33 Machine Learning Engineer jobs in United Kingdom available for remote work from home. Apply for positions such as Machine Learning Engineer, Machine Learning Engineer, Machine Learning Engineer and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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Design, develop, and refine machine learning models using Python while managing data efficiently with MongoDB. Collaborate with cross-functional teams to evaluate model performance, tune hyperparameters, and document methodologies for repeatable workflows.
Design and maintain scalable MLOps infrastructure and CI/CD pipelines for machine learning and Generative AI applications. Monitor production ML systems and manage the end-to-end model lifecycle including deployment and governance.
Design and build scalable systems for real-time fraud detection, including data pipelines and backend services in Go. Develop and deploy ML models while ensuring high standards of security, privacy, and observability.
You will own the technical direction and roadmap for the pricing machine learning engine, defining the mathematical approach to optimize signing probability, portfolio balance, and margin. You will build and ship production-grade models end-to-end while acting as the primary voice for pricing ML across commercial and engineering teams.
You will own the technical direction for pricing machine learning, designing and implementing models that dynamically set prices to balance growth and margin. Additionally, you will develop probabilistic models to optimize risk management and short-term balancing decisions while bridging the gap between modeling and production.
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You will build the AI engine that powers interactive roleplay coaching, including developing models for speech analysis, performance scoring, and real-time feedback. Additionally, you will collaborate with cross-functional teams to design and implement scalable product features.
You will design and improve financial risk scoring algorithms while building scalable data pipelines to process massive datasets. Additionally, you will deploy production-ready machine learning models for fraud detection and behavioral analysis while ensuring model stability and explainability.
You will provide technical leadership and mentorship to machine learning engineers while driving the technical strategy for content-based embeddings and relevance in ads. Additionally, you will develop and iterate on embedding models and LLM-based representations to improve ranking outcomes and ad delivery performance.
You will own the end-to-end machine learning platform, including data pipelines, training infrastructure, and deployment services. Additionally, you will lead a small engineering team and design integrations with internal and third-party systems to ensure reliable model performance.
You will lead the architecture and orchestration of scalable ML pipelines, focusing on high-performance training for fraud detection models. Additionally, you will mentor team members and drive cross-functional initiatives to optimize ML infrastructure and data quality.
Design and maintain business-critical AI agent services and production microservices using LangChain. Lead system architecture decisions and mentor junior engineers to improve engineering delivery through agentic development.
Develop and iterate on custom ML models and agentic systems for customer feedback analysis, including fine-tuning pipelines and retrieval systems. Collaborate with engineering and product teams to productionize models and translate business needs into technical ML solutions.
Lead the vision and strategic execution of AI-powered solutions to drive long-term value across the business. Manage a team of ML engineers to design and deploy scalable, production-grade AI/ML systems and inference pipelines.
Participate in research projects, interviews, and surveys to provide expert insights on workflow management tools. Share professional expertise to help researchers and organizations improve AI models and development processes.
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Participate in research projects, interviews, and surveys to provide expert insights on workflow management tools. Share professional expertise to help researchers and organizations improve AI models and development processes.
Identify and solve challenging marketplace dynamics problems by designing predictive models and conducting causal analysis. Productionize and automate ML model pipelines within Python services to drive business impact.
Design and deploy ML models for document classification, entity extraction, and summarization of complex financial documents. Build scalable production-ready ML services and contribute to the MLOps stack including CI/CD and evaluation frameworks.
You will own the end-to-end development of critical ML subsystems, including data preparation, training, and production deployment. You will collaborate with research and engineering teams to build reliable, scalable AI systems that meet performance and latency targets.
You will be responsible for building and owning end-to-end machine learning pipelines, including training, evaluation, inference, and production deployment. You will collaborate with research and application teams to integrate scalable, high-performance AI systems into products.
Evaluate and analyze LLM performance while architecting and building inference and training pipelines. You will contribute to hands-on design, model training, and deployment strategies within a technical greenfield project.
You will own the end-to-end deployment of machine learning models, including data validation, model selection, and performance monitoring. You will also collaborate with clients to understand their business needs and ensure the forecasting system delivers actionable results.
The role involves designing modular and scalable real-time data pipelines to manage large datasets and implementing custom Machine Learning algorithms within a low-latency environment. Responsibilities also include working on microservice architectures that handle the training, inference, and monitoring of thousands of ML models concurrently.
Lead the development and scaling of the Wildfire Fuel Detection Model, focusing on understanding vegetation structure and wildfire risk. Collaborate with data scientists and product teams to ensure models are accurate and production-ready.
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You will research, develop, and deploy advanced machine learning solutions for image analysis and processing within high-performance scientific software. Additionally, you will provide technical leadership and mentor team members while ensuring code quality and performance optimization for CPU and GPU environments.
You will research, develop, and deploy advanced machine learning solutions for image analysis and processing within scientific products. Additionally, you will provide technical leadership and collaborate with multidisciplinary teams to integrate these models into high-performance software applications.
You will lead and manage a team of machine learning engineers to develop and deploy fraud detection models while maintaining strict performance SLAs. You will also drive cross-functional initiatives to improve data architecture and foster innovation across the product roadmap.
Design, build, and operate high-load distributed backend services that power the company's machine learning infrastructure. Partner with ML and product teams to turn their needs into reliable, reusable platform capabilities.
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