51 to 75 of 1,713 Machine Learning Jobs in the UK

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 ...

Learning Engineer, you’ll play a central role in advancing our Generative Physics simulation platform. You’ll work at the intersection of ML research and engineering—contributing to core model development, shaping model architecture, and delivering performant systems that integrate seamlessly into our real-world design optimization workflows. … work closely with our ML research team, software engineers, and industry partners to deploy robust, scalable models that deliver real-world impact. Responsibilities Physics-Focused AI Model Development: Design and train deep learning models for physics simulation across aerodynamic and engineering domains. Scalability & Performance: Drive optimization efforts for model ...

applied artificial intelligence, solving complex real-world problems in national security and defence environments. The Role You will design, develop and deploy advanced ML models within secure environments, contributing across the full lifecycle from research through to production. Projects may include: Computer vision and pattern recognition systems Synthetic data generation … modelling Deep neural networks (DNNs) for real-world defence applications Simulation and AI-driven modelling environments Advanced research-led ML solutions You will collaborate with research scientists, software engineers and domain experts to translate cutting-edge techniques into deployable capability. Key Skills & Experience Proven experience working within the UK defence ...

Senior Data Scientist and Machine Learning Researcher, you will be responsible for the technical development and leadership of AI/ML projects from initial idea scoping right through to final project delivery both in customer and internal domains. You will demonstrate novel thinking and propose new ideas … with minimal guidance Brings experience in scoping, designing, and delivering data-centric solutions while working collaboratively across disciplines Undertake research and applied AI/ML tasks on both customer and internal research projects Generate ideas for new research directions and provide technical leadership in small project groups Mentor more junior ...

disciplines – Mechanical, Electronic, Electrical and Software Engineering – within the Digital team we develop software for IoT edge devices, cloud services, frontend UI, AI/ML models in computer vision, and Data Analysis. We are seeking a talented and enthusiastic Data Engineer to join our talented AI/ML team. … experienced professionals and gain valuable hands-on experience across the entire product development lifecycle. Responsibilities of the role • Develop, deploy and validate high-performing machine learning models for computer vision applications, such as image classification, object detection, image segmentation, and video analysis. • Conduct thorough data analysis, feature engineering ...

disciplines – Mechanical, Electronic, Electrical and Software Engineering – within the Digital team we develop software for IoT edge devices, cloud services, frontend UI, AI/ML models in computer vision, and Data Analysis. We are seeking a talented and enthusiastic Data Engineer to join our talented AI/ML team. … experienced professionals and gain valuable hands-on experience across the entire product development lifecycle. Responsibilities of the role • Develop, deploy and validate high-performing machine learning models for computer vision applications, such as image classification, object detection, image segmentation, and video analysis. • Conduct thorough data analysis, feature engineering ...

disciplines – Mechanical, Electronic, Electrical and Software Engineering – within the Digital team we develop software for IoT edge devices, cloud services, frontend UI, AI/ML models in computer vision, and Data Analysis. We are seeking a talented and enthusiastic Data Engineer to join our talented AI/ML team. … experienced professionals and gain valuable hands-on experience across the entire product development lifecycle. Responsibilities of the role • Develop, deploy and validate high-performing machine learning models for computer vision applications, such as image classification, object detection, image segmentation, and video analysis. • Conduct thorough data analysis, feature engineering ...

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 ...

combines scale, innovation and purpose. As a Machine Learning Engineer, you'll help maintain the stability and performance of core data and ML systems across Europe. This technical engineering role focuses on reliability, optimisation and critical fixes, ideal if you excel at investigating and debugging complex data flows … ML issues in live production environments. We're looking for individuals with: Experience: Proven background as a Machine Learning Engineer. Technical Skills: Strong in SQL and Python (Pandas, Scikit-learn, Jupyter, Matplotlib). Data transformation & manipulation : experience with Airflow, DBT and Kubeflow Cloud: Experience with GCP and Vertex ...

Learning Engineer to join our growing team. This is an exciting opportunity for women who want to advance their career in AI/ML, work on meaningful projects, and thrive in a supportive, collaborative environment. What Youll Do: Design, develop, and deploy machine learning models using modern … stakeholders to turn ideas into impactful solutions. Optimize and deploy models with tools like TensorFlow Serving, TorchServe, ONNX, and TensorRT. Build and manage ML pipelines using MLflow, Kubeflow, and Azure ML Pipelines. Work with large-scale data using PySpark and integrate ML solutions into production environments. Monitor and improve model ...

ingestion Marketplace or data aggregation platforms Entity resolution, record linkage or deduplication systems Matching systems or recommendation engines Geospatial or address based data platforms ML systems deployed into production You do not need sector experience, but you should be comfortable working with complex datasets where the same entity appears across … multiple sources. What They Are Looking For Strong Data Science background with hands on machine learning experience Experience deploying ML models into production environments Understanding of data engineering concepts and modern cloud data platforms Architectural thinking around data platforms and ML systems Experience working with messy, multi source ...

Redefine What’s Possible. We’re working with an early-stage deep tech company developing a next-generation analytical platform at the intersection of machine learning, mathematics, and experimental science. They … hiring one of their first technical team members, a Machine Learning Research Engineer, to help design and develop the core ML-driven analysis engine that underpins the entire platform. This is not a narrow modelling role. It’s an opportunity to help define the research direction, shape ...