Top Machine Learning Jobs in London

Develop scalable ML systems for driving behaviors, leading RL algorithms, and collaborating on technical projects to enhance Waymo's autonomous technology.

Lead development and deployment of AI systems, design scalable ML software, mentor junior engineers, and collaborate with teams to solve client challenges.

Lead the development and optimization of machine learning algorithms, owning the end-to-end model development and collaborating with Data Engineers for integration into live systems.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

Four-month, full-time research fellowship focused on ML systems and performance. Fellows build and evaluate infrastructure-heavy projects (e.g., CPU simulators, accelerator backends, large-scale distributed training), use external models/APIs, receive mentorship, funding for compute, and aim to produce public research outputs.

Build and optimize ML infrastructure and data platforms for large-scale multimodal datasets, supporting training, evaluation, lifecycle management, and production inference. Work on data ingestion, distributed training, experiment tracking, model versioning, and scalable serving while improving reliability, performance, and observability. Provide technical leadership at senior levels.

The role involves designing scalable data pipelines, implementing custom ML algorithms, and working on microservice architectures for ad optimization models.

Lead engineering systems and teams for an AI chat application, focusing on backend, mobile, and desktop. Oversee strategy, architecture, integration, and high-impact technical decisions for reliable user experiences.

Lead design and implementation of Preply's ML platform: experiment tracking, artifact management, scalable GPU training and inference, CI/CD for ML, observability, drift detection, and LLM/RAG capabilities; mentor engineers and align platform with product and cost goals.

As a Senior Machine Learning Scientist at Depop, you'll design and deliver ML solutions for search and ranking, conduct experiments, and collaborate with product and engineering teams.

As a Machine Learning Engineer, you'll optimize and scale ML applications in drug discovery, collaborating with a team to enhance model training and deployment.

The Senior Platform Engineer in Machine Learning at Monzo will design, maintain infrastructure for ML models, and work with various cloud technologies.

Lead and mentor AI/ML engineering teams to design, build, deploy, and monitor production ML and LLM solutions. Collaborate with clients and cross-functional teams, define project scope, ensure model governance and responsible AI, implement MLOps/CI-CD pipelines, and continuously evaluate and improve model performance.

The Senior AI Transformation Consultant works with clients to identify AI opportunities, define robust business cases, and oversee AI project deployments, ensuring solutions deliver value.

Manage the end-to-end product lifecycle for financial crime tools, focusing on compliance, innovation, and backlog management while liaising between engineering and business teams.

The Machine Learning Engineer will research algorithms for networking, develop ML infrastructure, maintain documentation, and assist in technical communication.

The Machine Learning Engineer III will design and optimize AI systems, utilizing techniques like deep learning and statistical modeling to solve real-world business problems, collaborate globally, and communicate complex data effectively.

Design, build, and deploy end-to-end ML and generative AI solutions (ranking, recommender, chatbots, agentic systems). Handle data engineering, feature engineering, modeling, evaluation, deployment, monitoring, and cross-functional communication to deliver business impact.

Lead and mentor a team of AI/ML Engineers, developing production-grade AI solutions that extract value from structured and unstructured data, with a focus on customer-centric performance.

The role involves developing and supporting data processing pipelines, collaborating with ML Engineers, and improving infrastructure while mentoring team members.

As a Machine Learning Engineer, you'll build and deploy production-grade ML software, lead technical decisions, and collaborate with teams to deliver AI solutions.

Design, build and deploy production-grade ML systems for retail and consumer clients. Lead technical scoping, architecture and best practices, collaborate with data scientists and engineers, operationalise models, and advise customers to deliver scalable, secure ML solutions that drive business outcomes.

The ML Data Engineer will design and manage data pipelines for large-scale unstructured data, primarily images, ensuring efficient ingestion and preprocessing while collaborating with ML engineers for model training.

Lead architectural design and engineering build-out of company-wide MLOps and agentic AI platforms. Develop and productionize ML/LLM models, RAG, multi-agent systems, and GNNs; implement training, evaluation, serving, feature/vector stores, monitoring, and CI/CD; set engineering standards; collaborate with data science and senior stakeholders; represent company externally.