Top Machine Learning Jobs in London

Leads Jazz Pharmaceuticals’ central monitoring function across sponsored clinical trials. Defines KRIs, statistical monitoring analytics, dashboards, and risk signals; translates findings into risk-based monitoring changes; oversees platforms, documentation, inspections, governance, and cross-functional collaboration. Manages and develops central monitoring staff while advancing RBQM methodology, automation, AI, and machine learning applications. Requires substantial pharmaceutical or biotechnology clinical operations experience, people leadership, and expertise with clinical trial data, monitoring systems, and ICH guidance.

Build and maintain infrastructure, data systems, and tools for evaluating and deploying AI/ML models supporting scientific drug discovery. Partner with scientists, product managers, ML experts, and DevOps engineers to define requirements, implement algorithms, optimize GPU performance, develop cloud-native systems, and own features through production. Contribute to architecture, testing, code reviews, documentation, and engineering practices while mentoring and collaborating across teams.

Lead DTCC’s data science and AI strategy, research emerging technologies, and develop enterprise-scale prototypes involving machine learning, generative AI, LLMs, and agent-based systems. Manage and mentor data scientists, oversee project delivery and MLOps, establish data governance and modeling standards, collaborate with product and engineering leaders, and measure business impact through KPIs.

Serve as the technical authority for signal processing, inference, and machine learning systems that convert optical signals into clinical brain-monitoring measurements. Lead algorithms from research through validated production implementation, establish standards for uncertainty, calibration, reproducibility, and evaluation, and guide technical reviews, publications, patents, and regulatory documentation. Mentor data scientists and collaborate with R&D, Clinical, and Software Engineering teams.

The Tarbell Fellowship combines three months of remote training in AI and journalism fundamentals with a nine-month newsroom placement as an artificial intelligence reporter. Fellows write and investigate AI-related topics, build journalism skills, complete coursework and assignments, and attend a weeklong in-person summit. The program is full-time and intended for candidates with journalism or AI expertise, strong writing ability, persistence, curiosity, and open-mindedness. Senior fellowships are available to candidates with at least seven years of experience and exceptional prior work.

Lead the design and implementation of complex Android features, develop Server-Driven UI capabilities, and maintain mobile infrastructure including CI/CD, deployment workflows, and automated UI testing. Collaborate with product, design, and engineering teams to deliver high-scale mobile applications, champion engineering best practices, analyze user experiences, and support rapid feature delivery.

A 12-month UK industrial placement within Liberty Global’s Emerging Technology function. The student will gain exposure to technology strategy and cybersecurity, while exploring AI, data, software-led innovation, and generative AI. Responsibilities may include data analysis, coding, rapid prototyping, and developing practical AI-enabled solutions. The role includes structured training, expert support, networking, and opportunities to solve business and technology challenges.

Develop image-processing and computer-vision pipelines for optical satellite detection, streak extraction, astrometry, and real-time closed-loop tracking. Calibrate cameras and timing, validate measurements against catalogue orbits and laser-ranging data, and collaborate on camera selection and integration. The role requires image processing, computer vision, astrometry, photometry, and camera-sensor expertise in an on-site London spacetech environment.

Build and deploy practical machine learning, deep learning, and generative AI solutions. Design and evaluate experiments, work with large language models and AI agents, write production-quality code, optimize model performance, and collaborate with product and engineering teams to deliver scalable AI systems.

Build practical machine learning, statistical, and data science systems for Prolific’s participant marketplace. The role focuses on modeling participant supply, demand, and quality; improving matching to studies; validating useful signals; designing experiments and metrics; and deploying production-ready solutions. The scientist will independently shape ambiguous problems, select appropriate approaches ranging from classical statistics to LLMs, and collaborate with product, engineering, and Supply Data Science specialists.

Own the strategy, roadmap, delivery, quality, governance, commercialization, and adoption of Cognism’s B2B data products. Translate customer needs into data requirements, collaborate with engineering and data science, evaluate ML-driven data features, define success metrics, manage cross-functional stakeholders, and ensure compliance, reliability, and customer value across APIs, CRMs, and agentic systems.

Deliver enterprise and operational risk transformation and advisory services for financial institutions. Assess and improve risk processes, controls, governance, market abuse frameworks, and trade surveillance. Support AI-enabled analysis, surveillance model tuning, workflow automation, validation, auditability, and responsible AI practices. Collaborate with business, technology, compliance, and risk stakeholders to identify control gaps and deliver data-driven improvements.

Leads the design, development, deployment, and evaluation of machine learning, forecasting, recommendation, and analytical solutions. Partners with Product, Commercial, Engineering, and Data teams to solve business problems, define metrics, build scalable pipelines, and deliver measurable impact. Provides technical leadership and mentorship, promotes model development and governance best practices, and contributes to the data science roadmap.

The Senior Research Engineer will develop and deliver scalable ML solutions, collaborate with teams, and apply innovative technologies in AI to impact professionals in legal and accounting fields.

Own the end-to-end customer lifecycle for SaaS accounts, driving adoption, measurable value, renewals, and expansion. Lead strategic reviews, success plans, stakeholder engagement, and commercial negotiations. Act as a product and technical expert, explain complex concepts clearly, and collaborate with Product, Engineering, and Sales. Help shape scalable customer success processes while managing multiple customer workstreams.

Own the end-to-end customer lifecycle for SaaS accounts, including onboarding, adoption, value realization, renewals, and expansion. Build strategic success plans, lead customer reviews, identify commercial opportunities, and serve as a product and technical expert. Partner with customer success leaders, executives, revenue, operations, product, and data teams while collaborating internally to deliver customer outcomes and improve scalable customer success processes.

Lead customer-facing delivery of production-grade data and AI solutions on the Databricks platform. Own architecture and design decisions, implement end-to-end data pipelines, applications, and ML integrations, and guide customers through adoption. Collaborate with engineering, product, support, project managers, and executives; manage technical scope and outcomes; resolve implementation challenges; and create reusable frameworks and best practices. The role requires enterprise client engagement, technical leadership, cloud expertise, distributed computing knowledge, and approximately 20% customer travel.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads Salesforce’s Agentforce sales organization across the UKI region, setting sales strategy, exceeding revenue targets, developing high-performing teams, and expanding strategic customer relationships. The role requires strong AI and machine learning knowledge, technical discovery capabilities, executive engagement, pipeline and forecast management, and experience selling complex SaaS or integration solutions. The leader will drive AI adoption, consumption, retention, and expansion while partnering closely with Product, Customer Success, Marketing, and other Salesforce teams.

Designs and governs secure, scalable client solutions from discovery through implementation. Partners with Sales on technical discovery, feasibility, RFPs, and proof of concepts, while leading integration architecture across APIs, data flows, event-driven systems, cloud infrastructure, and deployment. Documents reference architectures, ensures regulatory and security compliance, reduces delivery risk, and mentors internal teams. Requires financial crime or AML experience, enterprise architecture expertise, and strong client communication skills.

Leads Ipsen’s AI Programming capability within Biometry, establishing strategy, standards, governance, and operating models for AI-enabled programming and automation. Manages clinical data scientists, AI programmers, and application developers while delivering ML, LLM, data products, and applications for clinical and R&D use cases. Partners across Biometry, IT, data and analytics teams, and external vendors to ensure validated, secure, scalable, reproducible solutions and drive organizational adoption.

Partner with large UK private-sector clients from early ideation through production planning. Provide technical advocacy, solution design, validation project management, best-practice guidance, and expertise across networking, cybersecurity, edge computing, Internet performance, and Cloudflare technologies. Explain complex concepts and ROI, support customer buying decisions, collaborate with internal specialists, and contribute to technical knowledge sharing. The role includes significant in-region travel to customer offices, conferences, and events.

Build and deploy end-to-end data science and AI solutions, including scalable data pipelines, machine learning and generative AI workflows. Translate product and business questions into measurable analytics use cases, analyze product data, establish evaluation and monitoring practices, and collaborate with product, engineering, and analytics teams to deliver business impact.

Lead the design, development, and deployment of scalable, production-grade machine learning systems. Define ML deployment standards, make architectural decisions, collaborate with technical and commercial teams, advise clients, and mentor engineers. The role requires expertise across the ML lifecycle, software engineering, cloud infrastructure, containerization, orchestration, and responsible AI delivery.

Build and deploy production-grade machine learning software, tools, and infrastructure for diverse clients. Develop scalable reusable ML solutions, collaborate with engineers and data scientists, lead technical scoping and architecture decisions, establish deployment standards, and advise technical and non-technical stakeholders. The role requires expertise across the ML lifecycle, Python software engineering, cloud infrastructure, containerization, orchestration, and core machine learning concepts.