Machine Learning Engineer

Latinx in AI (LXAI)
Bournemouth
4 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Faculty AI

Full-time • On-site • Bournemouth, Dorset, United Kingdom • Technology


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.


Because of the potential to work with our UK Defence clients, you will need to be eligible for UK SC clearance and willing to work up to three days per week on site with these customers, which may require travel to locations outside of our London base. The minimum requirement for SC clearance is 5 years continuous residence in the UK up to the present.


About The Role

You will design, build, and deploy production‑grade software, infrastructure, and MLOps systems that leverage machine learning. The work you do will help our customers solve a broad range of high‑impact problems in the Defence and National Security arena.


What You’ll Be Doing

You are engineering‑focused, with a keen interest and working knowledge of operationalised machine learning. You have a desire to take cutting‑edge ML applications into the real world. You will develop new methodologies and champion best practices for managing AI systems deployed at scale, with regard to technical, ethical and practical requirements. You will support both technical, and non‑technical stakeholders, to deploy ML to solve real‑world problems.



  • Building software and infrastructure that leverages Machine Learning;
  • Creating reusable, scalable tools to enable better delivery of ML systems
  • Working with our customers to help understand their needs
  • Working with data scientists and engineers to develop best practices and new technologies; and
  • Implementing and developing Faculty’s view on what it means to operationalise ML software.

As a Rapidly Growing Organisation, Roles Are Dynamic And Subject To Change. Your Role Will Evolve Alongside Business Needs, But You Can Expect Your Key Responsibilities To Include:



  • Working in cross‑functional teams of engineers, data scientists, designers and managers to deliver technically sophisticated, high‑impact systems.
  • Working with senior engineers to scope projects and design systems
  • Providing technical expertise to our customers
  • Technical Delivery

Who We’re Looking For

You can view our company principles here. We look for individuals who share these principles and our excitement to help our customers reap the rewards of AI responsibly.


To Succeed In This Role, You’ll Need The Following - These Are Illustrative Requirements And We Don’t Expect All Applicants To Have Experience In Everything (70% Is a Rough Guide):



  • Understanding of, and experience with the full machine learning lifecycle
  • Working with Data Scientists to deploy trained machine learning models into production environments
  • Working with a range of models developed using common frameworks such as Scikit‑learn, TensorFlow, or PyTorch
  • Experience with software engineering best practices and developing applications in Python.
  • Technical experience of cloud architecture, security, deployment, and open‑source tools ideally with one of the 3 major cloud providers (AWS, GCP or Azure)
  • Demonstrable experience with containers and specifically Docker and Kubernetes
  • An understanding of the core concepts of probability and statistics and familiarity with common supervised and unsupervised learning techniques
  • Demonstrable experience of managing/mentoring more junior members of the team
  • Outstanding verbal and written communication.
  • Excitement about working in a dynamic role with the autonomy and freedom you need to take ownership of problems and see them through to execution

We Like People Who Combine Expertise And Ambition With Optimism -- Who Are Interested In Changing The World For The Better -- And Have The Drive And Intelligence To Make It Happen. If You’re The Right Candidate For Us, You Probably:



  • Think scientifically, even if you’re not a scientist - you test assumptions, seek evidence and are always looking for opportunities to improve the way we do things.
  • Love finding new ways to solve old problems - when it comes to your work and professional development, you don’t believe in ‘good enough’. You always seek new ways to solve old challenges.
  • Are pragmatic and outcome‑focused - you know how to balance the big picture with the little details and know a great idea is useless if it can’t be executed in the real world.

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.