Machine Le arning Engineer in London

Machine Le arning Engineer in London

London Full-Time 63000 - 77000 £ / year (est.) Home office (partial)
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At a Glance

  • Tasks: Build and deploy impactful AI solutions for diverse clients using cutting-edge machine learning techniques.
  • Company: Join Faculty, a leader in responsible AI innovation since 2014.
  • Benefits: Enjoy unlimited annual leave, private healthcare, and flexible working options.
  • Other info: Diverse and inclusive team culture with excellent career growth opportunities.
  • Why this job: Make a real-world impact with AI while collaborating with top experts in the field.
  • Qualifications: Experience in machine learning frameworks and strong Python skills required.

The predicted salary is between 63000 - 77000 £ per year.

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. 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 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.

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 deliver solutions.

Our Interview Process:

  • Talent Team Screen (30 minutes)
  • Pair Programming Interview (90 minutes)
  • System Design Interview (90 minutes)
  • 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.

Machine Le arning Engineer in London employer: Faculty

At Faculty, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. As the Head of Banking AI Transformation, you will have the opportunity to lead transformative projects in a rapidly evolving sector while benefiting from our commitment to employee growth through continuous learning and development. Located in a vibrant area, our team enjoys a supportive environment that values diversity and encourages meaningful contributions to the financial services landscape.

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Contact Details:

Faculty Recruitment Team

We think you need these skills to ace Machine Le arning Engineer in London

Machine Learning Lifecycle
Scikit-learn
TensorFlow
PyTorch
Python
Software Engineering Best Practices
Cloud Platforms (AWS, Azure, GCP)