Top Machine Learning Jobs in London

The role involves leading the identification, design, and delivery of AI solutions, managing transformation programs, and ensuring measurable outcomes and team capabilities.

Design, build and deploy autonomous robotic systems for real-world environments. Develop planning, perception and control systems, train and deploy ML models, integrate sensors/actuators, and drive long-term robotics strategy.

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.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Lead and develop teams of data scientists, driving impactful projects through experimentation, causal inference, and applied machine learning while collaborating with various stakeholders.

Provide EU-focused data privacy and digital regulatory legal counsel across business segments. Lead privacy program development, support HR, Marketing, M&A, and transactional work, manage incident response, regulatory engagement, DPIAs, cross-border data transfers, and advise on EU Data Act, GDPR, data governance, and product compliance.

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.

The Global Named Account Manager drives revenue growth through strategic sales to global enterprise brands, managing complex sales cycles and collaborating with key teams.

As a Senior Ecommerce Manager, you will drive growth for brands on marketplaces by managing relationships, executing growth plans, and improving operations through data-driven strategies.

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.

Design and implement large-scale AI/ML systems using LLMs and RAG. Develop data pipelines, optimize models, and collaborate with teams on actionable intelligence solutions.

Build, test, and ship AI features and shared platform capabilities using LLMs and RAG. Integrate AI services with data sources via APIs, contribute to evaluation, observability, testing, and CI/CD, apply responsible-AI practices in a regulated bank, and support AI adoption across engineering teams.

Lead enterprise-wide AI transformation by defining strategy, operating model, governance, and roadmap. Build and run an AI Transformation Office, scale high-impact AI use cases, ensure responsible AI governance, drive change management and AI literacy, measure ROI and automation maturity, and align cross-functional teams to deliver measurable business value.

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 an ML Engineer Intern, you will build and deploy ML models, design scalable infrastructure, and collaborate with data science teams in a fast-paced environment.

As an ML Engineer, you'll build and deploy scalable ML systems, optimize models, and collaborate with teams to improve content across platforms.

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.

Build and maintain scalable ML infrastructure for LLM post-training, RL, evaluation, inference, and agentic workflows. Design data-control systems, optimize training and inference performance across networking, storage, and compute, and deliver tooling for experiments, evals, dashboards, reproducibility, and failure inspection. Collaborate with researchers and product to iterate models and infrastructure.

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.

As a Machine Learning Engineer, you will develop ML models, optimize solutions, automate workflows, and design ML architecture while ensuring high-quality production software delivery.

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.

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.