1 to 25 of 298 Machine Learning Jobs in the South East

that transform industrial automation into a scalable, productised platform. As the business continues to grow, they are now looking for an Applied Scientist/ML Engineer to help develop the machine learning systems powering the next generation of intelligent robotics. This is an opportunity for someone who enjoys … modern machine learning techniques to solve complex industrial challenges. This includes leveraging foundation models, generative AI approaches, synthetic data generation, and scalable ML infrastructure to improve robotics capability and deployment speed. A key responsibility will be developing robust neural network architectures focused on object detection, segmentation, pose estimation ...

work is thoughtful, challenging, and purposeful, rather than driven by novelty or trend. What you'll be doing You'll take ownership of complex ML problems, applying scientific thinking and pragmatism in equal measure. You will: Lead end-to-end machine learning delivery , from problem definition through experimentation … with internal teams and selected external partners working at the leading edge of AI What we're looking for This role suits a senior ML practitioner who values judgement, evidence, and outcomes over theoretical or tooling purity. Essential experience: Proven experience operating at senior practitioner level as a Machine ...

collaborate extensively with scientific and operational teams to ensure the robustness, scalability, and reliability of AI tools. By implementing automation and standardisation throughout the ML lifecycle, your work will support faster, data-informed decision-making and innovation. Key Responsibilities: Design, build, and maintain resilient Machine Learning Operations pipelines … learning and large language models in practical operational settings, transforming research findings into functional business tools. Support the development and evaluation of ML models, including both standard machine learning and neural network-focused models, as requested by R&D teams. Develop standardised, reusable workflows that ...

machine learning delivery in a role that blends technical depth with real world impact. You will work on production grade ML systems that directly improve operational and commercial outcomes, with the backing of a highly skilled, collaborative data team. The Company They are a specialist, data led organisation … learning models into live production environments Designing and maintaining MLOps workflows, including model lifecycle management Developing APIs and containerised services to support ML products Implementing DevOps best practices across development and production Collaborating cross functionally to align data, modelling, and business requirements Your Skills and Experience Strong commercial ...

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 ...

Japan, and 1 in Seattle to build the future of advanced metal components. To do this it uses proprietary software packages which combine advanced machine learning and physical modelling to invent better alloys, devise better ways to process them, and design better 3D-printed components. Responsibilities Design, develop … validate novel machine learning models to optimize manufacturing processes and material composition Collaborate closely with process engineers, material scientists and other domain experts to identify and engineer the most meaningful features Develop Alloyed’s machine learning platforms to facilitate adoption and application of validated models Work ...

novels such as Judge Dredd. Job Overview We’re seeking a skilled and innovative Machine Learning Engineer to join our cutting‐edge ML Team. You’ll be part of a small, highly collaborative group dedicated to pushing the boundaries of AI/ML technology in gaming. Position Summary … future of our game products. Key Responsibilities Generate game levels and 3D worlds. Enhance our game products. Develop custom tools for our teams. Integrate ML and deep learning (DL) models into our production workflows and real‐time gaming environments. Implementation, and optimisation of ML algorithms and tools for game ...

client's organization, the Data Science team tackles complex business challenges across various domains, including customer experience, supply chain, and technology. They leverage advanced machine learning, generative AI, and large language models (LLMs) to optimize prices, enhance store operations, and personalize customer experiences. Team members enjoy a collaborative … expand their expertise and make a significant impact. Your Role:As a Data Scientist, you'll be at the forefront of developing and deploying machine learning solutions that transform decision-making processes. Your key responsibilities will include: Designing and implementing robust models to optimize business operations. Building ...

specialist for machine learning and GenAI. Working closely with data engineering, analytics, QA and product, you will build the foundations for scalable ML and AI use cases across the organisation. Key responsibilities include: Owning and delivering end-to-end ML and GenAI solutions, from data preparation through … production deployment. Designing and building robust ML pipelines, CI/CD processes and MLOps frameworks. Leading GenAI initiatives such as document processing, LLM fine-tuning, prompt engineering and orchestration using modern frameworks. Organising and processing large, complex datasets to enable AI and ML use cases. Acting ...

influence the technical roadmap, and work directly with experienced founders and a senior engineering team. What you'll be doing: Designing, building and deploying ML-powered features across a production platform Working across Python and TypeScript, integrating models into scalable, real-time systems Fine-tuning and training NLP models using … best practices from the ground up Collaborating closely with product, engineering, and leadership What we're looking for: Strong commercial experience building and deploying ML models in production Proficiency in Python and familiarity with TypeScript/JavaScript Experience with NLP or recommendation systems is a bonus Excited by start ...

Project Work with Hugging Face to build a sentiment analysis classifier using real-world AI techniques. Step 8 – AI Prompt Engineering Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems … Combine prompt engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine ...

Project Work with Hugging Face to build a sentiment analysis classifier using real-world AI techniques. Step 8 – AI Prompt Engineering Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems … Combine prompt engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine ...

Project Work with Hugging Face to build a sentiment analysis classifier using real-world AI techniques. Step 8 – AI Prompt Engineering Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems … Combine prompt engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine ...