Time since we first saw it
Day 47
Similar jobs usually come down between day 7 and day 71.
From 4,682 similar postings that came down. Coming down is not always a hire.
Lead Machine Learning Engineer
London · London, United Kingdom
Live, first seen by us 47 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 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 postingAs 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.
Design and maintain reliable, scalable ML systems for production
Build shared libraries and infrastructure for model deployment and CI/CD
Create APIs and services that integrate models into applications
Oversee delivery of multiple complex AI workstreams
Manage and coach multiple engineers, setting team development goals
From the employer’s posting
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.
Designing, implementing, and maintaining reliable, scalable ML systems
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
Managing and coaching multiple individuals, setting team-wide development goals
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 21, 202647 days ago
- Recorded sightings
- 88
- Last seen by us
- Nov 6, 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
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
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Kubernetes
- PyTorch
- TensorFlow
Where the posting names them
- 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 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.
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
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
Has Faculty posted this job before?
Yes. We’ve followed 5 other Lead Machine Learning Engineer jobs there since June 2026. All 5 are still up.
Large employers often post one title for several teams, so these can be separate openings.
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
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
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
Talent Team Screen (30 minutes)
Introduction to the Hiring Manager (30 minutes)
Pair Programming Interview (90 minutes)
System Design Interview (90 minutes)
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
Source notes
Source excerptsSelected 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
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
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.