Principal Machine Learning Engineer
Faculty is hiring a Principal Machine Learning Engineer for a remote role open to applicants in United Kingdom. Level rates it ; you can apply on Faculty.
Level's read on this job
- On Level's scale, this machine learning engineer role is Builds AI ●●●●: the job is building AI systems.
- This is listed as a remote role.
- The posting mentions MLOps, AI safety and machine learning among its tools and skills.
- Level classes it as a lead role.
- Faculty has 86 other open roles on Level.
AI in this role
Serve as the technical authority on machine learning engineering, owning architectural vision and delivery of AI systems.
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 Principal Machine Learning Engineer, you'll serve as the definitive technical authority on ML engineering across the organisation, setting the standard for how we design, build, and scale AI systems. You will own the architectural vision for our most complex and high-stakes programmes, influence engineering strategy at an organisational level, and act as a trusted partner to senior leadership and executive clients.
This role goes beyond leading projects, you are a force multiplier who shapes the engineering culture, elevates the capability of those around you, and ensures our technical foundations are built to last.
What you'll be doing:
Defining and owning the organisation-wide technical vision for ML engineering, establishing architectural standards and engineering practices that scale across teams and clients.
Overseeing the end-to-end delivery of flagship, business-critical ML programmes, spanning multiple workstreams, engineering teams, and client organisations.
Driving the strategic adoption of new technologies, platforms, and ways of working, influencing how the entire engineering craft evolves.
Cultivating senior engineering talent, coaching leads and managers, building capability pipelines, and embedding a culture of technical rigour and excellence.
Acting as the principal technical voice in C-suite and executive client engagements, shaping AI strategy and securing long-term strategic partnerships.
Leading on the most significant sales and pre-sales opportunities, providing architectural authority that differentiates our proposition and wins high-value engagements.
Partnering with Data Science, Commercial, and Infrastructure leadership to align ML engineering strategy with broader business goals and growth ambitions.
Who we're looking for:
You are a recognised ML engineering authority, able to go exceptionally deep across multiple domains and provide definitive technical judgement on the hardest problems.
You have mastery of Python and a proven track record of designing and operationalising production-grade ML systems at scale, across multiple frameworks and paradigms.
You are an expert across multiple major Cloud Solution Providers and have set the architectural direction for complex, full-stack AI platforms spanning multiple organisations.
You have deep expertise in containerisation, orchestration, and MLOps at scale, and have established the standards others follow.
You have a proven track record of developing senior engineering talent, coaching leads and managers, building capability, and shaping high-performance engineering cultures.
You have a visible external profile, through publications, conference contributions, open-source leadership, or industry influence, that reflects your standing in the field.
You operate naturally at the executive level, translating complex ML engineering decisions into commercial outcomes and proactively shaping the strategic direction of client engagements.
Our Interview Process
Talent Team Screen (30 minutes)
Introduction to the role (45 minutes)
Pair Programming Interview (90 minutes)
System Design Interview (90 minutes)
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.
How we rate this
Principal Machine Learning Engineer at Faculty rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in machine learning engineer jobs at this level
Pay
- Typical machine learning engineer job
- $197k
This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you think about the risk of an AI system in this kind of role failing silently?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where mlops was part of your work. What did you do?
- Tell me about a project where architecture was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: ML Ops, AI Safety, Machine learning, MLOps, and Architecture. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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