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 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
ML Engineer Location: Chester (Hybrid - 2x week in office) Salary: £70,000 - £80,000 About the Role I'm working with an established company who are looking to bring an ML Engineer into their team. You will report into the Head of Platform Engineering and work closely with data scientists, analysts, engineers, and design managers in a fast-paced, high-impact environment. What...
Harnham
Chester
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...
About Us We are a VC-backed startup focused on hyper-personalisation, currently in stealth. Inspired by the latest in recommender systems, we leverage transformers and graph learning alongside decision-making models to build the most engaging customer experiences for in-store retail. Our mission is to change retail forever through hyper-personalised experiences that are both simple and beautiful. About the Job – Machine...
Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio.
This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.
Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression.
This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.
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.
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