1 to 25 of 1,581 Permanent Machine Learning Jobs in England

advanced machine learning and computer vision models used within clinical research and drug development. The business is building large-scale, production-grade ML systems applied to complex 3D imaging data. This role is highly engineering-focused, sitting between machine learning, infrastructure and deployment. You will work … closely with ML Scientists and engineering teams to ensure models can be trained and deployed reliably at scale. Location: 1 day per week in London - Shepherd's Bush (4 days remote) Salary: £70,000 £90,000 per annum, with flexibility for the right candidate, plus benefits Requirements for Senior/ ...

Competitive Salary | London | Hybrid Working 🚀 Are you a Junior Machine Learning Engineer looking to kick-start your career working on real-world ML systems used at scale? We’re looking for a curious and motivated Junior Machine Learning Engineer to join a collaborative, data-driven technology … learn from experienced engineers in a fast-moving environment. You’ll be part of a modern engineering function working on large-scale data and ML products, contributing to model development, data pipelines, and deployment workflows that power business-critical platforms. 🔍 In this role, you’ll: 🤖 Build, train, and evaluate machine ...

Competitive Salary | London | Hybrid Working Are you a Junior Machine Learning Engineer looking to kick-start your career working on real-world ML systems used at scale? We're looking for a curious and motivated Junior Machine Learning Engineer to join a collaborative, data-driven technology … learn from experienced engineers in a fast-moving environment. You'll be part of a modern engineering function working on large-scale data and ML products, contributing to model development, data pipelines, and deployment workflows that power business-critical platforms. In this role, you'll: Build, train, and evaluate machine ...

+ Bonuses & Benefits Location: Cheltenham (3 days on-site per week) Security Clearance: Active Enhanced DV (West) Required Key Skills: Machine Learning, AWS/Azure, Python, NLP, AI Who We Are? We are seeking a highly skilled Data Scientist with enhanced DV clearance to join a globally recognised … techniques to solve complex challenges Conducting research and innovation to develop cutting-edge solutions Keeping up to date with the latest developments in ML and data technologies Engaging in the full data science lifecycle, from data collection to model deployment Producing clear and well-documented technical outputs Delivering high ...

access services quickly and safely. Our Applied Scientist team consists of about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely … Have strong experience in machine learning and computer vision. Have a strong record of successfully delivering high-performance ML-driven products. Have a deep understanding of machine learning theory. Have strong coding skills in Python and PyTorch. Care about building fair and cutting-edge machine ...

access services quickly and safely. Our Applied Scientist team consists of about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely … good fit if you: Have strong experience in machine learning and computer vision. Have a strong record of successfully delivering high-performance ML-driven products. Have a deep understanding of machine learning theory. Have strong coding skills in Python and PyTorch. Care about building fair ...

summary Postdoctoral Researcher in Machine Learning analysis of Magnetic Resonance Imaging (MRI) Applications are invited for an enthusiastic and motivated Post-doctoral Researcher to join the Lysosomal Disorders Unit at Cambridge University Hospitals NHS Foundation Trust. The post (Band 7) is funded by an award from industry … years. The successful applicant will work as part of a team of clinical and imaging specialists. The ethically-approved award is to develop a machine-learning technique to simulate quantitative Dixon fat/water images from a database of traditional T1 and T2-weighted MR images of muscle. ...

navigate into the future with a focus on how to scale out into the wider industry. Implementing and driving the digital roadmap, ensuring that ML, Automation and AI enable the business to meet ‘Treasury targets” This role will involve partnering with 3rd parties that include suppliers, start-ups and universities … applied machine learning and AI to improve commercial impact, cost avoidance, safety, performance and customer outcomes. Build and lead the in-house ML team (10+ staff) and vendors to deploy AI products and frameworks with industry leading ROI, £50m+ over 3 years. Lead the in-House and near ...

same! Key Responsibilities: Build, maintain, and productionise machine learning models using PyTorch or similar frameworks. Design and optimise data pipelines for ML inference at scale. Work with large datasets using Big Data tools. Deploy ML workflows using AWS infrastructure . Collaborate with data engineers and product teams … integrate ML solutions into RealityMine’s products. Investigate and troubleshoot customer feedback on data quality and drive model improvements from experimentation . Promote best practices in ML engineering and data quality across the business. Adhere to Company Policies and Procedures with respect to Security, Quality and Health & Safety. AI first ...

same! Key Responsibilities: Build, maintain, and productionise machine learning models using PyTorch or similar frameworks. Design and optimise data pipelines for ML inference at scale. Work with large datasets using Big Data tools. Deploy ML workflows using AWS infrastructure . Collaborate with data engineers and product teams … integrate ML solutions into RealityMine’s products. Investigate and troubleshoot customer feedback on data quality and drive model improvements from experimentation . Promote best practices in ML engineering and data quality across the business. Adhere to Company Policies and Procedures with respect to Security, Quality and Health & Safety. AI first ...

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 … technology? You could be progressing your career working on real-world problems within a high successful SaaS tech company that provides AI and ML products for automotive innovators to design better cars faster and achieve greater sustainability through Machine Learning. As a Machine Learning Researcher you will ...

Machine Learning Engineer, you’ll work closely with Data Scientists, Data Engineers and delivery teams to productionise models and build robust, scalable ML systems within client environments. This role is highly hands-on, with a strong focus on deployment, performance and reliability. Key Responsibilities Translating data science models … into scalable, production-ready machine learning solutions. Designing and building end-to-end ML pipelines, from data ingestion to deployment and monitoring. Collaborating with data engineers on data architecture, pipelines and feature stores. Working closely with data scientists to productionise models and improve performance. Deploying, monitoring and maintaining ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...