Top Machine Learning Jobs in London

Own MGA portfolio risk and technical pricing analysis, maintaining statistical and machine-learning pricing models using Python, R, SQL, and specialized pricing tools. Identify market trends, recommend price adjustments, assess new product profitability, create underwriting dashboards, and communicate insights to business partners. The role also includes mentoring junior analysts and collaborating with underwriting and data science teams.

Build and deploy production machine learning components for agentic systems supporting geopolitical intelligence. Responsibilities include model selection, training, evaluation, benchmarking, model serving, retrieval systems, agent orchestration, and reliable deployment. The role involves collaborating with software engineering, product design, and domain analysts to translate research and user feedback into scalable, maintainable AI solutions.

Lead the design, training, evaluation, and production deployment of machine learning and agentic systems for geopolitical intelligence. Build scalable workflows using small models, LLMs, retrieval systems, and agent orchestration. Partner with software engineering, product design, and domain experts to translate research into reliable products, make technical decisions, establish engineering and evaluation standards, and mentor team members.

Design and lead architecture for agentic AI, knowledge graph, semantic, and enterprise data platforms supporting supply chain systems. Build reusable data models, ingestion pipelines, integrations, entity resolution, schema alignment, validation, and graph capabilities. Evaluate emerging technologies, define scalable architecture and governance patterns, establish quality controls and evaluators for agentic systems, and mentor technical teams while influencing product and engineering direction.

Leads technical foresight on how AI and emerging technologies may reshape global finance. Assesses AI capabilities, architectures, scaling, constraints, governance, failure modes, and systemic risks; translates findings into scenarios, hypotheses, briefings, research priorities, and thought leadership. Partners with research, standards, advocacy, and industry stakeholders to stress-test assumptions and evaluate technology’s implications for investment workflows, institutions, markets, professional roles, and standards.

Develop and maintain production data and machine learning platforms, APIs, pipelines, and services supporting live retail planning systems. Package, serve, version, and monitor machine learning models; improve CI/CD and deployment processes; define architectures and service boundaries; collaborate with data scientists and engineering teams; and provide on-call support for mission-critical tools.

Lead data science initiatives from conception through deployment, developing statistical models and machine learning algorithms for trading intelligence. Analyze large datasets, integrate solutions into production systems, support automated trading strategies, communicate insights to stakeholders, and mentor junior data scientists. Collaborate cross-functionally to improve trading processes, product offerings, and business operations.

Designs, builds, and operates scalable machine learning platforms supporting training, deployment, inference, search, recommendations, and personalization. Responsibilities include cloud-native infrastructure, backend services, CI/CD, observability, automation, distributed computing, Infrastructure as Code, incident management, and architectural leadership. The role collaborates with data scientists, engineers, and product managers, owns production reliability, mentors engineers, and shapes long-term MLOps and AI platform strategy.

Design, build, deploy, and operate large-scale machine learning systems for personalized recommendations, search, outfit discovery, and generative AI experiences. Partner with scientists, engineers, and product managers to optimize batch and real-time models, improve reliability and scalability, contribute to architecture decisions, mentor engineers, and advance shared MLOps capabilities.

Lead global direct-to-consumer commercial strategy initiatives from opportunity identification through launch, optimization, and operational handover. Translate customer, market, competitive, and trade data into recommendations, business cases, measurement frameworks, and product direction. Partner with Product, Engineering, Marketing, Analytics, Data Science, Finance, Legal, and Operations teams while influencing senior stakeholders, shaping ML roadmaps, managing change, and delivering measurable commercial growth.

Lead product strategy and roadmap for Encord’s AI data platform, spanning core platform, data curation, and exploration. Manage and develop Product Managers, establish product craft standards, conduct customer discovery, and translate enterprise needs into product bets. Partner with Engineering, Design, Sales, and Customer Success to deliver features, drive adoption, expansion, and customer outcomes while building a high-performing product culture.

Develop and deploy data science, machine learning, and AI solutions for secure government and intelligence environments. Responsibilities include predictive modelling, NLP, graph analytics, data integration, anomaly detection, model validation, dashboards, and production deployment. The role collaborates with engineering and operational teams to deliver automated insights and decision-support capabilities, while contributing to AI/ML strategy and technical roadmaps.

Build end-to-end data, analytics, machine learning, generative AI, and agent-based solutions. Develop predictive models, conduct experiments, analyze trends, create insights, and support governed, secure, scalable data platforms. Collaborate in agile teams while applying data management, privacy, risk controls, and compliance practices. The program includes mentorship and training for students pursuing quantitative or technical degrees.

Own strategic post-sales engagement for BioCatch’s largest customers across EMEA. Serve as the primary customer contact, coordinate technology delivery projects, manage risks and escalations, lead business reviews, advocate for customer needs internally, and identify expansion opportunities with account managers. Translate technical requirements for business stakeholders, oversee project plans and milestones, support solution adoption, and coach project teams while maintaining trusted-advisor relationships.

Drive strategic account growth and new-logo acquisition across Media and Entertainment clients. Build executive relationships, diagnose business and technical challenges, develop Google Cloud-backed data and AI transformation roadmaps, lead commercial negotiations, and collaborate with Google Cloud and internal delivery teams to realize client value and expand engagements.

Own platform-level optimization for production LLM and VLM inference. Responsibilities include improving latency, throughput, memory efficiency, GPU utilization, reliability, quality, and cost; deploying and extending inference engines; building compression and acceleration workflows; developing benchmark and rollout frameworks; diagnosing performance bottlenecks; and providing technical direction across machine learning, GPU, infrastructure, research, product, and customer teams.

Lead technical strategy, architecture, capability testing, and deployment of secure, scalable machine learning systems. Build production-grade ML infrastructure, establish safe and reliable AI standards, guide multi-agent and frontier-model evaluations, advise senior clients, mentor engineers, and collaborate with Frontier Labs on AI safety, governance, and sandboxing frameworks.

Own the fraud threat model, risk appetite, detection thresholds, measurement framework, and fraud response policies. Lead fraud specialists, define workflows and escalation standards, guide machine-learning strategy, and manage tooling and vendor decisions. Partner with Operations, Product, and Engineering to evolve fraud detection, balance fraud prevention with genuine-user experience, and establish a scalable fraud function.

Build and maintain scalable AI platform infrastructure, frameworks, libraries, and services supporting multiple engineering teams. Develop AI applications using LLMs, RAG, agentic architectures, prompt engineering, guardrails, and evaluation frameworks. Improve reliability, observability, monitoring, incident management, cost efficiency, developer experience, and safe deployment of AI-powered products while partnering with product and engineering teams.

Produce analytical evidence for AI product decisions by scoping questions, designing experiments, building prototypes, analyzing guardrail performance, benchmarking metrics, and recommending actions to product managers. Work includes evaluating AI safety controls, measuring accuracy, latency and cost, prototyping workflows, and reporting results. The role develops expertise in AI security, guardrails, agent systems, and experimental design while providing a path toward product management.

Leads advanced data science projects and specialized modeling for media effectiveness and marketing analytics. Translates complex analytical requirements into deployable solutions, contributes hands-on to machine learning and statistical modeling, develops analytical tools and dashboards, and applies AI and generative AI. Manages projects from conception through delivery, communicates technical concepts to stakeholders, and provides technical leadership across analytics, media, and data teams.

The Senior PreSales Consultant partners with sales and technical teams to discover customer needs, deliver tailored demonstrations, lead solution and architecture workshops, answer RFPs, and articulate platform value. The role develops decisioning and AI/ML solution strategies, maintains demonstration environments, supports webinars and thought leadership, documents solution requirements, enables internal teams, and feeds customer insights into product roadmaps. Expertise in fraud, financial crime, enterprise software, and financial services is central to the position.

Leads transformation-focused product strategy for Tekion’s automotive retail platform. Serves as the dealer operations subject matter expert, shaping roadmap priorities across sales administration, vehicle stock management, accounting integration, and enterprise workflows. Defines operational standards, transformation frameworks, and change-readiness tools; engages dealer groups and finance stakeholders throughout sales, onboarding, and post-launch. Owns UK and Canadian market considerations, including regulatory, tax, franchise, and OEM requirements, while feeding customer and implementation insights back into product development.

Executive leader responsible for globally scaling Customer Success across delivery, support, engineering, education, and project management. The role focuses on reducing churn, increasing adoption and expansion, optimizing the customer lifecycle, proving customer value, incorporating customer feedback, improving operational efficiency, and building talent. The VP partners closely with Sales, Product, Marketing, Finance, and Legal while serving on the executive leadership team and reporting to the CEO.

Lead full-cycle recruitment for niche AI/ML, computational, scientific, and deep-tech roles. Source passive candidates, manage complex global interview processes, advise hiring managers, support Programme Director searches, and build scalable recruiting systems. Partner with Communications on employer branding and represent ARIA at scientific and technology events to develop talent pipelines. The role requires strong in-house recruitment experience, proactive sourcing, autonomy, organization, systems thinking, and familiarity with the UK R&D talent landscape.