Top Machine Learning Jobs in London

As a Data Analytics Principal Consultant, you'll lead data transformation initiatives, mentor consultants, and engage with stakeholders in financial services to drive data literacy and innovation.

Build and deploy production-grade AI solutions, including LLM agents, multi-agent workflows, RAG systems, classical ML, and speech models. Develop reliable, secure, observable, and scalable AI platforms using strong software engineering and MLOps practices. Own solutions from client scoping through monitoring and continuous improvement, collaborate with technical and business stakeholders, mentor engineers, and drive innovation in enterprise AI.

Build governed data assets, reliable pipelines, lakehouse models, and analytical products using Microsoft Fabric and Databricks. Conduct exploratory analysis and develop validated statistical and machine-learning models for classification, ranking, recommendation, forecasting, anomaly detection, NLP, entity resolution, and information extraction. Implement data quality, lineage, monitoring, testing, documentation, and responsible AI practices while supporting legal-services R&D, client intelligence, opportunity identification, pricing, and matter analysis.

Prepare daily P&L and market risk reports for oil, refined products, and biofuels trading; monitor limits, identify significant risks, and support senior management. Develop forward curve methodologies, assess new products, review Endur changes, and support month-end activities. Collaborate with traders, front office, back office, and risk committees while mentoring junior team members and contributing to automation and AI-enabled trading tools.

Lead technical pre-sales engagements for Databricks, demoing Spark and data/AI solutions, building architectures, authoring reference materials, and collaborating with account teams to design and deliver customised proofs-of-concept and workshops.

Build data pipelines, integrations, and infrastructure for AI research workflows. Work directly with frontier AI labs and enterprise teams to clean and prepare datasets, automate experiments, support model-in-the-loop and human-in-the-loop quality control, and translate ambiguous research needs into scalable production systems for model training, evaluation, safety, interpretability, and alignment.

Lead development of spatial intelligence for humanoid robots, including state estimation, mapping, localization, 3D scene understanding, spatial memory, and semantic navigation. Train and deploy large multimodal models and integrate them into low-latency robot systems running on edge hardware. Drive perception research from model evaluation and dataset benchmarking through reliable production implementation, collaborating across engineering and research teams.

Own the AI and Automation product strategy, roadmap, discovery, delivery, adoption, and commercial outcomes across the insurance customer journey. Identify and prioritize AI use cases, translate business needs into product requirements, manage cross-functional delivery, build opportunity pipelines, support go-to-market efforts, and ensure responsible AI governance. The role also drives organizational adoption through training, playbooks, stakeholder engagement, and measurable value realization.

Own the AI and Automation product strategy, roadmap, discovery, delivery, adoption, and value realization across the insurance customer journey. Identify and prioritize use cases, translate business needs into product requirements, lead complex transformation programs, build commercial opportunity pipelines, and partner with Engineering, Compliance, Legal, Risk, market leaders, and branch teams. Drive responsible AI adoption through training, governance, practical propositions, and measurable operational and commercial outcomes.

Lead architecture and technical strategy for Ripple Treasury’s liquidity platform. Build scalable cloud-native systems, real-time cash and liquidity data pipelines, digital asset capabilities, and AI-powered cash-flow prediction features. Mentor engineers, establish engineering standards, improve reliability and observability, manage technical debt, lead design reviews, and own critical systems from development through production support. The role requires deep C#/.NET expertise, database knowledge, large-scale enterprise application experience, and proficiency with modern cloud, frontend, and API technologies.

Designs, builds, and ships production machine learning systems for real-time ranking, personalization, reward optimization, behavioral prediction, and measurement. Owns pipelines, training, serving, monitoring, experimentation, causal inference, and uplift modeling at large scale. Provides technical leadership and mentorship while partnering with engineering, product, and data science teams.

Build and evolve 9fin’s AI platform from the ground up, supporting generative AI, agentic AI, RAG, model serving, embeddings, vector search, and intelligent automation. Develop APIs, SDKs, orchestration layers, deployment pipelines, observability, evaluation, and governance capabilities. Partner with AI and backend engineers to establish scalable architecture, developer tooling, operational standards, and reliable production AI services.

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

Design and build generative AI applications for complex financial and legal workflows. Lead the full model lifecycle from experimentation and training through testing, monitoring, and deployment. Apply advanced techniques across information retrieval, knowledge graphs, recommendation systems, statistical modeling, and GenAI. Collaborate cross-functionally, translate business problems into scoped initiatives, mentor teammates, promote AI adoption, and deliver production-ready solutions with strong product impact.

Build production generative AI systems for complex financial and legal workflows. Responsibilities include document understanding, structured information extraction, model lifecycle management, retrieval and context engineering, data validation, evaluation, deployment, and monitoring. The role requires collaboration across teams, mentoring, product focus, and applying advanced AI research to convert legal and financial documents into reliable structured data.

Lead delivery of advanced analytics and data science for global clients, develop scalable AI-enabled analytics products (prediction engines, experimentation, MMM), embed analytics into client decision-making, and build capability across teams through playbooks and thought leadership.

Lead delivery of advanced analytics and AI solutions for global clients, design scalable analytics products, industrialise prototypes to production, embed measurement and experimentation, drive adoption across markets, and build analytics capability and thought leadership within the organisation.

Leads and scales Databricks’ EMEA Forward Deployed AI Engineering team, overseeing hiring, mentoring, customer relationships, strategic AI professional services engagements, delivery processes, account outcomes, and cross-functional collaboration with Sales, Product, Research, Engineering, and GTM teams. The role guides enterprise customers through AI/ML transformations, develops scalable engagement models, manages executive stakeholders, and drives production ML and GenAI solutions in cloud environments.

Build and own production software across a Node.js/TypeScript backend, React frontend, C#/WPF desktop connector, and Python ML/RAG workers on Azure. Design the entity-resolution and knowledge-graph architecture, evolve APIs and service contracts, improve retrieval quality, and develop automated tests and deployment practices. The role involves direct customer interaction, technical ownership, pragmatic architecture decisions, and close collaboration with founders and structural engineering customers.

Lead architecture and hands-on implementation of scalable decision automation and production ML systems for Candy Crush Saga. Build reusable decisioning foundations, improve experimentation and causal inference tooling, mentor senior engineers, and ensure reliability, observability, and cross-team alignment for player-facing personalization and adaptation at scale.

Develop and deploy signal processing algorithms for radar-based contactless monitoring systems. Responsibilities include extracting clinical features from sensor data, validating algorithms using clinical studies, documenting work for regulatory and intellectual property needs, and implementing algorithms on embedded devices. The role collaborates with product, clinical research, data science, electronics, firmware, and algorithms teams to move innovations from research into shipped healthcare products.

Partner with UK and European financial-services clients to design, deploy, and operationalize AI risk controls, evaluations, and guardrails. Translate regulatory and risk expectations into testable controls, operating models, evidence standards, and triage workflows. Lead client workshops, support audit readiness, monitor production guardrail performance, and collaborate with product, engineering, and deployment teams to improve AI governance capabilities.

Design and validate threat models for agentic AI systems, create adversarial experiments and synthetic environments, develop static and dynamic risk analysis, fine-tune and evaluate models, and build production-ready guardrails, monitoring, dashboards, and evaluation infrastructure. Optimize security systems for latency, cost, reliability, and measurable real-world performance while translating research into product capabilities.

Lead the technical strategy, design, and production integration of fault detection and fallback systems for autonomous vehicles. Develop robust robotics solutions, collaborate with machine learning and software teams, guide architecture decisions, and improve overall system safety and reliability. The role also involves mentoring, defining acceptance criteria, analyzing fleet data, and potentially building monitoring, debugging, and real-time systems for large-scale driverless vehicle operations.

Develop and improve InstaLILY’s core AI platform, collaborating with product, design, UI/UX, and business teams to deliver reliable customer-focused features. The role includes writing software, testing, supporting CI/CD practices, maintaining technical quality, and staying current with AI and software engineering technologies. Candidates should have 0–2 years of software engineering experience, programming experience in Python and JavaScript, and familiarity with cloud computing or AWS, Azure, or GCP.