Back to jobs

Lead Machine Learning Engineer (Safety)

London · London, United Kingdom

Live, first seen by us 14 days ago
Pay
Pay not listed
Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting
Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting
Team
Engineering › National Security & Safety — team source
Department: Engineering › National Security & Safety
From the employer’s posting

Before you apply

Sponsorship
Visa sponsorship not confirmed — sponsorship source
Do you require visa sponsorship either now, or at any point in the future, to work in the UK? This would cover either upon joining Faculty or if a visa needs to be extended at a later date.
From the employer’s application form

What you’ll work on

Full posting

As a Lead Machine Learning Engineer at Faculty, you’ll drive the technical strategy, architecture, and deployment of cutting-edge, secure AI systems across high-stakes domains.

Working alongside Frontier Labs, you will champion practical, safety-first AI frameworks, model capability evaluations, and multi-agent systems.

  • Set technical strategy, architecture and capability testing for complex ML and Frontier AI systems

  • Architect production-grade, secure ML software and scalable cloud infrastructure

  • Set company-wide standards for safe, reliable and ethical ML deployment

  • Advise senior clients on AI safety and technical strategy

  • Mentor and develop engineering talent

From the employer’s posting
As a Lead Machine Learning Engineer at Faculty, you’ll drive the technical strategy, architecture, and deployment of cutting-edge, secure AI systems across high-stakes domains. Bridging AI research and production impact, you’ll set the benchmark for engineering excellence and operationalise scalable ML infrastructure.
Working alongside Frontier Labs, you will champion practical, safety-first AI frameworks, model capability evaluations, and multi-agent systems. You will act as a principal advisor to client leadership, mentor engineering talent, and steer cross-functional teams to solve critical, real-world AI challenges.
Steering technical strategy, system architecture, and rigorous capability testing for complex ML systems and Frontier AI models
Architecting production-grade, secure ML software, orchestration tools, and scalable cloud infrastructure
Establishing company-wide standards and best practices for safe, reliable, and ethical machine learning deployment at scale
Advising senior clients and partners as a trusted AI safety and technical authority, articulating complex strategies clearly
Mentoring and developing engineering talent, actively elevating team capabilities and nurturing an inclusive, high-performing culture

Tools in this posting

  • Python
  • AWS
  • Azure
  • Docker
  • GCP
  • Kubernetes
  • PyTorch
  • TensorFlow
Source — Tool mentions in context
- You have significant leadership experience designing, deploying, and securing scalable LLM applications, multi-agent frameworks, and AI safety evaluation processes. - You bring exceptional software engineering fundamentals and advanced Python expertise, with a focus on building resilient, enterprise-ready systems. - You demonstrate deep hands-on experience with cloud platforms (AWS, Azure, GCP), scalable cloud architectures, container orchestration (Docker, Kubernetes), agent sandboxing, and end-to-end cybersecurity.
- You bring exceptional software engineering fundamentals and advanced Python expertise, with a focus on building resilient, enterprise-ready systems. - You demonstrate deep hands-on experience with cloud platforms (AWS, Azure, GCP), scalable cloud architectures, container orchestration (Docker, Kubernetes), agent sandboxing, and end-to-end cybersecurity. - You possess mastery over the full ML lifecycle, with proven expertise operationalising production systems using PyTorch, TensorFlow, or similar frameworks.
- You demonstrate deep hands-on experience with cloud platforms (AWS, Azure, GCP), scalable cloud architectures, container orchestration (Docker, Kubernetes), agent sandboxing, and end-to-end cybersecurity. - You possess mastery over the full ML lifecycle, with proven expertise operationalising production systems using PyTorch, TensorFlow, or similar frameworks. - You excel in fast-paced, high-growth environments, bringing autonomy, strategic ownership, and a track record of guiding technical decisions.

Posting record

Report an error
Status in our records
Live
First seen by us
Oct 21, 202614 days ago
Recorded sightings
17
Last seen by us
Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 14

Similar jobs usually come down between day 6 and day 69.

From 4,284 similar postings that came down. Coming down is not always a hire.

Faculty is hiring

36 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • ML Engineer hiring is steady
  • Its jobs tend to stay up a long time
Hiring record

Your next step

Check the employer’s posting for the current role and application details.

Applying through Ashby

  • No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.

Already applied? Track this application

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

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 role

As a Lead Machine Learning Engineer at Faculty, you’ll drive the technical strategy, architecture, and deployment of cutting-edge, secure AI systems across high-stakes domains. Bridging AI research and production impact, you’ll set the benchmark for engineering excellence and operationalise scalable ML infrastructure.

Working alongside Frontier Labs, you will champion practical, safety-first AI frameworks, model capability evaluations, and multi-agent systems. You will act as a principal advisor to client leadership, mentor engineering talent, and steer cross-functional teams to solve critical, real-world AI challenges.

What you'll be doing:

  • Steering technical strategy, system architecture, and rigorous capability testing for complex ML systems and Frontier AI models

  • Architecting production-grade, secure ML software, orchestration tools, and scalable cloud infrastructure

  • Establishing company-wide standards and best practices for safe, reliable, and ethical machine learning deployment at scale

  • Collaborating with cross-functional teams and executive stakeholders to translate high-impact client needs into resilient AI solutions

  • Advising senior clients and partners as a trusted AI safety and technical authority, articulating complex strategies clearly

  • Mentoring and developing engineering talent, actively elevating team capabilities and nurturing an inclusive, high-performing culture

  • Partnering with Frontier Labs to advance safety evaluation procedures, multi-agent harness tooling, and robust AI governance frameworks

Who we're looking for:

  • You have significant leadership experience designing, deploying, and securing scalable LLM applications, multi-agent frameworks, and AI safety evaluation processes.

  • You bring exceptional software engineering fundamentals and advanced Python expertise, with a focus on building resilient, enterprise-ready systems.

  • You demonstrate deep hands-on experience with cloud platforms (AWS, Azure, GCP), scalable cloud architectures, container orchestration (Docker, Kubernetes), agent sandboxing, and end-to-end cybersecurity.

  • You possess mastery over the full ML lifecycle, with proven expertise operationalising production systems using PyTorch, TensorFlow, or similar frameworks.

  • You excel in fast-paced, high-growth environments, bringing autonomy, strategic ownership, and a track record of guiding technical decisions.

  • You communicate persuasively with both technical teams and senior non-technical stakeholders, translating complex technical risks into clear strategic direction.

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)

     

#LI-PRIO

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.

Source details

View original posting ↗

Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay

No pay amount identified in the saved description.

Location & working pattern

London, United Kingdom

Working pattern and location restrictions need checking in the full posting.

Work authorization

No clear work-authorization passage found. Eligibility is unconfirmed.

AI answers unavailable

We couldn’t identify enough role detail in this saved description to support an AI answer. Read the full posting