Top Machine Learning Jobs in London

Design, prototype, and evaluate machine learning models for travel products under experienced scientists’ guidance. Build maintainable training and inference code, conduct experiments and A/B tests, analyze and prepare data, perform feature engineering and error analysis, and contribute to ML system, API, and data design. Collaborate with scientists, engineers, product partners, and mentors to define metrics and develop robust, fair, production-aware AI solutions.

Leads Airwallex’s global sanctions compliance strategy, second-line governance, screening oversight, regulatory engagement, and technology modernization. The role manages complex sanctions escalations, regulatory examinations, disclosures, geopolitical risk monitoring, and AI-enabled screening initiatives across global payment operations. It also reports programme risk to executives and the board while scaling a cross-regional sanctions team.

Lead Perk’s 40-plus-person data organization and define its company-wide data strategy, infrastructure roadmap, governance, privacy, and AI adoption. Build data into core product infrastructure, improve quality and scalability, shift the organization from reactive reporting to strategic insight, and develop senior leaders. Partner with Product, Engineering, Finance, Marketing, Revenue, Customer Care, and People leadership while owning the data budget, organizational design, and executive-level outcomes.

Build and operate scalable full-stack systems that protect customer accounts from takeovers, scams, and financial crime. Partner with Product, Design, Data Science, and Investigations teams to develop customer-facing controls, improve fraud detection, and increase automation. Responsibilities include Java and React development, distributed system design, database optimization, CI/CD, testing, independent project ownership, and mentoring junior engineers.

Build and operate backend and frontend systems that prevent account takeovers and financial crime. Own projects end to end, develop scalable detection controls, improve automation, optimize data systems, and collaborate with Product, Data Science, and Investigations teams. The role is approximately 80% backend and 20% frontend, with a strong focus on customer security, product impact, system reliability, and data-driven decision-making.

Design and build scalable AML detection controls for money laundering and terrorist financing risks. Own major projects and technical roadmaps, influence product decisions, improve reliability, security, and technical quality, and mentor engineers. Work with Java, Spring Boot, Kafka, distributed services, asynchronous processing, and large-scale data alongside product, data science, analytics, and AML specialists.

Lead strategic and operational data science through quantitative analysis, forecasting, experimentation, and data storytelling. Mentor data scientists, advise cross-functional leaders, define metrics and forecasts, and build scalable decision-support systems. The role also contributes to AI and machine-learning systems that optimize product behavior, pricing, risk, allocation, and customer experiences, with an emphasis on rigorous validation, governance, and measurable outcomes.

Conduct research on AI compute, machine learning algorithms, large language models, efficient inference, numerics, world models, molecule generation, or model safety. Implement models and algorithms in Python or C++, design and analyze experiments using frameworks such as PyTorch, and communicate findings through technical presentations or research papers under experienced mentorship.

Data Science Interns will complete a real-world data science project using machine learning techniques, develop customer-impacting solutions, prioritize product improvements, and experiment with advanced algorithms. The role includes mentorship, intern learning sessions, and collaboration within a global cohort. Candidates must be graduating in 2028, available for a 10-week summer internship, understand computer science and machine learning fundamentals, use Python and SQL, and be eligible to work in London without visa sponsorship.

Support data science projects that improve Wise products and customer experiences. Develop machine learning solutions, experiment with advanced algorithms, identify high-impact opportunities, and collaborate with autonomous teams across areas such as marketing, treasury, and fraud. The role includes learning Wise’s domain and technology stack, contributing ideas during planning cycles, and applying data science methods to real-world problems.

Owns the product lifecycle for AI and autonomous-agent solutions, translating business needs into production-grade workflows. Responsibilities include defining requirements and roadmaps, managing delivery and backlogs, building integrations and infrastructure, evaluating model performance, monitoring drift, gathering user feedback, measuring impact, and ensuring governance, compliance, safety, and reliability in a regulated banking environment.

Develop AML risk detection systems using supervised, unsupervised, semi-supervised, and generative AI techniques. Build and evaluate machine learning controls, optimize precision-recall trade-offs, create investigator insights, automate data labeling and retraining pipelines, deploy production-grade Python services, and collaborate cross-functionally with operational teams. The role also includes mentoring junior colleagues and solving financial crime problems at global scale.

Own the strategy, roadmap, governance, evaluation, and production performance of a portfolio of agentic AI products. Lead cross-functional teams across engineering, data science, design, and controls; establish standards for agent behavior, safety, reliability, monitoring, and reuse; prioritize investments; manage stakeholder relationships; and drive adoption and measurable business outcomes across a regulated financial institution.

Lead the design, development, deployment, and continuous improvement of AI/ML solutions for enterprise customers. Build integrations using Python or JavaScript, APIs, microservices, and enterprise systems; analyze data; automate workflows; address AI risk, privacy, and compliance; and align technical solutions with business goals. Mentor engineers and communicate effectively with technical and non-technical stakeholders in complex, ambiguous environments.

Own the technical roadmap and delivery of large-scale backend systems for Wise’s global KYC and onboarding platform. Design scalable microservices, APIs, asynchronous integrations, and database architectures while improving reliability, security, compliance, and automation. Lead complex cross-team initiatives, solve ambiguous problems, mentor engineers, and deliver customer-focused verification solutions.

Develop and support credit risk and pricing models for lending products. Responsibilities include data exploration, feature engineering, statistical analysis, model monitoring, deployment support, documentation, and collaboration with Credit Strategy, Product, Engineering, and Data Engineering teams. The role is designed for an early-career data scientist with strong quantitative skills and an interest in credit and financial services.

Lead and grow Teya’s data science function within Credit & Analytics. Own credit risk, pricing, and customer value models from problem definition through deployment, monitoring, and governance. Manage and mentor data scientists, establish the modelling roadmap, ensure statistical and regulatory rigor, and partner with Credit Strategy, Product, Finance, Engineering, and Data Engineering. Present insights to senior stakeholders and connect modelling decisions to credit performance, pricing, and commercial strategy.

Advances Mastercard’s AI and data strategy through research, strategic analysis, responsible AI governance, stakeholder engagement, and adoption initiatives. Develops governance frameworks, evaluates emerging technologies and risks, supports AI and analytics integration, promotes data literacy, contributes to thought leadership, and mentors junior team members. Collaborates with product, engineering, legal, compliance, privacy, security, risk, and business stakeholders to promote responsible innovation.

Leads the Verification Data Science team within KYC and onboarding. Owns the data science roadmap, guides machine learning projects, manages image and tabular datasets, develops deep learning document-verification models, optimizes production performance, maintains Python code, researches fraud prevention, and coaches and hires data scientists.

Leads the Content AI organization, managing and developing data scientists and machine learning engineers. Oversees machine learning solutions for content generation, moderation, quality, and relevance across text, video, and images. Sets strategy, technical priorities, and team practices while partnering with Product, Engineering, and leadership. Guides production-ready, scalable AI systems, stakeholder communication, delivery optimization, recruiting, performance management, and career development.

Leads software engineering development teams building AI-centric SaaS products. Responsibilities include technical oversight of the software development lifecycle, strategic planning, workforce planning, recruitment, budgeting, performance management, coaching, career development, innovation, process improvement, quality, and compliance. The manager also supports cloud and data engineering, testing, Agile delivery, risk mitigation, and cross-functional collaboration while managing team objectives and individual performance.

Build high-performance tools and large-data research platforms to enable trading strategies. Work in small engineering teams to design systems, develop platforms and web frameworks, and apply techniques such as distributed computing, NLP, and machine learning to solve complex problems.

Designs and deploys AI, LLM, generative AI, and machine learning solutions for credit trading workflows. Responsibilities include generating trading signals, optimizing portfolios, identifying credit exposures and hedging strategies, automating risk and position reporting, and developing trade-support infrastructure. The role collaborates with model control teams and global quantitative research groups to support compliance, portfolio management, and digital transformation.

Leads enterprise and portfolio architecture for Commercial Banking and international technology domains. Defines target-state architecture, modernization and AI strategies, governance standards, technology roadmaps, resiliency and risk controls, and investment priorities. Advises CIOs and senior stakeholders, manages engineering leaders, drives cross-functional initiatives, develops talent, and oversees secure, scalable technology solutions. The role requires substantial executive influence, architecture expertise, regulatory awareness, and experience governing enterprise AI solutions.

Leads complex technology programs and data platform initiatives, managing project plans, CI/CD delivery, risks, budgets, resources, governance, and executive reporting. Partners with engineering, product, architecture, data science, and business stakeholders to deliver scalable, secure, resilient platforms. Applies analytical reasoning, agile practices, AI-assisted program governance, and data-driven optimization within a regulated financial services environment.