1 to 25 of 2,264 Machine Learning Jobs in the UK

Machine Learning Engineer Join the analytics team as a Machine Learning Engineer in the insurance industry, where you'll design and implement innovative machine learning solutions. This permanent role in London offers an exciting opportunity to work on impactful projects in a forward-thinking … environment. Client Details Machine Learning Engineer This opportunity is with a medium-sized organisation in the insurance industry. The company is committed to utilising advanced analytics and machine learning to enhance its services and deliver value to its clients. Description Machine Learning Engineer Design

Machine Learning Engineer Join the analytics team as a Machine Learning Engineer in the insurance industry, where you'll design and implement innovative machine learning solutions. This permanent role in London offers an exciting opportunity to work on impactful projects in a forward-thinking … environment. Client Details Machine Learning Engineer This opportunity is with a medium-sized organisation in the insurance industry. The company is committed to utilising advanced analytics and machine learning to enhance its services and deliver value to its clients. Description Machine Learning Engineer Design

people closer together in the real world. Our fifth-generation Spectacles, powered by Snap OS, showcase how standalone, see-through AR glasses make playing, learning, and working better together. The Spectacles team is looking for a Computer Vision Engineer to join the AR team at Snap inc! What … this role, you will be working on state of the art machine learning and 3D computer vision technologies to straddle the boundaries between the real and the virtual world with the next generation of Snap’s wearable computing devices. Working from our London office, you will be collaborating

Machine Learning Operations Engineer Our financial services client based in London is looking to recruit a Machine Learning Operations Engineer ASAP. The position will be a Hybrid role be working from home and their offices in London. To be considered for the role you must have … within the company. Technical Skills required Previous experience in designing, building, optimising, deploying and managing business-critical machine learning models using Azure ML in Production environments. Experience in data wrangling using Python, SQL and ADF. Experience in CI/CD and DevOps/MLOps and version control. Familiarity

Machine Learning Operations Engineer Our financial services client based in London is looking to recruit a Machine Learning Operations Engineer ASAP. The position will be a Hybrid role be working from home and their offices in London. To be considered for the role you must have … within the company. Technical Skills required Previous experience in designing, building, optimising, deploying and managing business-critical machine learning models using Azure ML in Production environments. Experience in data wrangling using Python, SQL and ADF. Experience in CI/CD and DevOps/MLOps and version control. Familiarity

where you'll not only design and build advanced machine learning solutions but also line-manage engineers, coordinate delivery across high-impact ML projects, and define the technical bar across the team. Your compensation reflects both the strategic leadership scope and technical excellence expected in this role. What … technical direction for GenAI and retrieval-based systems, ensuring consistent architecture, tooling, and research-to-production workflows. Act as delivery owner across multiple ML streams, coordinating timelines, resources, and cross-functional collaboration. Drive best practices for scalable, reproducible, and robust ML system development. Be hands-on when needed, contributing

where you’ll not only design and build advanced machine learning solutions but also line-manage engineers, coordinate delivery across high-impact ML projects, and define the technical bar across the team. Your compensation reflects both the strategic leadership scope and technical excellence expected in this role. What … technical direction for GenAI and retrieval-based systems, ensuring consistent architecture, tooling, and research-to-production workflows. Act as delivery owner across multiple ML streams, coordinating timelines, resources, and cross-functional collaboration. Drive best practices for scalable, reproducible, and robust ML system development. Be hands-on when needed, contributing

Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities : Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions … cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data

leadership role: you’ll set the AI strategy, design and scale production NLP and machine learning systems, and grow a high-performing ML team - while closely partnering with Product and Engineering to deliver real, measurable impact for customers. This VP of AI & Machine Learning will Take … ownership of the company’s AI roadmap — from concept to production. Build and scale NLP and ML models that turn complex organisational data into meaningful insights. Lead and develop a small ML engineering team, acting as a player-coach. Establish MLOps, monitoring, governance, and responsible AI practices. Work cross-functionally

leadership role: you’ll set the AI strategy, design and scale production NLP and machine learning systems, and grow a high-performing ML team - while closely partnering with Product and Engineering to deliver real, measurable impact for customers. This Head of AI & Machine Learning will Take … ownership of the company’s AI roadmap — from concept to production. Build and scale NLP and ML models that turn complex organisational data into meaningful insights. Lead and develop a small ML engineering team, acting as a player-coach. Establish MLOps, monitoring, governance, and responsible AI practices. Work cross-functionally

Collaborate extensively with scientific and operational teams to guarantee the robustness, scalability, and reliability of our AI tools. Implementing automation and standardization throughout the ML lifecycle, your efforts will support quicker, data-informed decision-making and boost innovation. Help our CDMO's mission by turning research insights into practical solutions … learning and large language models (LLMs) in practical operational settings, transforming research findings into functional business tools. Facilitate the progress and examination of ML models, involving both standard machine learning and neural network-focused models, as requested by R&D teams. Develop standardized, reusable workflows that

intersection of data science, engineering, and commercial strategy, working closely with our product and core engineering teams to translate business challenges into deployable ML solutions. You'll have the autonomy to experiment, iterate, and bring ideas to production that directly impact revenue and user engagement. This role would suit … Machine Learning Engineer with at least three years commercial ML/AI experience gained working In a mission focussed, product led business. In return this ML Engineer will receive extensive growth and personal development opportunities as the business transitions away from legacy engineering practices. This ML Engineer based

just analyse data; you'll answer the critical questions that drive our strategy. We're looking for someone to use advanced data science and machine learning to uncover the 'why' behind customer behaviour, such as identifying complex churn triggers for our energy customers. You will also develop … Acquisition Cost (CAC). This isn't a research role. We need a "full-stack" data scientist who can own the end-to-end ML lifecycle. You will take your ideas from initial Proof of Concept (POC) and exploratory analysis all the way through to building, deploying, and maintaining production

metal components are conceived, tested, and produced - enabling breakthroughs in aerospace, energy, and beyond. This is a rare chance to apply your ML expertise to problems that have a tangible, physical impact - from inventing new alloys to optimising complex manufacturing processes. You'll collaborate with leading data scientists, engineers … materials researchers to build models that drive real-world innovation. Expect to design, validate, and deploy state-of-the-art ML pipelines that move seamlessly from concept to production. If you thrive in fast-paced, intellectually charged environments where every model could change an industry, you'll fit right in.

Role/Job title Data Scientist - Azure ML Ops & Databricks Work Location Wokingham, UK Role type - Permanent/Fixed Term/Contracting Contracting Mode of working Hybrid/office based Office based 5 Days in Office If Hybrid, how many days are required in office? NA Number of positions … other working conditions - travel/on call/shifts To be published on job boards from below onwards The Role: Data Scientist - Azure ML Ops & Databricks Your responsibilities: Data Analysis & Modeling: Utilize Python for advanced data analysis, exploration, and processing to uncover actionable insights. Develop and implement machine learning

Machine Learning Engineer (Python/MLE ) 6 Month Contract £650 - £750 Remote Umbrella Urgent Start We are looking for a number of MLE/Machine Learning Engineers for a critical 6 month contract with a household name. This role is essential for maintaining the stability … major public cloud provider (GCP, AWS, or Azure). Experience with BigQuery would be good. Machine Learning: Experience supporting and understanding ML pipelines and models in a production setting. Direct experience with Google Cloud Platform, BigQuery, and associated tooling. Experience with workflow tools like Airflow or Kubeflow. Familiarity

established data function, working on real production workloads rather than prototypes. You'll play a key role in shaping how intelligent automation, LLMs and ML-driven services are designed, deployed and continuously improved. What you'll be working on * Designing and delivering Gen AI and LLM-driven applications* Building … maintaining machine learning pipelines and data workflows* Developing cloud-native (serverless) ML components within a distributed engineering team* Improving existing ML systems and identifying opportunities for optimisation* Supporting sprint planning, estimation and shaping a healthy product backlog What you'll need to bring * Strong hands-on Python skills

smarter platform interactions Helping build scalable data pipelines and leveraging large datasets to generate insights that influence product strategy Assisting in developing and optimizing ML infrastructure to ensure scalability, efficiency, and readiness for production deployment Working closely with product and engineering teams to integrate ML models into the platform with … with strong foundations in algorithms, data structures, or statistical modelling Proficient in Python, with hands-on experience deploying models into production environments Comfortable maintaining ML infrastructure, including deployment pipelines, monitoring tools, and systems designed for scaling data and models (preferred) Experienced in fast-paced, high-performance environments, ideally within high

smarter platform interactions Helping build scalable data pipelines and leveraging large datasets to generate insights that influence product strategy Assisting in developing and optimizing ML infrastructure to ensure scalability, efficiency, and readiness for production deployment Working closely with product and engineering teams to integrate ML models into the platform with … with strong foundations in algorithms, data structures, or statistical modelling Proficient in Python, with hands-on experience deploying models into production environments Comfortable maintaining ML infrastructure, including deployment pipelines, monitoring tools, and systems designed for scaling data and models (preferred) Experienced in fast-paced, high-performance environments, ideally within high

This temporary role requires an ML Quant Engineer with expertise within an Investment Bank. The position is based in London and involves developing and implementing machine learning models to support financial decision-making. Client Details The hiring organisation is a large entity within the financial services industry. Description … Design and implement machine learning models for financial applications, with a focus on derivatives pricing, risk analytics, and market forecasting. Build scalable ML pipelines to process large volumes of financial data efficiently. Develop deep learning architectures for time series prediction, anomaly detection, and pattern recognition in market

processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering … workflows, scheduling tools (Azure Data Factory), and ensuring data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing

processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering … workflows, scheduling tools (Azure Data Factory), and ensuring data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing

Machine Learning Lead, youll be a hands-on Machine Learning Engineer with a strong track record of building and deploying ML solutions at scaleparticularly in NLP and GenAI. You will: Design, develop and scale NLP and GenAI capabilities to optimise business processes. Build out a robust … GenAI application technology stack, from experimentation through to production. Develop, maintain and improve existing ML pipelines, data transformation workflows and MLOps practices. Create serverless data/ML pipelines in cloud environments (Azure preferred). Work closely with architects, senior developers, product owners and business analysts to shape requirements, solution design

with prompt engineering, RAG architectures, and multimodal models. Contribute to internal tools for monitoring, testing, and improving AI performance. Stay on the edge of ML/AI research — we give you time and resources to explore, learn, and publish. What We’re Looking For 0–3 years of experience … environment. Is curious, ambitious, and eager to build real AI systems that have an impact. What You’ll Get Hands-on mentorship from senior ML engineers, AI researchers, and founders. Freedom to experiment with state-of-the-art models, tools, and frameworks. Modern tech stack (Python, LangChain, Hugging Face, OpenAI

Shares + Benefits A fast-growing Data & AI Product-based company at the forefront of cutting-edge AI solutions are looking for an ML Lead to join the team and spearhead a number of ambitious products, shaping the architecture, direction, and delivery of advanced AI and Machine Learning … What youll do Lead the design and development of Agentic AI systems, generative models, and NLP-powered solutions. Guide and manage a small talented ML engineering team, fostering innovation and technical excellence. Collaborate with product, data science, and engineering teams to bring state-of-the-art research into production. Evaluate