Top Machine Learning Jobs in London

Own the roadmap for acquiring fraud and risk monitoring. Build real-time rules, ML models and Agentic AI to reduce fraud and chargebacks, improve chargeback handling and recovery, define metrics, run experiments, and lead cross-functional delivery across Engineering, Data Science, Risk, Operations and Finance.

Define and lead AI and data science strategy across ML, NLP, search, recommendation, experimentation, and generative AI. Lead and develop teams to deliver production-ready AI systems (LLMs, RAG, semantic search, embeddings), establish evaluation and responsible AI frameworks, and influence cross-functional stakeholders to align roadmaps and drive measurable product and business outcomes.

Lead corporate actuarial activities including business planning, performance monitoring, and portfolio optimisation. Provide actuarial insight for pricing, reserving and strategy, develop tools and frameworks, collaborate with underwriting, risk and finance, ensure governance and compliance, and manage and develop a team.

Own the real-time fraud decisioning platform and complete machine learning lifecycle, including feature infrastructure, model serving, feedback loops, fallback paths, monitoring, replay, shadow testing, staged rollouts, and model retirement. Lead technical direction through ambiguity, establish engineering standards, scale high-availability services under strict latency budgets, and ensure alignment between training and production serving.

Design, build, and deploy intelligent AI agents for e-commerce and logistics. Develop agent architectures using ML, NLP, reinforcement learning, and multi-agent techniques; integrate with JD systems; run experiments, monitor performance, and collaborate with cross-functional teams to deliver scalable, production-ready solutions.

Lead ownership of ML models and infrastructure lifecycle: develop, deploy, monitor, and improve ML systems. Build CI/CD for software and ML workflows, automate orchestration and environment management, ensure scalability, reliability, observability, and security, troubleshoot production issues, and document operational procedures while collaborating with engineers, data scientists, and product teams.

As a Research Intern at Atla, you'll conduct machine learning research, contribute to product development, and output publications and datasets while collaborating with engineers on projects involving iterative model improvement and evaluation systems.

The role involves developing new AI model architectures and adapting existing models, conducting experiments, and contributing to technical strategy in a high-growth research environment.

The Reserve Engineer provides technical support for robotic hardware, resolves hardware failures, and assists with robot deployments while ensuring excellent customer service.

The Managing Consultant will develop AI-driven transformation strategies for financial services, overseeing client engagements and business development while leveraging industry connections and expertise.

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.

Design, build and deploy end-to-end ML pipelines for UK property transaction data. Own model lifecycle (evaluation, deployment, monitoring, retraining), establish MLOps standards, collaborate with product and engineering, ensure data quality for features, and drive ML-driven product opportunities.

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.

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.

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

The Senior Machine Learning Engineer will develop production-ready trading models, design tooling and frameworks, and optimize performance using Python and modern ML libraries.

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.

Senior individual-contributor responsible for net and gross revenue retention across enterprise accounts, driving renewals and expansions with compliance/AML stakeholders, and acquiring one strategic channel partner end-to-end (market mapping, negotiation, onboarding). Acts as customer voice to product and leads cross-functional account strategy, risk mitigation, executive reviews, and partner enablement.

Lead global transformation of Citi's Non-Financial Regulatory Reporting by defining strategy, delivering an AI-enabled Regulatory Intelligence Platform, executing a unified Target Operating Model, driving automation and data quality, managing global teams, and ensuring robust governance, risk controls, and senior stakeholder engagement.

Work with quantitative researchers and portfolio managers to build, maintain and productionise quantitative trading frameworks and strategies, develop tooling and ML pipelines, improve engineering standards, and deliver robust production solutions for systematic trading.

Design, develop, test, deploy and maintain search and recommendation systems. Translate business needs into technical requirements, optimize code, coach engineers, manage priorities, and improve processes. Work onsite in London to drive product impact for Joybuy.

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.

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.