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Featured Jobs
Machine Learning Engineer
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
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 Research Engineer - NLP / LLM
An incredible opportunity for a Machine Learning Research Engineer to work on researching and investigating new concepts for an industry-leading, machine-learning software company in Cambridge, UK. This unique opportunity is ideally suited to those with a Ph.D. relating to classic Machine Learning and Natural Language Processing and its application to an ever-advancing technical landscape. On a daily basis you will...
RedTech Recruitment Ltd
Horseheath
Machine Learning Researcher Statistics Python AI
Machine Learning Researcher (PhD Statistics Python AI R&D) Cambridge / WFH to £85k Are you a tech savvy, PhD educated, Machine Learning Researcher looking for an opportunity to work on complex and interesting systems at the cutting edge of AI technology? You could be progressing your career working on real-world problems within a high successful SaaS tech company that provides...
Client Server
Cambridge
Machine Learning Scientist
Machine Learning Scientist [Analyst/Associate] About the job As a Machine Learning Scientist on the AI team at Cerberus, you’ll work on high-impact projects that combine the pace of a startup with the reach of a global investment platform. Our team partners directly with internal investment desks as well as portfolio companies across industries to deliver machine learning solutions that unlock...
Cerberus Capital Management
City of London
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...
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.
Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level.
Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas:
Linear algebra essentials (vectors, matrices, projections, PCA intuition)
Probability & statistics (uncertainty, metrics, sampling, base rates)
Calculus essentials (derivatives, chain rule, gradients, backprop intuition)
Basic optimisation (loss functions, gradient descent, regularisation, tuning)
If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews.
This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.
Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world.
That makes it a natural home for many neurodivergent people.
If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists.
This guide is written for neurodivergent ML job seekers in the UK. We’ll explore:
What neurodiversity means in a machine learning context
How ADHD, autism & dyslexia strengths map to ML roles
Practical workplace adjustments you can ask for under UK law
How to talk about neurodivergence in applications & interviews
By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.
As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before.
Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations.
Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.
Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers.
Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.
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