Top Machine Learning Jobs in London

Lead product ownership for Instruction Capture Client Data Exchange, managing a major institutional portfolio conversion. Translate client requirements into scope and business requirements across SWIFT, XML, FIX and CSV formats. Drive product vision, roadmap, AI-enabled validation and straight-through processing improvements, coordinate cross-functional delivery, manage testing and releases, and align stakeholders and data governance with the Chief Data & Analytics Office.

Lead the development and lifecycle management of ML models for KYC and document verification. Build and maintain Python codebases, manage large image and tabular datasets, iterate on deep learning image models, measure and optimise model performance, research improvements, and mentor junior data scientists while collaborating with cross-functional teams.

Lead the development and lifecycle management of KYC document verification ML systems: train, test, iterate and optimize deep learning image models; manage large image and tabular datasets; maintain Python codebase; collaborate cross-functionally; mentor junior data scientists; research and apply new ML techniques to reduce fraud and improve onboarding experience.

11-week internship performing quantitative research to develop, backtest, and implement automated trading strategies. Translate mathematical models into code, analyze unconventional data sources, conduct statistical research (ML, time-series, NLP), and collaborate with senior researchers to refine trading signals and monetize models.

The intern will work on developing high-performance research platforms, create tools for trading strategies, and collaborate with teams to innovate financial technology.

Conduct original research applying machine learning, deep learning, and NLP to extract patterns from large and unstructured datasets; implement and back-test algorithms in high-quality code; document findings and collaborate with trading teams to deploy research-driven models.

Senior Data Scientist embedded in international teams to ingest, validate, and transform partner oncology data into research-grade datasets. Build and automate scalable pipelines, apply ML/LLM techniques to unstructured EHR data, and collaborate with cross-functional stakeholders to develop and deploy real-world data products.

Build high-performance, production-ready trading and research platforms in small teams. Design systems architectures, develop platforms and web frameworks, apply distributed computing, ML and NLP techniques, and collaborate on complex technical problems.

Design and build reliable, scalable systems for business onboarding and verification across Europe and the UK. Automate information collection and decisioning, create adaptable onboarding journeys, implement controls to mitigate financial crime risk, and own projects end-to-end while collaborating with Product, Compliance, Operations and other engineering teams.

Define and deliver AI-powered analytics and decision-support products. Set strategy and roadmaps, partner with engineering and analytics teams to build LLM/RAG-driven conversational and agentic experiences, translate customer needs into requirements, use data to measure performance, and ensure trust, governance, privacy, and responsible AI practices.

Senior individual contributor providing analytics and decision support for Localization product. Design and lead A/B and causal experiments, build and deploy production ML/NLP models, and develop scalable SQL/Python analytics pipelines and dashboards. Translate multilingual insights for stakeholders, mentor junior staff, and co-own metric frameworks and ML-enabled solutions.

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.

Work on research projects addressing security, privacy, and scalable systems. Tasks include engineering, data analysis, protocol design, open-source development, measurement, and implementation using systems languages. Projects may involve AI, distributed systems, privacy, or trusted execution. Interns matched to research directions, expected to communicate results and collaborate with teammates.

Analyzes adversary intrusions, malware, files, and events to create and improve machine-learning security detections. Reviews false positives and negatives, investigates emerging threats, analyzes data at scale, and coordinates with internal teams on product and service improvements. The role also addresses customer questions about detection-model efficacy and may require on-call coverage several times annually.

Lead strategy and product for AML prevention, building ML/LLM-driven systems to stop regional and hyper-specific financial crime. Collaborate with regulators, law enforcement, and cross-functional teams to define metrics, roadmaps, and automation to maximise prevention while minimising operational cost and customer friction. Mentor and support other product managers and drive delivery across London and Tallinn.

Design, develop, and maintain secure, high-quality production code and system architectures. Troubleshoot complex issues, analyze large datasets for improvements, ensure application resiliency and CI/CD practices, and contribute to engineering communities and inclusive team culture.

Conduct quantitative research and statistical analysis on crypto markets to develop, backtest, and implement automated trading strategies. Work with large and unconventional data sets to build valuation models, refine algorithms, and deploy signals in live trading to drive P&L growth.

Develop, test, and deploy software solutions for automated trading systems in partnership with Quantitative Researchers. Implement high-performance, scalable systems using advanced quantitative techniques, distributed computing, ML/NLP, and strong software engineering practices. Collaborate on priorities, deliver custom solutions, and engage with senior team members during an 11-week internship.

11-week internship building high-performance research platforms and trading tools. Work in small teams on distributed systems, NLP/ML applications, and web frameworks while collaborating with senior engineers.

Lead product efforts to detect money laundering and related fincrime by building hyper-targeted, region-specific detection systems using machine learning and LLMs. Work with compliance, data scientists, engineers, designers and ops to define vision, roadmap, and measurable metrics (AML Recall and SAR Cost) while satisfying global regulatory requirements and improving automation and precision.

Conduct empirical research to discover and validate novel alpha sources using ML, AI, econometrics and large datasets. Build scalable research pipelines, backtest strategies, and partner with PMs and engineers to deploy live systematic equity strategies.

Lead scientific and strategic AI efforts for Vaccines Medical: define AI vision and roadmap, govern responsible AI standards, prioritize and deliver validated AI-enabled solutions, partner cross-functionally, oversee deployment and adoption, manage budget and vendors, and ensure regulatory, ethical, and scientific compliance across global medical affairs.

Design, develop, test, and deploy Python-based solutions for finance regulatory reporting platforms (Axiom ControllerView). Improve data pipelines and ETL processes, collaborate with business and tech partners, identify service-quality improvements, and follow security, compliance, and Agile/DevOps practices.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

Lead bioinformatics strategy and execution across Discovery client engagements. Design and implement computational multi-omics workflows, translate biological questions into analytical frameworks, deliver client-ready insights, mentor junior staff, support business development, and help grow internal bioinformatics capabilities and thought leadership.