Top Machine Learning Jobs in London

Surgo Health seeks a Senior Product Manager for AI and Data Products to define product strategy, lead product initiatives, and collaborate with cross-functional teams. The role emphasizes building AI-driven solutions in healthcare and requires strong product management skills with a technical background.

Lead and grow engineering teams building a broker platform. Drive technical strategy, deliver microservice-based features, advocate Agile/Lean practices, recruit talent, measure team impact, and collaborate with senior stakeholders and CTO on architecture and infrastructure.

Design and develop image/video diffusion models, perform large-scale model training, maintain data for training, and optimize models for production use.

Design and develop novel machine learning models and training algorithms for drug discovery. Lead and run research projects, implement and scale training/inference frameworks, analyze experiments, and collaborate across disciplines. Mentor and provide technical guidance to other ML researchers and contribute to research direction and presentations.

The Treasury Manager will oversee treasury operations, manage cash flow, implement risk management practices, and lead a high-performing team while aligning strategies with business objectives.

Drive strategic revenue and transform enterprise client relationships through long-term engagements, navigating challenges and delivering measurable business value.

Lead the design, development, and operation of analytical and ML systems for financial services, ensuring model accuracy and compliance.

Join the Data Science team to innovate solutions for data problems, develop forecasting models, and enhance quant strategies using machine learning.

Design, build, optimize, and productionize machine learning pipelines, data engineering workflows, tooling, and low-latency Python/C++ systems. Partner with quantitative researchers to turn trading model prototypes into scalable production systems. Responsibilities include architecture design, performance optimization, automated testing, cloud deployment, model serving, and supporting real-time inference for sports betting analytics.

Leads large-scale data curation, enrichment, and rare-event mining for autonomous vehicle machine learning systems. Builds workflows for model introspection, out-of-distribution detection, robust evaluation, and driverless operation. The role spans model engineering, infrastructure, compute, and data, requiring ownership of distributed ML systems, large-scale inference, and collaboration across research and engineering teams.

Lead large-scale training and improvement of Gaia, Wayve’s video world model for autonomous driving. Design experiments, influence model architecture and training strategy, improve synthetic scenario generation, and deliver production-grade training systems. Collaborate with research, simulation, applications, and infrastructure teams while providing technical leadership through mentorship, reviews, and direction-setting.

Design and optimize overseas risk-control strategy and tools; manage anti-fraud and chargeback countermeasures; perform user/merchant data analysis; develop risk indicators, monitor daily risks, and produce reports to guide process optimization and business decisions.

Lead design and operation of end-to-end risk governance and decision systems for marketplace fraud and integrity. Build real-time and pre-transaction controls, integrate rule engines with ML/AI signals, set decision policies, tune production systems, and lead cross-functional teams to detect, prevent, and optimize fraud prevention at scale.

Lead and mentor an algorithms team to design, develop, and optimize search and recommendation models. Oversee traffic distribution, experimentation, scalable deployment, and performance evaluation to improve personalization, conversion, and engagement while collaborating with product, engineering, and business teams.

Lead complex, cross-functional technology programs across AI, software, robotics, infrastructure, data, evaluation, validation, and product teams. Own project delivery, improve workflows and tooling, manage risks and dependencies, align stakeholders, support strategic priorities, and drive continuous process improvement. The role requires strong technical understanding, systems thinking, communication, project management, and the ability to influence teams in an ambiguous, fast-paced environment.

Lead and grow a team of ML engineers to build and operate production ML and LLM systems—real-time inference APIs, batch data products, and agentic AI—ensuring deployment, monitoring, CI/CD, reproducibility, and measurable business impact.

Develop and test Generative AI and LLM applications in Python, including RAG systems, prompt workflows, AI agents, and API integrations. Evaluate model performance, reliability, accuracy, and responsible AI considerations while collaborating with engineering, data science, and product teams. Contribute to documentation and Git-based development, learn emerging AI technologies, and help deliver production-ready solutions.

Reviews and evaluates engineering, infrastructure, and platform documents and work products for technical accuracy, completeness, clarity, consistency, and relevance. Provides structured feedback, identifies errors and gaps, compares technical approaches, and follows defined evaluation frameworks. Requires independent work, strong communication, analytical ability, and professional engineering judgment. The role is fully remote and supports varied technical evaluation projects as an independent contractor.

Own the full lifecycle of machine learning features, including research, experimentation, training, deployment, monitoring, and production infrastructure. Build smart payment-routing models and MLOps practices, partner with product, data, engineering, and customers, evaluate outcomes statistically, mentor engineers, and help define ML technical direction and reliable development processes.

Own the full lifecycle of machine learning features, including research, experimentation, training, deployment, monitoring, and production optimization. Build ML infrastructure for payment routing, establish reliable MLOps practices, partner with product, data, engineering, and customers, mentor engineers, and guide technical priorities. Apply statistical thinking, A/B testing, pragmatic model evaluation, and reinforcement learning concepts in a remote-first environment.

Own an AI stockbroking product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, security, scalability, and failure handling. Integrate KYC, transaction monitoring, privacy, licensing, and regulatory requirements. Apply AI with evaluation, safeguards, and human oversight while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.

Develop and ship end-to-end machine learning models for autonomous driving behaviors across L2-L4 systems. Own data curation, training, evaluation, experimentation, and on-road validation. Improve model performance through iterative training cycles while addressing real-world safety, quality, and deployment constraints. The role may involve reinforcement learning, transformer networks, automotive systems, model compression, and embedded deployment.

Own an AI investing product vertical end-to-end, including strategy, roadmap, technical design, delivery, metrics, and launch. Partner with engineering on APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling. Embed regulatory requirements across KYC, monitoring, limits, privacy, and licensing. Apply AI with evaluation, safeguards, human oversight, and reliability controls while aligning compliance, operations, finance, treasury, and design.

Owns an AI neobank product vertical end-to-end, including strategy, roadmap, technical design, delivery, regulatory requirements, AI evaluation, reliability, and launch. Partners with engineering, compliance, operations, treasury, finance, and design across payments, remittance, insurance, wealth, and trading products. Must balance correctness, scalability, security, user experience, cost, speed, and regulatory constraints in high-consequence financial systems.

Lead design, development, testing, release, and production support for Equity Derivatives workflows. Own production outcomes, incident triage, stakeholder communication, UAT, architecture and observability. Drive adoption of AI-assisted engineering practices, maintain release discipline, gather telemetry for stability metrics, and improve system reliability and coding hygiene through cross-functional collaboration.