Top Machine Learning Jobs in London

Drive new commercial customer acquisition across the UK and Ireland by building outbound pipeline, managing complex sales cycles, advising executive stakeholders, negotiating and closing deals, and expanding existing accounts. The role requires consultative value selling of Zendesk’s AI-powered platform, collaboration with cross-functional teams, and strategic use of sales and customer data.

Develop and optimize state-of-the-art machine learning models for specialized AI hardware and systems containing thousands of accelerators. Responsibilities include model training, benchmarking, profiling, kernel optimization, experimentation, software testing, code reviews, and collaboration with research, software, and product teams. The role focuses on performance, scalability, distributed training and inference, and advancing next-generation AI hardware and software.

Lead retention and revenue growth for a portfolio of UK and Western EMEA accounts. Build high-touch client relationships, run multi-quarter growth plans, surface performance insights, launch and optimize campaigns, drive upsell/cross-sell, coordinate cross-functional teams, maintain forecasting and reporting, and represent RZR in strategic sessions and in-person client engagements.

Support subsurface technology R&D by combining reservoir engineering, Python, data science, machine learning, AI, analytics, and high-performance computing. Develop and deploy code to automate and parallelize subsurface workflows, improve reservoir uncertainty characterization, support regional teams, and provide technical assistance for deployed solutions. The internship is full-time, hybrid, and based in Sunbury, UK.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Leads the global Data Science Center of Excellence, overseeing statistical modeling, econometrics, machine learning, AI, forecasting, attribution, experimentation, and optimization. Builds technical capability, mentors teams, establishes quality and governance standards, owns advanced analytics products and services, and drives innovation. Partners with technology, consulting, and client teams to translate complex data science into scalable solutions, commercial value, and measurable media and marketing outcomes.

Lead real-world evidence biostatistical analyses using large-scale healthcare data to support clinical development, regulatory strategy, HEOR, and payer decisions. Design observational studies and target trial emulations, apply causal inference, statistical learning, machine learning, and NLP, and build reproducible Python/R workflows. Collaborate with clinical, epidemiology, data engineering, and HEOR teams while translating findings for technical and non-technical stakeholders. Support regulatory submissions, governance, audits, and scientific dissemination.

Lead the design, development, deployment, and operation of production-grade agentic AI systems supporting insurance underwriting and portfolio management. Build evaluation, observability, testing, tracing, fallback, and reliability capabilities. Partner with underwriters and portfolio managers to translate commercial needs into product and technical decisions. The role requires strong software architecture, cloud deployment, AI framework, governance, and operational expertise, with ownership from initial design through production delivery.

Leads a team of Data Scientists and delivers machine learning and statistical modelling solutions across the insurance business. Owns projects from exploration and experimentation through production deployment, monitoring and optimisation. Partners with stakeholders, Data Engineering and Technology teams to identify commercial opportunities, build scalable models, evaluate data sources, ensure governance, and translate technical findings into actionable business insights.

Leads economists, econometricians, and data analysts delivering applied economic, econometric, machine learning, and policy analysis for communications regulation. Responsibilities include directing concurrent projects, evaluating policies, shaping regulatory decisions, communicating findings to stakeholders, producing publications, and mentoring colleagues. The role requires extensive real-world economic analysis experience, strong applied microeconomics and econometrics expertise, analytical software proficiency, and the ability to lead teams and develop innovative quantitative approaches.

Leads a team of Data Scientists and shapes data science strategy across the insurance business. Develops and deploys machine learning, statistical, and predictive models across the full lifecycle, including experimentation, production deployment, monitoring, and optimization. Partners with engineering, technology, and senior stakeholders to identify commercial opportunities, evaluate data sources, govern models, and translate technical outputs into actionable business insights.

Principal Architect advising cross-functional teams on cloud-based infrastructure architecture, technology selection, strategic roadmaps, migrations, and platform adoption. The role translates client strategies into infrastructure requirements, develops AI-enabled engineering and governance patterns, and ensures solutions meet resiliency, security, and auditability standards. It also requires client-facing consultation, product ownership experience, advanced software architecture expertise, and communication with senior executives.

Develop and productionize scalable machine learning systems for content understanding, search, ranking, recommendations, moderation, computer vision, and generative AI. Responsibilities include data preparation, feature engineering, model training, evaluation, deployment, monitoring, optimization, batch and real-time pipelines, RAG and LLM applications, system design, observability, and cross-functional technical leadership.

Build scalable LLM-powered investment tools and AI workflows, including RAG, context management, data pipelines, model integrations, evaluation, and observability. Partner with investment and research teams to translate financial processes into secure enterprise solutions. Apply machine learning, statistical methods, Python, SQL, and financial-market knowledge in a regulated environment while optimizing AI quality, cost, latency, reliability, and business impact.

Manage strategic Central Government client relationships, identify and shape new business opportunities, lead bids, coordinate multidisciplinary teams, and develop value propositions. The role requires market intelligence, stakeholder engagement, and collaboration with technology, delivery, resourcing, and talent teams. Regular customer meetings across the UK and Bristol office attendance are expected. Candidates must be British passport holders who have lived permanently in the UK for five years and can obtain security clearance.

Leads hands-on data science and advanced analytics for drug development using clinical trial, real-world, omics, imaging, and biomarker data. Develops predictive models, machine learning and statistical analyses, reproducible pipelines, data quality frameworks, and scalable cloud-based workflows. Collaborates with statisticians, clinicians, translational scientists, engineers, and data teams to support clinical decisions, biomarker discovery, precision medicine, and innovative trial strategies. Provides technical mentorship, communicates findings, and coordinates multiple concurrent projects.

Leads Finance Systems Engineering teams delivering enterprise planning, forecasting, custom Finance applications, SaaS solutions, and SAP integrations. Owns technology strategy, multi-year roadmaps, platform lifecycle, operational performance, vendor partnerships, and organizational development. Partners with Finance, Data Engineering, and ML Engineering to apply data, AI, and machine learning to forecasting, automation, analysis, and decision-making while ensuring security, governance, scalability, and reliability.

Manage strategic enterprise publisher partnerships across EMEA, owning account growth, monetization, revenue targets, and multi-quarter plans. Build relationships with premium app, web, video, and CTV publishers; identify opportunities and manage risks. Collaborate with Product, Demand, Marketplace, Sales, Business Development, and Engineering teams to align supply and demand strategies. Represent InMobi at industry events, enable partners and sales teams, provide market feedback, and coach peers.

Supports data science and AI consulting projects by cleaning and exploring datasets, developing and evaluating statistical and machine learning models, prototyping language-model solutions, preparing visualizations and findings, and communicating results to technical and nontechnical audiences. The role emphasizes reproducible Python code, documentation, collaboration, and learning through client-focused projects.

Develop data science and AI solutions for government and public-sector clients. Responsibilities include defining analytical problems with subject matter experts, preparing data, building and evaluating statistical, forecasting, machine learning, and language-model prototypes, presenting findings, creating reproducible code, contributing to proposals, and collaborating across technical teams.

Join a selective quarterly cohort applying advanced technical skills to real-world client challenges in AI. Fellows work with experienced engineers and consultants on complex problems, receive mentorship, develop consulting and commercial capabilities, and build careers in artificial intelligence. Candidates should have recently completed a Master's or PhD in a STEM discipline and demonstrate strong coding, research, modelling, computational, or substantial technical project experience.

Leads enterprise data strategy, analytics, business intelligence, data science, experimentation, and game economy optimization. Builds trusted data platforms, governance, metrics, and decision frameworks while driving predictive analytics and responsible AI. Oversees monetization, segmentation, pricing, and portfolio-wide game economy standards to improve player engagement, retention, lifetime value, and commercial performance. Manages distributed technical and commercial teams and partners with Product and Live Operations leadership.

As a Principal Engineer at M&S, you'll drive technical strategy, influence architecture, and mentor engineers while delivering on personalization, search, and machine learning features in collaboration with cross-functional teams.

Build and own pricing data science capabilities, including demand and elasticity models, forecasting, willingness-to-pay models, pricing optimization, experimentation, and causal analysis. Deploy and monitor machine learning systems, establish governed pricing metrics, and partner with Commercial, Finance, and Product to improve margin and revenue. Set analytical standards for pricing decisions and build reusable analytical capabilities.

Leads claims-focused insights, analytics, business intelligence, and AI initiatives for an insurance organization. Partners with Claims, Reserving, Actuarial, Finance, Underwriting, and Transformation teams to translate business priorities into data products, dashboards, predictive insights, automation, and AI capabilities. Manages and develops an analytics team, ensures measurable business value, promotes responsible AI adoption, and maintains data, AI, and information governance standards.