Top Machine Learning Jobs in London

Own the strategy, roadmap and outcomes for a new data harmonisation capability. Lead development of reusable data products and capabilities involving OMOP, data transformation, ontologies, metadata, provenance and versioning. Work with researchers, engineering, data science and product teams to prioritise datasets, improve interoperability and make data AI-ready. Establish a scalable 0-to-1 product, guide technical trade-offs, and measure reductions in researchers’ data preparation time.

Build, deploy, and maintain machine learning models for Snapchat products. Apply ML techniques to real-world problems, contributing across data analysis, training, evaluation, and production deployment. Collaborate with cross-functional teams to launch scalable ML-driven features using modern AI tools and engineering workflows while maintaining high standards for correctness, security, and production quality.

Lead scientific delivery for partner materials-discovery projects, from scoping through results. Apply simulation, machine learning, data-driven optimization, or experiments; design discovery campaigns; validate models against experiments; and translate between experimental, computational, and ML teams. Build reliable automated workflows, improve data pipelines, prepare technical reports, and relay partner feedback to technical teams. The role requires deep expertise in materials science, chemistry, physics, chemical engineering, or related fields, along with Python and Git proficiency.

Lead Data and AI pre-sales activities, translating client needs into commercially viable solutions. Support opportunity qualification, workshops, bids, proposals, solution design, and account strategy while collaborating with sales teams and technology partners. Present solutions to technical and executive audiences, develop propositions and thought leadership, monitor market trends, and share customer insights. Travel to client sites and industry events as required.

Lead digital products for structured, collaborative, and AI-assisted authoring of scientific and regulatory content across pharmaceutical R&D. Own product vision, roadmap, backlog, value case, delivery, technical decisions, compliance, validation, and risk management. Partner with engineering, architecture, medical writing, regulatory, safety, clinical, CMC, and publications teams. Mentor Product Owners, manage strategic suppliers, and ensure GxP, data integrity, privacy, and AI-governance requirements are embedded throughout delivery.

Develop predictive models, machine-learning, AI, and Generative AI solutions for Crop Protection R&D. Analyze complex scientific datasets, identify valuable AI use cases, validate and govern models, and deploy scalable analytical applications. Partner with scientists, software teams, and stakeholders to support product discovery and decision-making. Communicate insights, track performance metrics, document methodologies, and mentor junior data scientists.

Design, build, test, deploy, and operate production AI and machine learning systems using Python. Own solutions across the full lifecycle, including data preparation, evaluation, deployment, monitoring, optimization, and continuous improvement. Build generative AI, agent-based systems, backend services, APIs, and data pipelines using CI/CD, containerisation, cloud-native practices, and observability. Apply responsible AI principles and communicate technical decisions across cross-functional teams.

Leads customer discovery, solution design, technical presentations, demonstrations, proofs of concept, and sales campaigns for Salesforce nonprofit solutions. Partners with sales, product, engineering, and implementation teams to develop CRM, data, AI, and Agentforce solutions. Advises executives and technical stakeholders, creates reusable accelerators, researches industry challenges, and communicates solution value through storytelling and persona-driven demonstrations.

Own and improve production machine learning pipelines focused on accuracy, efficiency, and trustworthiness. Build LLM evaluations, judges, datasets, metrics, and benchmarks; run experiments across prompts, models, and pipeline designs; and reduce LLM costs through smaller task-specific models. Expand anonymization capabilities, evaluate open-source models, and collaborate with quality engineers to ship reliable ML features. Document findings and communicate technical trade-offs clearly.

Develop machine learning models and decision-support tools for pricing optimization, margin forecasting, deal scoring, customer segmentation, and opportunity analysis. Analyze commercial datasets, create dashboards, support A/B testing, and translate insights into tools for sales, finance, and pricing teams. Collaborate with business intelligence, data engineering, pricing, and deal operations stakeholders to improve commercial decision-making.

Design, build, deploy, and operate large-scale batch and real-time machine learning systems for search, ranking, recommendations, and personalisation. Collaborate with scientists, engineers, and product managers to deliver reliable production models, improve engineering practices, mentor colleagues, and shape technical direction across the machine learning community.

Own the UK loans origination funnel and onboarding journey, improving conversion across web and mobile experiences. Define product strategy and roadmap, prioritize delivery, run data-driven experiments, and collaborate with Engineering, Credit, Data Science, Finance, Operations, Design, and third parties. Use Python, SQL, and AI tools to analyze performance, automate workflows, and drive growth while balancing innovation, compliance, and risk.

Lead strategic analytics across pricing, promotions, forecasting, growth, credit risk, product development, and portfolio performance. Build analytical models, KPIs, monitoring tools, and commercial assessment frameworks to guide responsible growth and expansion decisions. Collaborate with Credit Risk, Data Science, Product, and Growth teams, present recommendations to senior leadership, validate outcomes, and eventually hire and mentor a team of analysts.

Develop and deploy large-scale machine learning and LLM systems for Flo’s female health products. Build medically safe generative AI features, architect ML/LLM pipelines and serving infrastructure, manage data pipelines, and own solutions end to end. Collaborate with engineering, product, QA, and analytics teams while applying responsible AI practices, MLOps, testing, and CI/CD to deliver scalable product capabilities.

A 10-week technology internship at TD Securities in London involving three rotations across software engineering, data, AI, analytics, automation, and financial technology. Interns will design, build, test, deploy, and improve software, platforms, data capabilities, APIs, and user experiences while collaborating with global engineering and business teams.

A 12-month technology graduate programme involving three rotations across engineering, data, AI, compliance, and banking technology teams. Participants will design, build, test, deploy, and improve software, applications, platforms, APIs, data capabilities, analytics, automation, and AI solutions. The role includes debugging, peer reviews, collaboration with global teams, structured learning, and hands-on delivery in a financial-services environment.

Manage strategic mobile gaming and app publisher accounts across EMEA, driving monetization, revenue growth, mediation integrations, and cross-sell opportunities. Build partner growth plans, analyze metrics such as fill rate and eCPM, coordinate with Product, Marketplace, Demand, Engineering, and Sales teams, and represent InMobi at gaming industry events. The role also requires technical knowledge of SDKs, in-app bidding, mediation, and SSPs, plus coaching peers and influencing product strategy.

Lead Wise’s Risk Modelling ML Engineering pillar for financial crime detection. Own the model lifecycle from experimentation through deployment, monitoring, retraining, drift detection, and auditability. Architect a scalable model factory and experimentation platform supporting hundreds of models under strict latency requirements. Recruit, mentor, and lead senior ML engineers while setting technical standards, making architecture decisions, and partnering with platform teams.

Senior corporate counsel responsible for negotiating complex AI procurement, SaaS, data licensing, and technology transactions. The role provides legal and strategic guidance on AI, data, intellectual property, regulatory, and contractual risks; leads negotiation quality control; develops playbooks; supports product and go-to-market strategies; and drives cross-functional contracting initiatives. Candidates must be admitted to a UK or U.S. state bar and have at least 10 years of relevant legal experience.

Own the product roadmap and portfolio prioritization for vehicle data and telematics products supporting regional revenue growth. Lead customer discovery, product strategy, solution validation, reporting frameworks, feedback loops, and cross-functional execution from discovery through launch. Partner with UX, engineering, data science, project management, customers, and senior leadership while mentoring product managers and driving data-informed decisions using product KPIs and user engagement metrics.

Develop and deploy AI and machine learning solutions for workforce analytics. Conduct statistical analyses, experiments, and model evaluations; build scalable model factories and evaluation frameworks; apply generative AI and agentic development approaches; collaborate with business, product, and technology teams; communicate insights; and ensure compliance with risk, control, and governance requirements.

Develop propensity, segmentation, statistical, and machine-learning models to support business decisions. Operationalize analytical data products, lead end-to-end analytics and AI projects, identify actionable insights, and communicate recommendations to senior stakeholders. The role focuses on solving real business challenges, measuring outcomes, and helping Dogs Trust improve its impact through data-driven decision-making.

Lead enterprise-wide third-party risk management strategy, operating model design, regulatory readiness, operational resilience, outsourcing risk, supplier due diligence, and AI and technology supplier risk engagements. Advise boards, executives, regulators, and financial institutions; lead multidisciplinary transformation teams; develop client propositions and thought leadership; and mentor TPRM professionals. The role requires deep banking, insurance, asset management, or regulatory expertise combined with consulting, commercial, and client leadership experience.

Leads the design and deployment of document understanding AI for legal, tax, and accounting content. Develops semantic chunking, enrichment, classification, information extraction, knowledge graph, tabular understanding, and evaluation systems supporting search, retrieval, RAG, and agentic workflows. Provides architectural leadership, partners with engineering to productionize scalable solutions, contributes to AI strategy, and mentors applied scientists and machine learning practitioners.

Develop data science and marketing effectiveness solutions for international clients. Apply econometrics, statistical machine learning, AI, Marketing Mix Modeling, and customer analytics end to end, from data gathering and analysis through modeling and presentations. Collaborate with clients, contribute to research and development, strengthen data engineering capabilities, and support company culture and sustainability initiatives.