Top Machine Learning Jobs in London

Owns FICO’s strategic partner relationship across EMEA, driving joint revenue growth, pipeline development, sales campaigns, negotiations, and multi-million-dollar channel agreements. Builds C-suite relationships, develops multi-year engagement roadmaps, coordinates internal and partner teams, manages multiple sales engagements through close, and leads co-marketing and co-sales initiatives. Requires deep expertise in payments, banking, fintech, channel pricing, AI, decisioning, and partner ecosystem dynamics.

Owns FICO’s strategic partner relationship across EMEA, driving joint revenue growth, channel sales, multi-year engagements, and major contract negotiations. Builds C-suite relationships within the payments and fintech ecosystem, leads virtual sales and delivery teams, develops partner strategies and pipelines, and manages sales campaigns through close. The role also supports co-marketing, industry events, forecasting, and solution positioning across banking, payments, AI, machine learning, and decisioning markets. It requires regular onsite work in Basildon and London plus periodic US travel.

Join the Indexes New Product Development team to design, develop, validate, and launch thematic and smart-beta indexes. Collaborate with research and product teams, build portfolio construction and analytics tools using statistical and machine learning methods, explore AI implementations, and produce white papers, factsheets, and client-facing materials to support product launches.

Lead complex, cross-functional technology programs across AI, software, robotics, vehicle hardware, infrastructure, simulation, data, and product teams. Own delivery plans, improve efficiency, coordinate stakeholders, manage risks and dependencies, align execution with business goals, and drive continuous process improvement in a fast-paced autonomous-vehicle environment.

Own the full sales cycle for technically complex B2B deals involving APIs, on-premises deployments, and custom licensing. Manage multi-stakeholder negotiations, build strategic customer relationships, drive retention and expansion, and develop sales processes in an early-stage environment. Collaborate with engineering, research, product, solutions engineering, and legal teams while translating customer needs into product feedback.

Lead end-to-end technical strategy for customer accounts, from discovery through deployment and platform adoption. Design scalable solutions across data engineering, machine learning, AI, streaming, and real-time analytics. Advise senior technical stakeholders, lead architecture discussions, demonstrate competitive differentiation, coordinate cross-functional resources, develop technical specialization, and influence product direction through customer feedback.

Own Fuse Energy’s AI product roadmap from identifying high-impact problems through scoping, prioritization, and launch. Partner closely with AI Engineers on customer operations, data, and internal tooling projects; translate ambiguous business needs into defined initiatives and success metrics; manage the engineering backlog; and establish production evaluation and feedback loops to improve AI systems after launch.

Provide clinical guidance across sales, implementation, product, and support teams for InterSystems health information solutions. Engage senior clinical and executive stakeholders, lead demonstrations and workshops, support procurement and digital transformation, and translate clinical requirements into solution narratives to drive adoption and product strategy.

Paid 6-12 month research internship creating and experimenting with ML models across vision, language, and audio to support game development, own data analysis/creation, and help deploy models at scale while collaborating with research scientists and engineers.

Owns growth and P&L responsibility for strategic UK insurance accounts. Develops account plans, identifies new business and cross-sell opportunities, manages executive relationships, sells AI, data, cloud, and digital transformation solutions, and oversees the full sales cycle through contract negotiation and delivery coordination. The role manages revenue portfolios exceeding £5 million and travels 30–50% to client sites, conferences, and related events.

Lead an agile engineering team to design, build, review, and maintain secure, high-quality production software. Provide technical direction, drive product and architecture decisions, champion firmwide SDLC practices and AI-assisted engineering, and ensure operational stability and reusable automation across cloud-native applications.

Leads Babyzone’s data, impact measurement and evaluation strategy across its national hub network. Builds end-to-end data infrastructure, longitudinal outcome tracking, dashboards and trusted reporting systems; operationalises evaluation measures including the Parent Confidence Index and Good Level of Development analysis. Designs hands-on AI and automation workflows, ensures data protection and ethical governance, works with academic partners, supports policy influence, and builds organisational data capability through team leadership and frontline engagement.

Analyze Spotify’s planning and execution data to develop hypotheses, statistical and machine learning models, data pipelines, dashboards, and actionable recommendations. The role translates complex operational and strategic information into narratives, reports, and decision frameworks for leaders. It also explores AI and automation for faster insight generation and collaborates with data scientists, engineers, product managers, and business partners across the company.

Develop and optimize machine learning and generative AI solutions for digital agents, including LLMs, NLP, RAG, retrieval, experimentation, evaluation, and statistical analysis. Build scalable data and ML pipelines using Python, SQL, and Spark; assess model quality, reliability, safety, and business impact; and support production deployment. Collaborate with engineering, product, and data science teams while mentoring less experienced colleagues.

Guide clients through Cordial platform implementations, third-party integrations, API and JavaScript integrations, data modeling, dynamic scripting, and machine learning capabilities. Serve as a technical subject matter expert, resolve complex issues, manage projects and stakeholder expectations, document solutions, and collaborate with Product, Engineering, Client Success, Support, Strategy, Sales, and partners to drive adoption and successful client outcomes.

Build and operate production machine learning systems supporting customer engagement, marketing, pricing, personalization, and commercial decision-making. Own data ingestion, feature engineering, deployment, monitoring, and optimization across the ML lifecycle. Develop reusable MLOps tooling and infrastructure, influence architecture, partner with Applied Scientists and Data Engineers, and mentor engineers while delivering scalable solutions with measurable customer and business impact.

Lead post-training and fine-tuning of Wayve’s Gaia world model, improving long-horizon autoregressive stability, steerability, inference reliability, and deployment readiness. Collaborate across research, simulation, applications, and infrastructure teams to deliver measurable driving-model impact. Provide technical leadership through architecture and training decisions, mentorship, code review, experimentation, and targeted data curation.

Price complex property, construction, specialty, and casualty insurance accounts; develop, test, calibrate, and maintain pricing tools and models; analyze portfolios and market trends; build dashboards and management reporting; support regulatory submissions; review pricing adequacy; consult on underwriting projects; and train underwriters on new platforms and tools.

Build and deploy production machine learning models and fraud decisioning tools. Partner with business teams to identify performance gaps, automate repetitive work, run experiments, and optimize transaction-risk strategies. Write production and analytical Python code, use SQL and distributed data platforms such as Spark, Databricks, and GCP, and communicate findings to technical and business audiences. Participate in code reviews, peer collaboration, and an on-call weekend rotation approximately six times annually.

Lead product vision and roadmap for agentic AI solutions, own the backlog, translate business needs into user stories, partner with data scientists and engineers, manage Agile delivery cycles, ensure responsible AI practices, and monitor model performance and continuous improvement.

Leads enterprise-wide AI transformation strategy, operating model, governance, adoption, and value realization. Establishes the AI Transformation Office, scales intelligent automation and AI-enabled workflows, develops responsible AI controls, builds organizational AI literacy, and advises executive leadership and the Board. Oversees cross-functional AI leads and aligns Engineering, IT, Finance, HR, Supply Chain, Compliance, and Cybersecurity initiatives to deliver measurable productivity, quality, cost, and operational improvements.

Lead the strategy, roadmap, and evolution of Stewardship Intelligence, a governance and capital-markets data product. Manage Product Management and Research teams, improve dataset quality and coverage, scale research operations through automation and AI, and partner with Engineering, UX, Commercial, Marketing, Sales, and Customer Success. Translate institutional investor and corporate issuer needs into product priorities, launches, data services, and ongoing improvements.

The Senior Service Designer will create customer-centric products and services, lead workshops, and oversee design delivery while incorporating AI capabilities.

Build and improve tabular machine learning training pipelines for dynamic pricing and recommender systems in mobile games. Responsibilities include feature and label design, model training and evaluation, monitoring performance and drift, diagnosing data and model issues, and supporting A/B testing. The role partners with monetization and product teams to connect model decisions with revenue and player outcomes, while using and validating AI-assisted development tools.

Own optimization of LLM and VLM inference systems from model artifacts through production deployment. Improve latency, throughput, memory efficiency, GPU utilization, quality, reliability, and cost per token. Deploy and extend inference engines, build compression and quantization workflows, implement decoding and KV-cache optimizations, create reproducible benchmark harnesses, diagnose production regressions, and collaborate with kernel, platform, research, infrastructure, product, and customer teams.