Time since we first saw it
Day 64
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.
Machine Learning Engineer (Safety)
London · London, United Kingdom
Live, first seen by us 64 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 postingJoin us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients.
You will bridge the gap between AI research and real-world impact by building scalable, production-grade machine learning systems.
Build and deploy production-grade ML software, tools, and infrastructure
Create reusable, scalable solutions for ML delivery and testing of Frontier AI models
Lead technical scoping and architectural decisions for project feasibility
Define and implement standards for deploying machine learning at scale
Act as technical advisor to customers and partners, explaining ML concepts
From the employer’s posting
Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients.
You will bridge the gap between AI research and real-world impact by building scalable, production-grade machine learning systems. Partnering with clients and cross-functional teams, you will champion technical feasibility, ensure seamless delivery, and collaborate with Frontier Labs to reinforce our leadership in practical, high-stakes AI safety.
Building and deploying production-grade ML software, tools, and infrastructure.
Creating reusable, scalable solutions that accelerate the delivery of ML systems and capability testing of Frontier AI models.
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.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 4, 202664 days ago
- Recorded sightings
- 125
- Last seen by us
- Nov 8, 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.
- Python
- AWS
- Azure
- Docker
- GCP
- Kubernetes
- PyTorch
- scikit-learn
- TensorFlow
Where the posting names them
- You are comfortable building LLM applications, and are familiar with multi-agent harness tooling and AI Safety evaluation procedures. - 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 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 at Docker & Kubernetes to build and manage applications at scale
- 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 at Docker & Kubernetes to build and manage applications at scale - You are comfortable with core ML concepts, including probability, statistics, and common learning techniques.
- You are comfortable with core ML concepts, including probability, statistics, and common learning techniques. - You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch. - You're an excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders.
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
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- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
Already applied? Track this application
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.
About the role
Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients.
You will bridge the gap between AI research and real-world impact by building scalable, production-grade machine learning systems. Partnering with clients and cross-functional teams, you will champion technical feasibility, ensure seamless delivery, and collaborate with Frontier Labs to reinforce our leadership in practical, high-stakes AI safety.
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 and capability testing of Frontier AI models.
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 are comfortable building LLM applications, and are familiar with multi-agent harness tooling and AI Safety evaluation procedures.
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 at Docker & Kubernetes to build and manage applications at scale
You are comfortable with core ML concepts, including probability, statistics, and common learning techniques.
You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.
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 delivery solutions
Our Interview Process
Talent Team Screen (30 minutes)
Pair Programming Interview (90 minutes)
System Design Interview (90 minutes)
Commercial 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
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- 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.