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Featured Jobs
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...
Machine Learning Research Engineer - LLM post-training/mid-training Our team is partnered with a materials discovery stealth venture based in San Francisco and London, led by former Oxford and Isomorphic Labs leaders in AI and experimental science. The team is pioneering large-scale language models that reason, adapt, and accelerate discovery workflows. They are combining experimental validation, synthetic data generation, and scalable...
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.
Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences.
But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design.
This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.
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