Top Machine Learning Jobs in London

The role involves engineering machine learning solutions, optimizing models, conducting data science experiments, and leading projects while ensuring software quality and cloud integration.

The role involves developing an ML research and deployment pipeline, implementing best practices in model and feature management, and ensuring robustness in production through effective solutions and collaboration.

Lead the development of AI/ML infrastructure for simulations using large foundation models. Collaborate on realistic simulation improvements and mentor junior engineers.

As a Machine Learning Engineer, you will design, build, and deploy ML systems, manage AI project methodologies, and support delivery with technical expertise, collaborating with cross-functional teams and stakeholders.

The role involves building and scaling the reliability of AI cloud platforms, overseeing GPU compute infrastructure, incident response, and operational excellence while collaborating with various teams to enhance system efficiency and resilience.

The role involves developing and optimizing machine learning models and collaborating on research projects with quantitative researchers.

Conduct research using machine learning techniques to develop investment strategies, leveraging extensive data and computational resources.

The role involves developing machine learning models, optimising system performance, and collaborating with diverse teams on innovative projects.

As a Machine Learning Performance Engineer, you'll optimize large-scale workloads across GPU and CPU infrastructure, collaborating with researchers and engineers to improve performance and capabilities of research workloads.

Design, test, and implement innovative machine learning models to enhance predictive analytics for sports betting based on historical data.

Develop ML systems for autonomous driving, lead projects using RL and deep learning, and collaborate with cross-functional teams to improve Waymo's technology.

The Senior Machine Learning Engineer will design and build ML systems, lead product strategies, and mentor junior team members to improve search and personalization in the New Verticals business.

As a Senior Machine Learning Engineer, you'll lead the development and productionisation of ML models for customer support, ensuring reliability and collaborating with stakeholders for scalable solutions.

The Senior Data Scientist will investigate and incubate AI solutions, collaborate on product capabilities, and design scalable data infrastructure.

Lead the development and deployment of AI systems, designing scalable ML software and infrastructure while mentoring junior engineers and advising clients.

The AI/ML Quantitative Research Manager leads research efforts in quantitative strategies, develops AI/ML models for trading signals, and collaborates with internal teams to enhance trading systems.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

As an ML Solution Consultant, you'll guide R&D teams in using ML tools, ensuring effective data structuring, and maximizing the value of scientific data.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Develop and implement deep-learning and computer-vision models for histology imaging to create spatial biology biomarkers. Collaborate with engineers, clinicians and product teams, build scalable data pipelines, manage regulatory/product inputs to translate research into clinical-grade tools, and communicate results via stakeholders and publications.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

The Engineering Manager will lead a team to build and optimize ML infrastructure for drug discovery, mentor others, and ensure scalable solution deployment across varied ML workloads.

The Machine Learning Performance Engineer will enhance performance for training and inference platforms, work on optimising compilers, and design hardware architectures for AI models.

The role involves training and evaluating AI models, conducting AI training tasks, and providing expert feedback on their performance using machine learning skills.

AI Trainers will complete tasks involving analyzing, editing, and annotating data to improve AI models with real ML expertise, including performance evaluation and providing technical feedback.