Lead Machine Learning Engineer

Faculty
City of London
2 weeks ago
Applications closed

Related Jobs

View all jobs

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

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 to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.

Neurodiversity in AI Careers: Turning Different Thinking into a Superpower

The AI industry moves quickly, breaks rules & rewards people who see the world differently. That makes it a natural home for many neurodivergent people – including those with ADHD, autism & dyslexia. If you’re neurodivergent & considering a career in artificial intelligence, you might have been told your brain is “too much”, “too scattered” or “too different” for a technical field. In reality, many of the strengths that come with ADHD, autism & dyslexia map beautifully onto AI work – from spotting patterns in data to creative problem-solving & deep focus. This guide is written for AI job seekers in the UK. We’ll explore: What neurodiversity means in an AI context How ADHD, autism & dyslexia strengths match specific AI roles Practical workplace adjustments you can ask for under UK law How to talk about your neurodivergence during applications & interviews By the end, you’ll have a clearer picture of where you might thrive in AI – & how to set yourself up for success.