of the world.


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.


#LI-PRIO


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.


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)


What we can offer you:

The Faculty team is diverse and distinctive, and we all come from different personal, professional and organisational backgrounds. We all have one thing in common: we are driven by a deep intellectual curiosity that powers us forward each day.


Faculty is the professional challenge of a lifetime. You’ll be surrounded by an impressive group of brilliant minds working to achieve our collective goals.


Our consultants, product developers, business development specialists, operations professionals and more all bring something unique to Faculty, and you’ll learn something new from everyone you meet.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.