Top Machine Learning Jobs in London

Designs and delivers enterprise AI solutions using Claude. Acts as a pre-sales technical advisor, translating customer needs into scalable architectures, guiding evaluations and deployments, presenting to technical and executive audiences, and coordinating with Sales, Product, and Engineering. The role requires customer-facing architecture experience, enterprise integration expertise, Python familiarity, LLM knowledge, German and English fluency, and occasional customer travel.

The role involves defining and pursuing a research project in general-purpose world models, collaborating with teams to advance visual AI.

Own multi-year product vision and roadmap for a UK home-services marketplace. Lead and grow PM teams, prioritise commercial and platform investments, integrate AI/ML across product (search, matching, personalisation, automation), partner cross-functionally, and represent product to the board and investors to drive revenue, retention, and growth.

Lead product strategy and execution for core finance product areas, turning complex financial workflows into intuitive AI-powered experiences. Partner with Engineering, Design, Data, and GTM teams; work directly with CFOs and finance teams to discover user needs, define priorities and metrics, and ship high-impact features from 0→1 to scale.

The Senior Product Manager will drive product initiatives for UK Cards, improving customer experience and leveraging AI tools while collaborating cross-functionally on growth strategies.

The Product Manager will enhance the UK Cards product by driving growth initiatives and AI-enabled workflows, collaborating across teams and improving customer experience.

Manage and scale systematic trading strategies for global agricultural commodities by combining fundamental research with quantitative modeling, alternative datasets (weather, satellite, logistics), rigorous backtesting, portfolio construction, risk management, and productionalizing research into live trading systems.

Lead technical development and production deployment of ML models for search, personalization, ranking, and recommendations. Build scalable, explainable ML solutions, own end-to-end impact, collaborate with backend and product teams, and iterate on experiments using cloud-native data at scale.

Support customer implementations, onboarding, process documentation, internal tooling, knowledge-base management, operational reporting, and product feedback analysis. Collaborate with Customer Success, Product, and Engineering to scope improvements, identify process gaps, automate workflows, monitor customer adoption and KPIs, and perform QA for feature releases.

Principal Solution Engineer responsible for helping UK customers evaluate and adopt Snowflake. Partners with sales teams, channel partners, and customers to present solutions, conduct demos and proofs of concept, design architectures, develop technical champions, and secure technical wins. Requires strong modern data, cloud, SQL, presentation, and customer-facing skills. The role also involves competitive positioning, partner collaboration, account planning, and representing Snowflake at industry events.

Generate qualified B2B sales opportunities by following up inbound leads and targeted outreach. Conduct account research, org-chart identification, and high-volume outbound prospecting. Collaborate with Account Executives on campaign tactics, maintain CRM records, and support field marketing events.

Build and extend the platform layer: design shared backend services and APIs, improve reliability and correctness, collaborate with product teams to remove platform bottlenecks, mentor engineers, shape architecture and migrations, and write high-quality fullstack code with a backend focus.

Lead data strategy and engineering for AD/ADAS: collect, sample, label, and curate large-scale vehicle and simulator data; build evaluation frameworks and scalable pipelines; analyze data to improve perception and planning models; collaborate across teams and mentor colleagues to deploy production-ready ML solutions.

The Technical Account Manager will drive customer success, manage accounts, deliver onboarding and demos, and align engineering solutions with customer needs.

10-week on-site Data Science internship in London supporting data discovery, enrichment and analysis for quantitative research. Tasks include data blending, statistical analysis, exploratory data analysis, visualization, building tooling, and collaborating with data scientists to scale and automate data processes.

10-week London-based summer research internship applying mathematical modelling, ML/deep learning, optimisation and back-testing to extract predictive signals from financial time-series. Interns complete an independent research project with mentorship, present findings to senior management, and may be considered for full-time roles.

The Director of Colleague Data Science & Analytics will lead the global HR analytics function, driving innovative solutions to support decision-making and enhance the colleague experience.

Provide clinical and translational expertise to prioritize clinical questions, interpret model outputs, advise on data and academic partnerships, and support pharma engagement for onco-immunology digital-twin and data-generation programs.

Lead Workday Compensation implementations and HCM transformations: run workshops, gather requirements, configure, test, deploy, lead workstreams, liaise with clients and teams, and apply Accenture/Workday methodologies and AI-enabled tools.

Own the end-to-end product direction and quality of AI-powered email capabilities. Define requirements, evaluation frameworks, quality standards, and measurable outcomes while balancing model quality, latency, cost, reliability, safety, and user experience. Partner closely with ML, backend, and client engineers on system design, experimentation, failure handling, and iteration. Drive prioritization, technical decisions, execution, and user trust in a fast-moving zero-to-one environment.

Own the end-to-end product strategy and execution for an AI-powered email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing reliability, latency, cost, safety, and usability. Partner closely with ML, backend, and client engineering teams to identify failure modes, improve system performance, prioritize work, and deliver predictable, trustworthy AI capabilities in a fast-moving zero-to-one environment.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing quality, latency, cost, reliability, safety, and usability. Partner closely with ML, backend, and client engineers to identify failure modes, prioritize improvements, run experiments, and deliver predictable, trustworthy AI features in a fast-moving zero-to-one environment.

Owns the end-to-end definition, evaluation, and quality of AI-powered email capabilities. Partners with ML, backend, client, and product teams to translate model behavior and technical constraints into requirements, system decisions, experiments, and roadmaps. Balances quality, latency, cost, safety, reliability, and user experience while identifying failure modes and building trustworthy, predictable AI features.

Lead the engineering strategy and execution for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, lead a senior engineering team, make architecture and scalability decisions, and establish strong observability, monitoring, testing, security, and operational foundations. Partner with ML leadership to deliver fast, reliable, maintainable product experiences.

Lead application engineering for an AI email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, guide a senior engineering team, make architecture and reliability trade-offs, and establish observability, monitoring, testing, and operational excellence. Partner with ML leadership to ship scalable, maintainable, and high-performing AI-powered experiences.