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Machine Learning Engineering ManagerLondonAbout the RoleAt Deliveroo we have an outstanding data science organisation, with a mission to enable the highest quality human and machine decision-making. We work throughout the company - in product, business and platform teams - using analysis, experimentation, causal inference and machine learning techniques. We are uniquely placed to use data to help make better decisions...
Machine Learning Researcher - LLM/VLMAre you a PhD-educated Machine Learning Researcher looking for a new opportunity? If so, our client, a global consumer electronics company, is actively expanding their team. This role is based at one of their flagship AI centres in Cambridge, Cambridgeshire.Key Responsibilities:As a Machine Learning Researcher, you will:Work on on-device LLMs and VLMs, as well as adaptive...
Machine Learning Researcher (NLP, LLMs) | FinTechWe are searching for an Machine Learning Researcher (NLP, LLMs) to join a Series A startup on a mission to modernise outdated financial infra!They're using cutting-edge tech to build smarter, faster systems that rethink how finance works from the ground up. This collaborative, forward-thinking team, are building powerful, AI-driven solutions usingopen-source LLMs, replacing outdated...
Machine Learning EngineerDay Rate:Up to £650 outside IR35Industry:Defence/National SecurityLocation:CentralLondon - HybridDuration:3 Month RollingStart Date:1 Week NoticeThe Opportunity:The UK’s leading AI Consultancy seeks an influential ML Engineer to push the boundaries of innovation in a mission-critical defence project.The Role:You'll join a high-performing ML engineering team of data science and AI experts solving complex challenges in Radar and Sonar Signal Processing to...
Company:My client is on a mission to combat misinformation and plagiarism in the publishing industry, using cutting-edge technology around Large Language Models (LLMs). They specialise in quickly assessing the validity of research, allowing for faster turnaround times for their clients. As a result of their rapid growth, they are now looking to expand their team with a Machine Learning Engineer.Role:As...
Machine Learning Researcher (NLP, LLMs) | FinTechWe are searching for an Machine Learning Researcher (NLP, LLMs) to join a Series A startup on a mission to modernise outdated financial infra!They're using cutting-edge tech to build smarter, faster systems that rethink how finance works from the ground up. This collaborative, forward-thinking team, are building powerful, AI-driven solutions usingopen-source LLMs, replacing outdated...
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Machine learning (ML) has rapidly moved from academic research labs to the heart of industrial and governmental operations. Its ability to uncover patterns, predict outcomes, and automate complex tasks has revolutionised industries ranging from finance to retail. Now, the public sector—encompassing government departments, healthcare systems, and defence agencies—has become an increasingly fertile ground for machine learning jobs. Why? Because government bodies oversee vast datasets, manage critical services for millions of citizens, and must operate efficiently under tight resource constraints. From using ML algorithms to improve patient outcomes in the NHS, to enhancing cybersecurity within the Ministry of Defence (MOD), there’s a growing demand for skilled ML professionals in UK public sector roles. If you’re passionate about harnessing data-driven insights to solve large-scale problems and contribute to societal well-being, machine learning jobs in the public sector offer an unparalleled blend of challenge and impact. In this article, we’ll explore the key reasons behind the public sector’s investment in ML, highlight the leading organisations, outline common job roles, and provide practical guidance on securing a machine learning position that helps shape the future of government services.
Machine learning (ML) has swiftly become one of the most transformative forces in the UK technology landscape. From conversational AI and autonomous vehicles to fraud detection and personalised recommendations, ML algorithms are reshaping how organisations operate and how consumers experience products and services. In response, job opportunities in machine learning—including roles in data science, MLOps, natural language processing (NLP), computer vision, and more—have risen dramatically. Yet, as the demand for ML expertise booms, professionals face a pivotal choice about how they want to work. Some choose day‑rate contracting, leveraging short-term projects for potentially higher immediate pay. Others embrace fixed-term contract (FTC) roles for mid-range stability, or permanent positions for comprehensive benefits and a well-defined career path. In this article, we will explore these different employment models, highlighting the pros and cons of each, offering sample take‑home pay scenarios, and providing insights into which path might pay better in 2025. Whether you’re a new graduate with a machine learning degree or an experienced practitioner pivoting into an ML-heavy role, understanding these options is key to making informed career decisions.
The Model Needs More Than Math When ChatGPT went viral and London start‑ups raised seed rounds around “foundation models,” many professionals asked, “Do I need to learn PyTorch to work in machine learning?” The answer is no. According to the Turing Institute’s UK ML Industry Survey 2024, 39 % of advertised ML roles focus on strategy, compliance, product or operations rather than writing code. As models move from proof‑of‑concept to production, demand surges for specialists who translate algorithms into business value, manage risk and drive adoption. This guide reveals the fastest‑growing non‑coding ML roles, the transferable skills you may already have, real transition stories and a 90‑day action plan—no gradient descent necessary.
Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.
Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.
Machine learning has swiftly become a cornerstone of modern technology, transforming entire industries—healthcare, finance, e-commerce, and beyond. As a result, demand for machine learning engineers, data scientists, and ML researchers continues to surge, creating a rich landscape of opportunity for job seekers. But if you’re new to the field—or even an experienced developer aiming to transition—the question arises: Which programming language should you learn first for a successful machine learning career? From Python and R to Scala, Java, C++, and Julia, the array of choices can feel overwhelming. Each language boasts its own community, tooling ecosystem, and industry use cases. This detailed guide, crafted for www.machinelearningjobs.co.uk, will help you align your learning path with in-demand machine learning roles. We’ll delve into the pros, cons, and ideal use cases for each language, offer a simple starter project to solidify your skills, and provide tips for leveraging the ML community and job market. By the end, you’ll have the insights you need to confidently pick a language that catapults your machine learning career to new heights.
