Time since we first saw it
Day 16
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
London · London, United Kingdom
Live, first seen by us 16 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 › Life Sciences — team source
Department: Engineering › Life Sciences
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 be instrumental in bringing machine learning out of the lab and into the real world, contributing to scalable software architecture and defining best practices.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 22, 202616 days ago
- Recorded sightings
- 17
- 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.
- Python
- AWS
- Azure
- Docker
- GCP
- Kubernetes
- PyTorch
- scikit-learn
- TensorFlow
Where the posting names them
- 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 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.
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.
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 6 other Machine Learning Engineer jobs there since June 2026. All 6 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
Bringing medicine to patients is complex, expensive and high-risk. Faculty’s Life Science’s team is concentrated on building AI solutions which optimise the research and commercialisation of life-changing therapies.
We partner with major pharma firms, academic research centres and MedTech start-ups to design and deliver solutions which address critical healthcare challenges, and help to democratise health for all.
About the role
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 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'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
The 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.
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
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.