1 to 25 of 655 Machine Learning Jobs in the UK excluding London

data infrastructure and machine learning technologies, the company is creating an environment where engineers can work on complex problems, develop scalable ML solutions, and collaborate closely with data scientists, engineers, and product teams. This is an exciting opportunity to join a high-performing team focused on delivering cutting … build, and deploy machine learning models into production environments. You will work closely with data scientists and software engineers to develop scalable ML pipelines, optimise model performance, and integrate machine learning capabilities into production systems. The role involves working with large datasets, building robust ML systems ...

processes giving Markerstudy a critical advantage in the increasingly competitive insurance market by minimizing the development to deployment and monitoring stages of the ML lifecycle through automation. You will also be responsible for refining, tuning, deploying and maintaining machine learning methods in our machine learning pipeline … customer-centric offerings across Motor, Home and Commercial Lines businesses. The successful candidate will also enjoy opportunities for leading, coaching, and mentoring more junior ML Engineers. Key Responsibilities: Report and communicate with Senior Stakeholders, such as the Head of Data Science and Machine Learning and Director of Technical ...

Graduate AI/Data Scientist/Machine Learning Engineer – Entry Level £28,000 – £35,000 | Hybrid Opportunities | North West/UK Are you a recent graduate looking to start your career in Artificial Intelligence, Machine Learning, Data Science or Software Engineering? We are currently working with … number of innovative technology organisations who are looking to recruit Graduate AI Engineers, Data Scientists and Machine Learning Engineers into their growing teams. This is an excellent opportunity for ambitious graduates who want to work with cutting-edge technologies including Artificial Intelligence, Machine Learning, Generative ...

applying modern machine learning techniques to complex scientific and engineering challenges. As a Machine Learning Engineer, you will develop advanced ML-driven applications that enhance the performance, stability, and sensitivity of next-generation technologies. Working closely with experimental scientists and hardware engineers, you will translate complex … process high-dimensional experimental and time-series datasets Train, validate, and optimise machine learning models using modern Python-based frameworks Integrate ML inference into real-time or near-real-time control environments Stay up to date with the latest developments in machine learning and sensor fusion ...

Machine Learning Engineer LOCATION- Reading TERM- Permanent SALARY- £60,000 to £90,000 per annum (plus benefits) My client is a technology-focused organisation developing advanced AI and data-driven solutions across a range of industries. They are looking to expand their technical team by hiring a Machine … models Preparing and processing datasets for model training and evaluation Improving model performance through experimentation and optimisation Collaborating with software engineers to integrate ML solutions into applications Researching and implementing new machine learning techniques where appropriate If you are interested in this position, please apply with ...

+ 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 ...

Data Scientist (Machine Learning) – Initial 12 mth FTC £50,000+Bonus+Benefits-Remote working My client are a leading Consultancy in the Commercial Sales arena helping Businesses and Retailers all across the UK. Due to exciting growth we are building a new machine Learning team. With most … team in place we are looking for a Data Scientist (Machine Learning) to support the build and launch of our new Microsoft Fabric Lakehouse and machine learning platform. This is a largely remote role with on average just 1 day a month required in the office. ...

Data Scientist (Machine Learning) – Initial 12 mth FTC £50,000+Bonus+Benefits-Remote working My client are a leading Consultancy in the Commercial Sales arena helping Businesses and Retailers all across the UK. Due to exciting growth we are building a new machine Learning team. With most … team in place we are looking for a Data Scientist (Machine Learning) to support the build and launch of our new Microsoft Fabric Lakehouse and machine learning platform. This is a largely remote role with on average just 1 day a month required in the office. ...

public sector programmes. Key Responsibilities: Design, build, and optimise machine learning models, including NLP, computer vision, and predictive analytics Own the ML lifecycle from data preparation through to training, evaluation, and deployment Implement and maintain MLOps workflows for continuous integration and delivery of ML models Collaborate with Data … Engineers and DevOps teams to ensure production readiness and scalability Contribute to architecture decisions for ML pipelines and data flows Apply secure coding and configuration practices in line with compliance standards Mentor junior engineers and share best practices across the team Support innovation by researching emerging ML techniques and tools ...

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 ...

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 ...