Top Machine Learning Jobs in London

Design, develop, and maintain secure, scalable software solutions as a seasoned agile team member. Produce high-quality production code, architecture/design artifacts, data visualizations, and troubleshooting. Use CI/CD, resilient application patterns, enterprise AI-assisted development tools, and validate AI outputs. Contribute to engineering communities and improve code hygiene and system architecture.

Leads Remitly’s global transaction monitoring program, owning policy, standards, coverage methodology, scenario strategy, tuning, model governance, regulatory change, and assurance. The role identifies coverage gaps, drives remediation, defines effectiveness metrics, supports new products and payment rails, and represents the program to regulators, auditors, and partner banks. It also builds and develops teams while partnering with Data Science, Engineering, Product, Operations, and regional compliance leaders.

Lead an applied AI team building and deploying agentic automation for banking operations. Own shared AI capabilities, orchestration, evaluation, and human-agent workflows while directing end-to-end production delivery under governance. Mentor AI engineers, establish technical direction, align cross-functional teams, manage senior stakeholders, and measure agent autonomy and human involvement. The role requires hands-on AI engineering, production agentic systems experience, strong software engineering foundations, and leadership in complex regulated environments.

Design and deliver enterprise-grade machine learning systems and AI-powered applications for banking. Build scalable, secure, distributed applications and automated cloud, desktop, and ML pipelines. Apply MLOps practices for versioning, reproducibility, and observability while collaborating with cloud and site reliability engineering teams. Develop reusable libraries and business-critical, data-intensive solutions, aligning machine learning problems with business objectives. The role may include mentoring or people leadership.

Off-cycle quantitative research intern supporting systematic trading, financial engineering, statistical modeling, portfolio optimization, and derivatives pricing. Responsibilities include developing mathematical models and algorithms, valuing and hedging transactions, conducting alpha research, calibrating models, optimizing pricing, managing portfolio risk, and collaborating with traders, technologists, sales, and risk teams. Candidates gain hands-on global markets experience and may be considered for full-time employment after successful completion.

Lead technical strategy, architecture, and delivery for a large-scale risk analytics and reporting platform. Design scalable Java and React-based applications, govern frontend architecture and design systems, integrate enterprise platforms and data sources, and drive reliability, resiliency, observability, security, and compliance. Partner with Risk, Product, Data, Architecture, and Engineering stakeholders while evaluating AI, machine learning, and generative AI capabilities for analytics, automation, and decision support.

Develop and deploy statistical, machine learning, mathematical, and optimization models for Prime Finance. Responsibilities include forecasting borrow rates and demand, optimizing pricing and inventory allocation, researching alpha signals and hedging strategies, building analytics infrastructure, managing model risk, and communicating results to trading, technology, risk, and senior stakeholders. The role also mentors junior researchers and documents methodologies.

Develop and deliver production AI/ML solutions across the full lifecycle using Agile methodologies. Translate business requirements into technical specifications, build secure production code and algorithms, create architecture artifacts, analyze large datasets, develop visualizations, troubleshoot systems, and identify improvements. The role requires expertise in machine learning, Python, Spark, distributed computing, AI frameworks, agentic workflows, and AWS services, while contributing to engineering communities and emerging technology initiatives.

Conduct quantitative research for systematic trading and financial markets. Develop mathematical models, algorithms, and infrastructure to price, hedge, and trade financial products. Perform alpha research, model calibration, portfolio optimization, and risk management while collaborating with traders, technologists, sales, marketing, and risk teams. Translate research into trading applications and communicate findings clearly. The program includes hands-on training and may lead to full-time employment.

Lead the design and development of production-grade agentic AI applications for Markets Operations. Build reusable AI architecture patterns, orchestration frameworks, context management, evaluation pipelines, guardrails, APIs, infrastructure, and monitoring. Partner with researchers, engineers, data scientists, and operations stakeholders to deliver secure, reliable, compliant AI solutions. Drive continuous improvement, document technical decisions, influence architecture, and mentor engineers.

Software engineering intern supporting the Platform Engines group and Policy Engine for SailPoint’s cloud identity security platform. Responsibilities include developing software, procedures, and test solutions; collaborating with software and QA engineers, technical writers, and product managers; and working in Agile teams. The intern will gain exposure to cloud and software technologies including Golang, Angular, AWS, Docker, Kafka, Kubernetes, REST, SQL, and Temporal.

Design and maintain production-quality test infrastructure for Graphcore’s machine learning software stack. Build scalable test frameworks and CI/CD pipelines across simulators, emulators, and physical AI hardware. Create representative ML workloads, analyze execution results, improve testing and tooling, contribute to open-source projects, and mentor junior engineers. The role combines software architecture, automated testing, low-level debugging, and hardware-aware validation.

Design and maintain production-quality ML test infrastructure, CI/CD pipelines, automated testing systems, and representative workloads across simulators, emulators, and AI accelerator hardware. Analyze numerical accuracy and performance, improve testing and developer tooling, contribute to software architecture, and mentor junior engineers. The role requires strong Python, software design, Linux, CI/CD, automated testing, and C or C++ skills.

Leads software engineering strategy and development for AI/ML data platforms. Designs reusable frameworks, architects governed agentic AI development workflows, builds secure and scalable cloud-native applications, and operates 24x7 tier-1 services. Influences technical, product, and business leaders; drives adoption of engineering methods, responsible AI controls, automation, and platform capabilities across teams.

Conduct quantitative and statistical analysis of private markets and investment opportunities using proprietary datasets and models. Lead data exploration, visualization, risk-return analysis, investment diligence, portfolio insights, and integration of quantitative models with fundamental deal-team research. Apply AI and machine learning techniques, communicate actionable findings, and support client-facing teams with ad hoc analysis. The role requires strong quantitative research, programming, statistical modeling, and large-dataset analysis experience.

Researches and applies advanced machine learning, NLP, and information retrieval techniques to improve Microsoft 365 Copilot Chat and Search quality. Responsibilities include developing vector retrieval and embedding solutions, analyzing user journeys and model behavior, evaluating search relevance, adapting large language models, and partnering with engineering and product teams. The role also involves building scalable evaluation tools, mining data for opportunities, and contributing to internal or published research.

Serve as a technical SME for the Geotab ecosystem: gather requirements, deliver demos and presentations, design tailored solutions, enable partners, support sales and product teams, document engagements, and drive partner upskilling and feedback to improve offerings.

Lead complex data science and machine learning projects from technical scoping through production delivery. Set technical direction, architect reliable solutions, define roadmaps, manage technical risk, and oversee multidisciplinary workstreams. Lead client engagements, support commercial opportunities, establish technical best practices, and mentor data scientists and managers. The role also requires influencing engineering, infrastructure, commercial, and business-unit initiatives while delivering practical outcomes under commercial pressure.

Designs and governs secure, scalable enterprise AI architectures across generative AI, machine learning, automation, and agent-based solutions. Establishes reusable patterns, standards, guardrails, and governance while integrating AI with enterprise data, applications, APIs, and cloud platforms. Advises teams on responsible AI, MLOps, LLMOps, monitoring, resilience, cost, compliance, and lifecycle management. Evaluates emerging technologies, supports proofs of concept, oversees architecture across initiatives, and communicates technical decisions to business and technology stakeholders.

Deploys, tests, troubleshoots, and integrates CHAOS software and edge-sensor data in customer environments. Translates mission workflows into technical requirements, builds software products, applies machine learning and AI to sensor-data problems, and collaborates with engineering teams to improve product direction. The role requires customer-site travel of up to 25% and may involve defense-related missions.

Deploys, tests, and troubleshoots software in customer environments; translates mission workflows into technical requirements; builds software integrating edge-sensor data; applies machine learning and AI to real-world problems; collaborates with engineering teams to shape products; and provides customer feedback. Travel to customer sites and company offices is typically up to 25%.

Analyze complex datasets, identify trends and risks, and build statistical and machine learning models for forecasting and decision-making. Prepare visualizations and presentations for technical and non-technical audiences, translate business questions into analytical tasks, and use Python and SQL for data preparation and modeling. The role also supports data quality, documentation, validation, repeatable analytical methods, and the organization’s broader data and AI capability.

Principal Engineer responsible for building and operating production software at scale across multiple codebases. The role involves shipping end-user products, improving event and data pipelines, diagnosing performance bottlenecks, influencing technical direction, establishing agentic engineering practices, and collaborating with data scientists. This hands-on individual contributor role requires broad experience across Python, Node.js, React, Rust, data science, cloud infrastructure, security, privacy, and regulated financial software.

Lead Wise’s Verification Platform engineering team, managing and mentoring engineers while owning the technical roadmap, architecture, reliability, security, scalability, and operational excellence of business KYC infrastructure. Partner with global product and engineering teams, contribute hands-on when needed, run engineering rituals and incidents, and guide development of configuration-driven rules, review tooling, vendor integrations, distributed decisioning, and event-driven re-evaluation systems.

Leads client engagements and RGM projects for CPG organizations, combining consulting, data science, machine learning, analytics, and stakeholder management. Responsibilities include facilitating workshops, designing solution frameworks, generating insights, guiding data scientists and analysts, developing and refining models, coordinating engineering and product teams, supporting industrialization, and ensuring scalable, modular codebases. The role requires deep CPG and revenue growth management expertise, strong client communication, and the ability to lead advanced analytics and machine learning initiatives.