Top Machine Learning Jobs in London

Design and deliver production-ready AI, NLP, and generative-AI solutions (including LLMs and RAG) for knowledge discovery. Build retrieval, ranking, recommendation, and decision-support systems, integrate scientific metadata and ontologies, establish evaluation and monitoring, write production Python code, and provide technical leadership and mentoring.

Design, implement, and optimize a low-latency high-frequency trading platform for cryptocurrency markets; collect market data, disseminate orders, analyze throughput/latency, build performance tools, and develop access to historical data and simulation systems.

As Director of AV Product Engineering, you will manage product engineering, develop autonomous vehicle capabilities, and lead multiple teams to ensure alignment with company objectives while fostering technical growth and innovation.

The AI Business Development Specialist will identify business opportunities, build client relationships, and promote AI solutions in various sectors, with a focus on the MENA region.

As a Regional Research Economist, you will analyze AI's economic impacts, collaborate with entities, and develop methodologies for economic measurement, influencing policy and business decisions.

As a Machine Learning Engineer, you'll train and deploy ML models for enterprise clients, work on deep learning solutions, and interface with product teams to enhance AI applications.

Lead GTM operations for EMEA/APAC by partnering with sales, finance, marketing and customer success. Build scalable commercial strategy, forecasting, territory design, and reporting. Leverage Salesforce, Snowflake and AI/ML to improve forecast accuracy, pipeline hygiene, and sales productivity while implementing standardized sales methodologies and KPIs.

The role involves designing, building, and deploying ML systems, supporting stakeholders, and operationalizing AI applications in Defence.

Leads Android software development across design, coding, testing, deployment, debugging, and production support. Develops scalable and secure mobile solutions using Kotlin and modern architectures, integrates AI and ML capabilities, conducts code reviews, supports CI/CD and DevOps activities, monitors performance, and promotes engineering best practices. Collaborates with cross-functional teams, owns feature delivery, mentors peers, and contributes to Walmart+ customer-focused platform improvements.

Lead development of large-scale machine learning models (generative AI/large models), design experiments to validate performance and business impact, collaborate with engineers to productionize solutions, and communicate findings to technical and non-technical stakeholders.

Design and implement advanced computer-vision and ML algorithms to detect defects and evaluate video quality. Optimize models for low latency and cost-efficient deployment, run model training/experimentation, and apply software engineering and statistical analysis to ensure scalable, near real-time performance.

Lead adoption and scaling of Generative AI and ML within Treasury, design and deliver AI/ML solutions (liquidity forecasting, capital optimisation, risk), migrate systems to secure scalable cloud, champion data governance, ensure automated testing, and mentor engineers while aligning stakeholders and driving innovation.

Develop and deploy machine learning and statistical models that solve business problems and create measurable impact. Responsibilities include exploratory data analysis, feature engineering, experimentation, model optimization, explainability, MLOps, prompt engineering, and cloud-based data collaboration. The role involves working with clients and stakeholders, communicating insights through visualization and storytelling, and contributing to scalable analytics, Generative AI, and data science solutions.

Own the full business development and sales cycle across Europe, including prospecting, partnerships, stakeholder engagement, negotiation, contracting, and closing against revenue targets. Develop channel opportunities, support European go-to-market strategies, communicate customer ROI, attend industry events, and collaborate with Marketing, Product, technology partners, and agencies. Track funnel performance, improve acquisition processes, and share market insights while engaging apparel brands and retailers.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Lead statistical design and analysis for Phase I-IV clinical studies, develop protocols and SAPs, perform efficacy and exploratory analyses, run simulations, apply advanced methods (mixed models, longitudinal), oversee datasets (ADaM/CDISC), use R and SAS for analyses, and provide strategic statistical guidance to cross-functional teams supporting regulatory submissions.

Provide senior-level data engineering and analytics consultancy: capture requirements, design data platform and BI/advanced analytics solutions, implement ETL and data transformation, and deliver clear reports and dashboards using Microsoft and cloud data technologies.

The Staff Machine Learning Scientist will design, develop, and maintain machine learning models for Depop's fashion resale platform, leading technical initiatives and collaborating across teams to drive innovation in ML solutions.

Lead and manage data science teams in optimizing logistics through ML and operations research, ensuring research translates into production-scale solutions.

Build and maintain Python-based supply and demand models and price forecasts for crude and refined products in EMEA. Collect and manipulate large datasets, perform quantitative and qualitative analysis, produce client-facing reports, Power BI dashboards and PowerPoint presentations, and support business development and stakeholder engagement.

Lead Depop's Ranking team to define and deliver ML-driven ranking across Search, Homepage and Recommendations. Set vision and prioritise models, features, infrastructure and experimentation. Partner with ML Engineers, Applied Scientists and product teams to deploy, evaluate and iterate production ranking systems that improve engagement, purchases and marketplace outcomes.

12-week summer internship for PhD students to conduct quantitative research in trading and systematic investing. Work includes analysing large financial datasets, developing and backtesting quantitative models using Python and statistical libraries, validating strategies, maintaining research infrastructure, and presenting findings to senior researchers and trading teams.

Lead cross-functional teams of machine learning engineers and data scientists, driving technical direction and fostering a collaborative environment to develop high-quality ML-driven capabilities.

The Senior FinCrime Product Owner will design and implement a financial crime prevention product, develop strategy, and lead product delivery at Deblock.

Design and lead Oversee's sales enablement strategy, building onboarding, certification, role-based learning, sales methodology, playbooks, and cross-functional engagement. Partner with Product, Marketing, Engineering, and RevOps to enable enterprise sales of AI and automation solutions, measure enablement impact, and improve win rates, ramp time, and sales cycle efficiency.