Lead Machine Learning Engineer

Faculty; Greater London; Hybrid; GBP 110,000 - 140,000; Full time

Job summary

Faculty in London seeks a Lead Machine Learning Engineer to set the technical direction for complex AI/ML projects, ensuring models perform at scale and in production over time by balancing technical trade-offs and guiding team priorities.

You will lead the delivery of large-scale AI-powered platforms in high-risk environments while defining project roadmaps across multiple complex workstreams. This is an ambitious leadership role with a trusted technical advisor mandate to senior stakeholders.

Qualifications

• Expert at defining technical roadmaps and managing project priorities to deliver high-stakes outcomes within high-growth environments.
• Mastery of cloud-native ecosystems and orchestration tools like Kubernetes to automate complex model lifecycles and robust CI/CD pipelines.
• Proven ability to design large-scale, AI-powered platforms and provide the technical justification for critical architectural decisions in high-risk environments.
• Experience in operationalising models within frameworks like TensorFlow or PyTorch to solve complex business challenges.

Responsibilities

• Designing, implementing, and maintaining reliable, scalable ML systems while justifying key architectural decisions for production environments.
• Driving the development of shared libraries and infrastructure for model deployment, lifecycle management, and CI/CD pipelines.
• Leading model integration with infrastructure by creating APIs and services that enable scalable AI functionality in applications.
• Overseeing the delivery of multiple complex workstreams and defining project problems in high-risk environments.
• Ensuring reliable model performance by defining testing frameworks and model versioning systems for senior engineers to implement.
• Managing and coaching multiple individuals, setting team-wide development goals to improve technical depth and client delivery.
• Executing proactive recommendations for adopting new technologies and AI frameworks to maintain Faculty's competitive market position.

Skills

Technical roadmaps

Cloud-native ecosystems

Team leadership

Production ML systems

Tools

Kubernetes

Docker

TensorFlow

PyTorch

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

As a Lead Machine Learning Engineer at Faculty, you will set the technical direction for complex AI/ML projects, ensuring models perform at scale and in production over time by balancing technical trade-offs and guiding team priorities.

You will lead the delivery of large-scale AI-powered platforms in high-risk environments while defining project roadmaps across multiple complex workstreams.

This is an ambitious, entrepreneurial leadership role where you will act as a trusted technical expert, defending your architectural rationale to senior stakeholders to ensure we deliver high-quality, high-value outputs.

What you'll be doing:

• Designing, implementing, and maintaining reliable, scalable ML systems while justifying key architectural decisions for production environments.
• Driving the development of shared libraries and infrastructure for model deployment, lifecycle management, and CI/CD pipelines.
• Leading model integration with infrastructure by creating APIs and services that enable scalable AI functionality in applications.
• Overseeing the delivery of multiple complex workstreams and defining project problems in high-risk environments.
• Ensuring reliable model performance by defining testing frameworks and model versioning systems for senior engineers to implement.
• Managing and coaching multiple individuals, setting team-wide development goals to improve technical depth and client delivery.
• Executing proactive recommendations for adopting new technologies and AI frameworks to maintain Faculty's competitive market position.

Who we're looking for:

• You are an expert at defining technical roadmaps and managing project priorities to deliver high-stakes outcomes within high-growth environments.
• You possess mastery of cloud-native ecosystems and orchestration tools like Kubernetes to automate complex model lifecycles and robust CI/CD pipelines.
• You have a proven ability to design large-scale, AI-powered platforms and provide the technical justification for critical architectural decisions in high-risk environments.
• You bring expert-level experience in operationalising models within frameworks like TensorFlow or PyTorch to solve complex, high-impact business challenges.
• You define the engineering standards and architecture patterns for agentic systems, ensuring teams build to a consistent, production-grade bar.
• You demonstrate an exceptional ability to align multi-disciplinary technical teams with broader business objectives and evolving customer needs.

The Interview Process

1. Talent Team Screen (30 minutes)
2. Introduction to the Hiring Manager (30 minutes)
3. Pair Programming Interview (90 minutes)
4. System Design Interview (90 minutes)
5. Commercial & Leadership 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.

If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part-time hours.

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.