Top Machine Learning Jobs in London

Lead presales and solutions consulting for NiCE Actimize financial crime and compliance products. Engage prospects, gather requirements, present and demonstrate solutions, explain business value, lead proofs of concept, complete RFI/RFP responses, and collaborate with sales and product teams. Build client relationships, communicate technical differentiation, and participate in industry events. The role requires client-facing enterprise software experience, financial crime knowledge, and travel to customer sites 40% or more.

Leads project controls and performance consulting engagements for major capital projects and programmes. Responsibilities include assessing client environments, developing recommendations, facilitating workshops, implementing controls methodologies and digital solutions, preparing reports, supporting business development, contributing to marketing initiatives, and managing direct reports. The role requires project controls experience across schedule, cost, risk, change, or performance reporting, strong stakeholder engagement, analytical skills, and familiarity with PMO and capability maturity assessments.

Develop algorithms and production systems for world-scale geospatial registration, 3D geometry, correspondence, mapping, and learned 3D reasoning. Build datasets, pipelines, benchmarks, and evaluation methods using imagery, point clouds, LiDAR, GPS, and geographic data. Apply computer vision, machine learning, transformers, and geometric optimization to improve a living world map, while collaborating across research, engineering, and product teams and communicating results through technical documentation and presentations.

Own the strategic vision, architecture, and delivery lifecycle for Kaluza’s global billing and payments platform. Lead Product Managers and Business Analysts, collaborate with Engineering, Finance, Architecture, Commercial, and Pre-Sales teams, and represent the product to enterprise clients. Drive scalable, compliant solutions across geographies and regulatory environments while developing product leaders and shaping the wider product organization.

Build post-training and agent systems for public-sector and regulated environments. Develop private improvement loops, permissioned tool-using agents, high-assurance evaluations, and deployable model updates within strict data and infrastructure boundaries. Conduct experiments, inspect traces, establish meaningful baselines, attribute failures, and collaborate directly with customers and research partners. The role requires hands-on model-training or agent experimentation, strong Python skills, sound research judgment, and comfort solving ambiguous problems.

Conduct applied research on reinforcement learning for long-horizon, tool-using, multi-agent systems. Develop agentic judges, reward and credit-assignment methods, environments, evaluations, and scalable experimental systems. Diagnose reward hacking, data leakage, and infrastructure artifacts through rigorous experimentation. Build reusable open-source systems and publish findings while collaborating with customers, application teams, research partners, and open-source users.

Own the development of open-source reinforcement learning, evaluation, agent, and environment systems. Build researcher-facing platform features, stable APIs, reference implementations, and technical documentation. Collaborate with engineers, customers, research partners, and external contributors to turn experimental research into reliable products and community contributions. Conduct model-training and post-training experiments, inspect traces, establish meaningful baselines, attribute failures, and improve workflows for researchers building and deploying advanced AI systems.

Build continual learning systems that convert agent trajectories, corrections, and failures into training signals and model updates. Develop and evaluate streaming reinforcement learning, post-training, distillation, memory, and adaptation methods. Create reproducible task-generation, training, evaluation, regression, and rollback pipelines for changing task distributions. Conduct field experiments with external collaborators, assess retention and transfer, and establish promotion gates and human oversight for continuously improving production agents.

Leads global AI industry solutions for financial services, translating R&D advancements into sales strategies, reference architectures, prototypes, and customer outcomes. Partners with Sales, Product, Engineering, Marketing, and customers to support high-value deals, validate architectures, calculate ROI, and drive thought leadership. Builds production-grade AI solutions for use cases such as fraud detection, risk modeling, and generative AI compliance while representing the company at industry events.

Use AI, machine learning, and advanced analytics to identify emerging enterprise risks, assess risk exposure, develop risk indicators and executive dashboards, automate reporting, and provide forward-looking economic, geopolitical, market, and industry intelligence to senior leaders and the Board.

Leads AI enablement across platform engineering teams by owning reusable patterns, paved paths, reference implementations, governance controls, and AI delivery standards. Drives adoption of AI-first practices, Responsible AI pipeline controls, evaluation gates, and cost governance. Builds engineering capability through coaching and communities of practice, measures adoption, translates enterprise standards into practical workflows, and feeds delivery experience back into shared patterns and primitives.

Lead the quality engineering strategy for complex, data-intensive financial applications. Build enterprise automation frameworks, oversee UI, API, database, integration, and end-to-end testing, and integrate quality gates into CI/CD pipelines. Drive AI-assisted testing, data validation, cloud-native testing, governance, KPIs, and continuous improvement. Provide technical leadership, coaching, and cross-functional coordination while ensuring application reliability, testability, and release readiness.

Develops statistical, machine learning, and natural language processing models using consumer and market data to identify emerging risks and consumer harm. Builds scalable analytical solutions, automated workflows, dashboards, and reporting tools for regulatory decision-making. Validates explainable methodologies, supports data governance and compliance, and communicates insights and recommendations to technical and non-technical stakeholders.

Develop, test, deploy, and own full-stack software features for frontline unmanned-systems operators. Work across backend Python, frontend, embedded systems, networking, machine learning, and DevOps. Translate customer feedback into reliable production updates, conduct field testing, and improve operational performance while expanding into unfamiliar technical areas as needed.

Leads global sales and account growth across cruise and ferry markets, with approximately 75% focused on new enterprise customer acquisition and 25% on strategic account expansion and partnerships. Owns prospecting, complex sales cycles, negotiations, pipeline management, executive relationships, and recurring revenue growth. Partners cross-functionally to develop technically credible solutions, represents the company at industry events, and builds alliances across maritime connectivity and technology ecosystems.

Conduct quantitative research on market behavior and trading strategies. Responsibilities include discovering predictive patterns, designing statistical and machine-learning models, building AI research agents, developing large-scale research infrastructure, running experiments and backtests, and translating findings into production-ready trading signals. Interns complete an end-to-end research project while collaborating with researchers and technologists.

Conduct quantitative research on financial markets by discovering predictive patterns, developing statistical and machine learning models, running large-scale simulations and backtests, and translating research into production-ready trading signals. Interns collaborate with researchers and technologists, build research infrastructure and AI-powered agents, and complete rigorous projects from hypothesis through evaluation under realistic market conditions.

Conduct quantitative research for systematic trading by analyzing market data, formulating and testing hypotheses, developing statistical and machine learning models, backtesting strategies, studying market microstructure, and partnering with technologists to deploy production-ready research and trading systems.

Own the roadmap for fraud prevention and transaction monitoring, translating financial crime risks into detection rules, models, controls, and product requirements. Lead cross-functional delivery with Compliance, Data, Engineering, Operations, and investigations teams. Apply AI and machine learning to automate routine alert handling, improve detection, and reduce false positives. Evaluate data quality and control effectiveness, oversee safe releases, monitor outcomes, and adapt capabilities for emerging threats and new markets. Help establish a new capability and grow the supporting product team.

Lead a virtual pharmacology modeling program by evaluating biological datasets, building data pipelines and machine learning proof-of-concept models, establishing scientific validation criteria, and preparing models for internal and commercial use. Collaborate with laboratory scientists, senior leadership, IT, and AI teams. Ensure reproducible, documented code and models, identify data gaps, guide additional experiments, communicate results to diverse audiences, and potentially build a data science team.

Own and improve matching, recommendation, and taxonomy-mapping models for Beamery’s talent data platform. Responsibilities include building evaluation datasets and metrics, conducting scientific experiments, tracking technical and business performance, writing tested Python code, reviewing AI-generated code, researching LLM and recommender-system advances, and communicating findings to product, engineering, and business stakeholders.

Leads a London-based team building and scaling B2B agentic commerce systems and production ML models. Owns engineering delivery, team development, architecture, ML operations, security, model risk governance, platform integration, vendor evaluation, and production stability. Partners with product, platform, data, and business stakeholders while establishing responsible AI and AI-assisted engineering practices.

Leads data science initiatives from conception through deployment, developing statistical models and machine learning algorithms for trading intelligence. Collaborates with cross-functional teams to productionize solutions, supports data-driven trading strategies, and contributes to trading automation design, development, maintenance, and testing. Communicates insights to stakeholders, mentors junior data scientists, and helps solve complex business problems using large datasets.

Own the roadmap and requirements for GRC analytics products, including datasets, reports, dashboards, embedded analytics, and personalization. Collaborate with customers, sales, product, engineering, data, and QA teams to deliver analytics capabilities using Power BI and Microsoft Fabric. Apply data visualization, SQL, data architecture, AI, and machine learning concepts to create actionable insights. Guide agile development, customer feedback integration, master data initiatives, and analytics best practices.

Build statistical and machine learning models for insurance pricing, risk-cost prediction, and profitability analysis across motor insurance products. Conduct data analysis, develop segmentations, monitor performance, improve pricing models, and implement innovative pricing solutions. Present technical insights to senior stakeholders and contribute to the pricing roadmap.