1 to 25 of 773 Remote/Hybrid Machine Learning Jobs

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 ...

model/agent development, deployment, and monitoring. You will build foundational experience designing and implementing AI agents and cloud-based AI solutions, while learning how to maintain scalable AI systems in production environments. You'll collaborate across teams to learn and apply established best practices and reusable components, helping … have supervisory duties. Posting date: Friday 12th June 2026 {11.59pm} Skills AI Agents, Business Case Analyses, Data Analytics, Data Science, Machine Learning (ML), Machine Learning Algorithms, Machine Learning Methods, Predictive Analytics, Predictive Modeling, Python (Programming Language), Structured Query Language (SQL), Waterfall Model Shape ...

Machine Learning Engineer with generative AI, LLM or ML experience is sought by a high growth scale up based near Birmingham. Working at the forefront of national security innovation this Machine Learning Engineer will work within a R&D team collaborating with industry partners to turn … would suit a software engineer with a couple of years commercial experience within broad range of technologies and a demonstratable passion for AI/ML innovation who is looking for a truly collaborative, autonomous working environment with end to end project exposure. In return this Machine Learning Engineer ...

hands-on leadership role where you'll influence both research strategy and engineering execution while working alongside computational biologists, protein engineers, and ML researchers. The ideal candidate combines deep expertise in foundation models with a genuine interest in solving complex problems in antibody design and immunology. You'll Be Working … validate model outputs Driving best practices across experimentation, MLOps, model deployment, and platform engineering Mentoring senior engineers and helping shape the long-term ML strategy of the company What They're Looking For Essential 5+ years of experience building advanced machine learning systems in production environments Strong expertise ...

higher-level apprenticeship programmes such as Advanced Data Fellowship and AI Data Specialist. You will cover a range of topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature … Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring, Maintenance, and Continuous Learning; Data Security, Privacy, and Governance; Stakeholder Communication and Documentation The Multiverse coaching team is a diverse mix of industry experts, educators, and academics ...

higher-level apprenticeship programmes such as Advanced Data Fellowship and AI Data Specialist. You will cover a range of topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature … Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring, Maintenance, and Continuous Learning; Data Security, Privacy, and Governance; Stakeholder Communication and Documentation The Multiverse coaching team is a diverse mix of industry experts, educators, and academics ...

higher-level apprenticeship programmes such as Advanced Data Fellowship and AI Data Specialist. You will cover a range of topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature … Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring, Maintenance, and Continuous Learning; Data Security, Privacy, and Governance; Stakeholder Communication and Documentation The Multiverse coaching team is a diverse mix of industry experts, educators, and academics ...

higher-level apprenticeship programmes such as Advanced Data Fellowship and AI Data Specialist. You will cover a range of topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature … Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring, Maintenance, and Continuous Learning; Data Security, Privacy, and Governance; Stakeholder Communication and Documentation The Multiverse coaching team is a diverse mix of industry experts, educators, and academics ...

higher-level apprenticeship programmes such as Advanced Data Fellowship and AI Data Specialist. You will cover a range of topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature … Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring, Maintenance, and Continuous Learning; Data Security, Privacy, and Governance; Stakeholder Communication and Documentation The Multiverse coaching team is a diverse mix of industry experts, educators, and academics ...

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 ...

develop machine learning and LLM-based solutions Work on advanced AI systems within secure and constrained environments Apply deep technical knowledge across ML, statistics, and optimisation Contribute to real-world AI applications in defence and high-assurance settings Collaborate with engineers and clients to solve complex challenges Take … solutions deployed in edge, constrained, or air-gapped environments Apply best practices in AI safety, explainability, and robustness Collaborate with engineering teams to integrate ML systems into production environments Key Requirements: Strong understanding of machine learning fundamentals Solid foundation in mathematics and statistics Hands-on experience with LLMs ...

develop machine learning and LLM-based solutions Work on advanced AI systems within secure and constrained environments Apply deep technical knowledge across ML, statistics, and optimisation Contribute to real-world AI applications in defence and high-assurance settings Collaborate with engineers and clients to solve complex challenges Take … solutions deployed in edge, constrained, or air-gapped environments Apply best practices in AI safety, explainability, and robustness Collaborate with engineering teams to integrate ML systems into production environments Key Requirements: Strong understanding of machine learning fundamentals Solid foundation in mathematics and statistics Hands-on experience with LLMs ...

develop machine learning and LLM-based solutions Work on advanced AI systems within secure and constrained environments Apply deep technical knowledge across ML, statistics, and optimisation Contribute to real-world AI applications in defence and high-assurance settings Collaborate with engineers and clients to solve complex challenges Take … solutions deployed in edge, constrained, or air-gapped environments Apply best practices in AI safety, explainability, and robustness Collaborate with engineering teams to integrate ML systems into production environments Key Requirements: Strong understanding of machine learning fundamentals Solid foundation in mathematics and statistics Hands-on experience with LLMs ...

advanced modeling approaches) to build predictive systems at scale for identity, audience, and cross-platform measurement. The position also leads architecture and standards for ML pipelines that capture, manage, store, and utilize large-scale structured and unstructured data, ensuring data integrity, interoperability, and reliability across production environments. Responsibilities and Duties … Role: • Lead development, training, and deployment of advanced ML models for identity resolution, look-alike modeling, and cross-platform measurement; translate algorithms into production-quality code; optimize for scale and performance. • Architect scalable ML platforms and reusable components (training/inference pipelines, feature/label foundations, model serving patterns) that ...

organisations with exceptional talent across AI, Technology, CRM and Senior Leadership appointments. We work with innovative organisations that are investing heavily in Artificial Intelligence, Machine Learning and Automation technologies to transform customer experiences, operational efficiency and business performance. We are currently supporting a high-growth organisation seeking … Working alongside AI Technical Leads, Product Teams, Software Engineers and Data Scientists, you will design, develop and deploy cutting-edge AI applications using modern Machine Learning and Generative AI technologies. You will play a key role in developing enterprise AI solutions, intelligent automation platforms, conversational AI systems, voice ...

organisations with exceptional talent across AI, Technology, CRM and Senior Leadership appointments. We work with innovative organisations that are investing heavily in Artificial Intelligence, Machine Learning and Automation technologies to transform customer experiences, operational efficiency and business performance. We are currently supporting a high-growth organisation seeking … Working alongside AI Technical Leads, Product Teams, Software Engineers and Data Scientists, you will design, develop and deploy cutting-edge AI applications using modern Machine Learning and Generative AI technologies. You will play a key role in developing enterprise AI solutions, intelligent automation platforms, conversational AI systems, voice ...

hands-on engineering role, working closely with data scientists, analysts, architects, and product teams to productionise models, build data pipelines, and embed ML capabilities into live services. Key Requirements Active Security Clearance (SC) - must be current and transferable Proven experience as a Machine Learning Engineer within central government … regulated environments Strong programming skills in Python (e.g. Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch) Experience building and deploying ML models into production environments Strong knowledge of cloud platforms, particularly: Azure (ML Studio, Azure ML, Data Factory, Synapse) Experience in AWS environments is beneficial Experience with MLOps practices 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 ...