Top Machine Learning Jobs in London

Early-career technology consulting fellowship for recent or upcoming STEM doctoral graduates. Fellows apply research, analysis, innovation, AI, automation, and structured problem-solving to technology strategy, digital transformation, cloud modernization, cybersecurity, fintech ventures, and proofs of concept for financial services clients. The role involves collaborating with technical and business stakeholders, using responsible AI practices, and traveling to client sites when required.

Leads strategic enterprise Cloud and AI sales opportunities from qualification through commercial commitment, execution readiness, transition, and early value realization. Builds executive relationships, shapes transformation strategies and architectures, develops value cases and Azure consumption plans, and coordinates Microsoft specialists, engineering, delivery, finance, partners, and deal teams. Advises customers on AI, cloud modernization, security, governance, reliability, FinOps, and enterprise operating models while supporting strategic Azure commitments and coaching peers.

Lead FactSet’s global privacy program and serve as senior legal counsel on technology, AI, data protection, cross-border transfers, privacy risks, and cybersecurity incidents. Advise product, engineering, compliance, and security leaders on GDPR, CCPA, AI governance, data licensing, and privacy-by-design. Manage regulatory engagement, privacy training, data subject rights, impact assessments, data mapping, and contract terms across multiple jurisdictions.

Lead the technology strategy and engineering organization for RLDatix’s Quality Compliance Systems platform. Own architecture, Azure infrastructure, software delivery, data, AI, security, and platform capabilities. Drive scalable SaaS modernization, reliable CI/CD, AI-assisted development, technical risk reduction, and secure-by-design practices while developing multidisciplinary engineering leaders and teams.

Assists with data exploration, mining, cleaning, validation, transformation, and predictive or prescriptive modeling. Supports model deployment, evaluation, and production monitoring while collaborating with technology and operations teams. Contributes to team standards, tooling, and best practices, and communicates data science approaches and results to non-technical stakeholders.

Researches AI and emerging technologies for socioeconomic development, financial inclusion, and climate resilience in low- and middle-income countries. Defines research methodologies, analyzes technical and commercial architectures, produces reports and thought leadership, advises innovators, supports Innovation Fund grantees, and engages donors, operators, startups, governments, NGOs, and multilateral organizations. Translates technical findings into actionable investment, partnership, and capability-building recommendations.

Develop and deploy machine learning models for retrieval, reranking, indexing, search relevance, crawling, and content understanding. Build large-scale embedding and retrieval systems, evaluation pipelines, and quality metrics for agent-native search. Integrate models into high-throughput production services while balancing latency, quality, and cost. Collaborate on architecture and product decisions involving search, retrieval, and LLM-based systems.

Lead design and delivery of production-grade ML and GenAI solutions on the Databricks platform. Architect end-to-end ML pipelines, optimize training/inference, implement MLOps/LLMOps, build MVPs for sales, influence product roadmap, mentor peers, and present technical content externally.

Implement and refine multimodal computer vision and perception algorithms for autonomous satellites and drones. Build sensor-fusion pipelines, semantic scene understanding, SLAM, pose and depth estimation, and neural world models. Deploy high-performance, production-quality C++ and Python code on embedded and edge-AI hardware for flight processors, HIL/SIL environments, and real aerospace and defense missions.

Drive business development and strategic engagement across UK defence and national security. Build relationships with government, defence, industry, and technology stakeholders; originate and close complex contracts; develop partnerships; manage opportunities from qualification through deployment; lead proposals, pricing, and negotiations; conduct customer discovery; and translate market intelligence into product roadmap and go-to-market recommendations.

Build and own backend services for iwoca’s Customer Operations Platform. Responsibilities include designing distributed systems, automating operational workflows with AI and ML, migrating monolith functionality into services, developing communication tools, translating operational needs into technical architecture, and collaborating with operations and engineering stakeholders. The role also involves technical scoping, prototyping, documentation, code review, and production ownership.

Lead a team of four backend and ML engineers building machine-learning-driven ad delivery. Own the ML roadmap, production model-serving stack, real-time decisioning, feature pipelines, experimentation, monitoring, and backend ad services across web and mobile. Partner with product, analytics, and client engineering teams to balance revenue, retention, and inventory. Contribute hands-on to the codebase while mentoring engineers, managing performance, and guiding technical strategy.

Own the strategy and roadmap for financial crime products covering onboarding, screening, monitoring, fraud prevention, and case management. Lead Product Owners, prioritize initiatives with Compliance, Operations, and Engineering, and translate regulatory controls into product requirements. Oversee build-versus-buy decisions, vendor performance, automation and machine learning deployments, product launches across markets, and post-launch performance measurement.

Lead and grow an engineering team building reliable, scalable, secure, privacy-first AI-powered health products. Set technical direction, guide architecture, unblock delivery, champion engineering quality, and collaborate across disciplines. Hire, coach, develop, and promote engineers while delivering products to a global audience. The role requires substantial coding experience in Scala, Java, Kotlin, or Objective-C/Swift, distributed systems expertise, and several years managing engineering teams.

Leads and develops a distributed EMEA team of Applied AI Architects, setting pre-sales standards and supporting strategic digital-native customers. Responsibilities include AI adoption strategy, use-case scoping, proof-of-concepts, technical demonstrations, ROI validation, API and LLM architecture, regulatory and procurement guidance, cross-functional alignment, and executive customer engagement. The role also represents Anthropic in the European technology community and requires regular EMEA travel.

Own the strategy, roadmap, and business outcomes for a critical B2B SaaS product. Partner with engineering, design, sales, marketing, customer success, executives, enterprise customers, and prospects. Shape UX and technical decisions involving AI, ML, backend systems, and data architecture. Drive adoption, retention, revenue, competitive positioning, product rigor, and team development while mentoring product managers.

Deliver AI and productivity workshops, drop-in sessions, and technical learning content for apprentices. Facilitate engaging online sessions, moderate assignments, provide developmental feedback, coordinate learner interventions, and collaborate with coaches and operations teams. Apply AI in business contexts to explain technical concepts, support learner progress, and demonstrate how AI creates measurable business value. Maintain current industry knowledge and contribute to an inclusive, motivating learning environment.

Conduct research and develop algorithms for 3D reconstruction, computer vision, spatial AI, neural scene representations, Gaussian splatting, and generative models. Publish at leading conferences, build large-scale experiments, prototype rapidly, and transition research into customer-facing production systems. Collaborate with researchers and engineers across computer vision, machine learning, graphics, robotics, and geospatial modeling. The role requires strong Python and PyTorch skills, a 3D vision research record, top-tier publications, and evidence of shipping research-driven products.

Own product design from discovery through launch across CreativeX product areas. Conduct customer research and usability testing, translate creative data and AI capabilities into intuitive workflows and dashboards, collaborate with Product and Engineering, extend the design system, and improve shipped experiences using feedback and product data. The role requires strong interaction and visual design, systems thinking, and independent ownership in a collaborative B2B SaaS environment.

Lead bioinformatics strategy and execution across Discovery client engagements. Design and implement computational multi-omics workflows, translate biological questions into analytical frameworks, deliver client-ready insights, mentor junior staff, support business development, and help grow internal bioinformatics capabilities and thought leadership.

Build and productionize quantitative trading tools, energy-market models, analytics, data pipelines, and visualization tools. Collaborate with traders, quants, and engineers to develop signal research, backtesting, risk and pricing integrations, and time-series data infrastructure. Monitor, test, document, and iteratively improve production models and tools based on trader feedback.

Lead end-to-end advanced analytics and data science projects for commercial and medical decision-making within AstraZeneca UK. Develop Python-based models, apply statistical and causal inference methods, validate analytical outputs, establish methodological standards, and communicate actionable insights to senior stakeholders. Partner across Sales, Marketing, Medical, Market Access, Data, and Technology while advancing machine learning, AI-enabled analytics, promotional mix modelling, and Commercial Digital Twin capabilities.

Deploys, monitors, and maintains machine learning models in production. The role improves MLOps frameworks, manages operational risks, establishes reliable deployment workflows, and coordinates data science, data engineering, software engineering, product, and marketing technology teams.

Lead AI Consultant responsible for translating client needs into scalable AI and machine learning solutions. Leads requirements gathering, architecture, implementation, deployment, monitoring, and operationalization of ML and generative AI solutions using Microsoft Azure services. Advises clients on AI best practices, explores and integrates data, leads delivery teams, ensures security and CI/CD standards, and presents recommendations to stakeholders.

Conduct research on chemical and biological risks associated with frontier AI systems. Responsibilities may include designing evaluations, benchmarks, datasets, threat models, safeguards, monitoring tools, and capability assessments; studying model generalization; and translating technical findings into recommendations for AI companies and government stakeholders.