Top Machine Learning Jobs in London

Lead global design, governance, and deployment of quantitative financial crime detection models and risk-rating engines. Translate regulatory requirements into algorithmic controls, calibrate monitoring and screening thresholds, validate models, and partner with Product, Engineering, and investigations teams to minimize false positives while maintaining detection performance. Represent model logic and tuning to regulators and banking partners.

The Senior Product Designer will lead the creation of user-centric digital products, focusing on UX, UI, and AI integration in financial services and energy.

Lead design and delivery of secure, scalable enterprise software for private banking. Build reusable frameworks, integrate AI/ML, ensure operational stability, mentor engineering teams, and influence senior stakeholders to drive digital transformation.

Lead a global KYC, name screening and transaction monitoring operations team. Define strategy, staffing and 24/7 coverage, drive AI/automation transformation, improve procedures and controls, balance risk with customer experience and commercial impact, and partner with Risk Product, Engineering and 2LOD to scale onboarding and FinCrime operations.

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 a Treasury Global Liquidity data engineering team to design and deliver real-time liquidity observability, scalable event-driven systems, automated investment and risk tooling, and analytics/ML collaboration. Define technical strategy, partner with product and stakeholders, mentor engineers, and ensure resilient, high-performance data platforms that optimise cash management and customer experience.

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.

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.

Lead product analytics for high-volume international transfers, driving insights on verification, service quality, and predictability. Conduct causal analyses and A/B tests, set KPIs with product teams, track impact, and recommend product and support strategies to improve retention, conversion, and operational efficiency.

Lead and manage an ML engineering team building post-origination credit models for repayment, collections, recovery, and loss mitigation. Define technical strategy, design and scale ML solutions, partner with product, engineering and risk, mentor engineers, and operationalize models to minimize loan loss and personalize collections.

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.

Design and deliver a real-time liquidity observability stack and core treasury functions. Build scalable, event-driven systems, implement monitoring and control mechanisms, automate cash investment and risk tooling, and collaborate with product, data scientists and stakeholders to forecast cash flows and optimize liquidity.

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

Lead applied AI research and architecture for GenAI in cybersecurity: design LLM architectures, implement production-grade deep learning systems at scale, mentor scientists, set technical standards, and align AI strategy with business goals.

Lead the development of AI-enabled products within a consulting environment focused on financial services and energy, utilizing modern AI tools and methodologies.

Conduct market microstructure research and develop quantitative models for equity algorithmic execution. Analyze large trading datasets, backtest methodologies, run A/B experiments, build analytics and data pipelines, validate production models, and collaborate with technology and client teams to industrialize research and produce client-facing materials.

Perform malware and threat research across the attack lifecycle, validate and improve Falcon detections, collaborate with engineering and ML teams, automate analysis, support external security testing, and translate findings into product protections.

Lead AI-driven data and analytics programmes in financial services, mentoring teams and ensuring delivery of measurable outcomes.

Conduct end-to-end ML research for options trading: design experiments, train large-scale models (LLMs/transformers), build scalable pipelines, validate and deploy models to production, and translate research into measurable P&L impact.

As a Data Analytics Principal Consultant, you'll lead data transformation initiatives, mentor consultants, and engage with stakeholders in financial services to drive data literacy and innovation.

Build and maintain the integrity layer for label data used in FinCrime ML models. Define and monitor statistical quality metrics, design automated label audit processes, and work end-to-end on model training, evaluation, and deployment in collaboration with Risk Intelligence, Data Engineering, and Product teams.

Lead and mentor an Applied AI team to design, train, evaluate, and deploy large-scale NLP/LLM, graph, and agentic ML solutions; collaborate with business, tech, and control partners; develop firm-wide ML frameworks; research and prototype advanced methods.

Design, build, and deploy scalable ML services combining NLP, CV, and statistical methods. Collaborate with engineers, cloud and SRE teams to productionize models, ensure observability and compliance, and maintain models over time. Communicate results to stakeholders and document processes for regulatory needs.

Lead design and delivery of enterprise-grade ML systems and production architectures. Build scalable, distributed, data-intensive applications using MLOps and cloud infrastructure (AWS, Kubernetes). Translate research into deployable ML solutions, implement automated pipelines, leverage PyTorch/NumPy and higher-level interfaces, collaborate with SRE/cloud teams, and mentor or optionally manage team members.

Lead a technical area driving design, development, and delivery of AI/ML and data platform solutions. Own architecture, governance, and adoption of AI-assisted engineering practices, ensure operational stability, and influence stakeholders while hiring and developing engineering talent.