Location | Newcastle upon TyneDiscipline: | Football OperationsJob type: | PermanentJob ref: | 008102Expiry date: | 05 Feb 2026 23:59 Machine Learning Engineer (ML Engineer) Newcastle United Permanent Newcastle Upon Tyne Competitive Salary We are the heartbeat of the city. Come and be a part of a long and proud history where we strive to be the best in everything...
Newcastle United Football Club
Newcastle Upon Tyne
Machine Learning Quant - Start Up
Machine Learning Quant - Start UpWant to make an application Make sure your CV is up to date, then read the following job specs carefully before applying.£150,000 GBP+ performance bonus + internal fund investmentOnsite WORKINGLocation: Central London, Greater London - United Kingdom Type: PermanentMy client is a stealth start-up Quant hedge fund founded by a Math Postdoc and advised by...
ANSON MCCADE
London
Machine Learning Engineer
Machine Learning Engineer We are working in partnership with a leading technology organisation to recruit an experienced Machine Learning Engineer. The successful candidate will design, train, and optimise high-performance machine learning models, build and manage datasets for real-world sensing systems, and clearly communicate technical work to stakeholders. Based in North Somerset, you'll be part of a collaborative and forward-thinking environment...
Electus Recruitment Solutions
Banwell
Machine Learning Engineer
Apex Resources limited are on the lookout for a Machine Learning Engineer (Agentic AI) in Glasgow for a hybrid role. A leading Glasgow-based AI firm is building next-generation agentic AI products that automate complex tax and finance workflows for UK accountancy firms and in-house finance teams. The platform leverages large language models and intelligent orchestration to remove repetitive work and...
Apex Resources Ltd
Glasgow
Machine Learning Engineer
MLOps Engineer Location: London, UK (Hybrid – 2 days per week in office) Day Rate: Market rate (Inside IR35 Duration: 6 months Role Overview As an MLOps Engineer, you will support machine learning products from inception, working across the full data ecosystem. This includes developing application-specific data pipelines, building CI/CD pipelines that automate ML model training and deployment, publishing model...
Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies.
Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field.
And yet, despite this growth in education, UK employers consistently report the same problem:
Many candidates with machine learning qualifications are not job-ready.
Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks.
The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs.
This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.
Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords.
This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.
Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals.
Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused.
In most cases, the issue is not the talent market — it is the job advert itself.
Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning.
This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.
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