Top Machine Learning Jobs in London

Join as a founding engineer building AI-powered control systems for industrial robots. Design, build, and deploy software and hardware integrations across perception, robot control, edge deployments, and industrial workflows. Work hands-on in an on-site lab and customer sites, installing and calibrating sensors and welding systems, tuning motion planning, and collaborating with vendors and partners.

As a Product Feature Owner, you will oversee ADAS features from concept to production, collaborating with OEMs and engineering teams to align features with customer needs and ensure compliance with industry standards.

As a Research Engineer in Alignment Science, you'll conduct experiments on AI safety, focusing on AI control and alignment stress-testing, while collaborating with cross-functional teams.

The Machine Learning Researcher will build deep learning models for trading strategies, collaborating with a team to optimize systems and approaches to tackle market challenges.

As the Head of Machine Learning Engineering, you will define the ML strategy, lead teams, deliver scalable solutions, and shape AI governance for enhanced customer travel experiences.

Lead the development and optimization of AI and machine learning models, mentor junior engineers, and enhance AI-driven features for the platform.

Train, fine-tune, and deploy large-scale multimodal models (vision, video, audio, speech). Build multimodal data pipelines, run experiments, optimize MoE and inference (quantization, distillation, low-latency), and develop multimodal alignment/RLHF pipelines. Define evaluation benchmarks and ship models to production.

Train and post-train LLMs using SFT/RLHF/DPO, build reward models and large-scale data pipelines, run distributed multi-GPU training, design evaluation benchmarks, and optimize models for production inference and serving.

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.

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

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.

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.

Lead design, deployment, and iteration of large-scale ranking models (recommendations and search). Collaborate cross-functionally, manage end-to-end ML lifecycle, drive research and innovation, and mentor team members while delivering measurable product outcomes.

As a Research Engineer, you will develop and optimize AI models for drug discovery, collaborating with scientists to advance ML applications in biopharmaceuticals.

As an AI Research Engineer at Helsing, you'll architect and enhance deep learning frameworks, optimize infrastructure for distributed training, and develop strategies for large datasets while collaborating with a cross-functional team.

The role involves developing, fine-tuning, and optimizing large language models, building ML infrastructure, and implementing CI/CD practices for AI/ML systems.

Develop, deploy, and maintain scalable AI/ML pipelines and production models, including LLMs and generative AI. Optimize model performance, latency, and cost across AWS, GCP, and Azure. Apply MLOps practices such as CI/CD, versioning, monitoring, logging, and maintenance. Collaborate with data scientists, software engineers, solution architects, and cross-functional teams to integrate AI capabilities into products and workflows.

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.

Lead development of enterprise commercial intelligence: build data models (pipeline, forecasting, segmentation), deploy AI/ML for forecasting and churn, create dashboards, establish data governance, partner with senior leaders to drive insight-led decisions and adoption across global teams.

The Research Manager will lead AI-powered insight projects, manage client relationships, oversee team execution, and innovate using AI tools to improve insights delivery.

Develop and apply sequence‑modelling ML techniques to DNA to interpret variants, discover genes, and model regulation. Train, fine‑tune and evaluate models, collaborate with computational and experimental teams, leverage large-scale datasets, design robust biological evaluations, and integrate ML advances into translational target discovery.

Lead enterprise sales for Everpure's AI/HPC data infrastructure, driving large deals with F1000 companies. Build executive relationships, lead cross-functional pursuit teams, influence product roadmap, manage a pipeline to exceed revenue targets, and evangelize all-flash storage and data pipeline solutions for AI workloads.

The role involves building and maintaining ML and cloud infrastructure for training large-scale generative models. Responsibilities include designing GPU clusters, supporting distributed training, managing storage systems, and collaborating with researchers and engineers.

Design and prototype generative ML models for protein design, build and maintain ML data pipelines and infrastructure, optimize training/inference on cloud accelerators, collaborate with protein engineers and biologists to plan wet-lab testing and iterate models based on experimental results, and share research externally.