Top Machine Learning Jobs in London

Senior front-end engineer on an agile Equities Sales & Trading team delivering secure, scalable cloud-native applications. Provides technical leadership, writes and reviews production code, drives design decisions, optimizes UI performance for high-frequency data, and advocates firmwide engineering practices and tools.

Lead development of cloud-native Python applications for global commodities trading desks. Design, build, test, and operate resilient, secure production systems; drive automation and CI/CD; evaluate vendor solutions; and mentor engineering communities of practice.

Build AI/ML-driven analytics and automation for CIB Treasury, Securities Services and Payments. Develop scalable data architecture, automate manual processes, create sequential decision-making tools to optimize net interest income, drive projects end-to-end from prototype to production, present commercial recommendations to stakeholders, and collaborate across Quant Research, Treasury, CIO, Technology and CDAO.

Lead development and adoption of information architecture standards, controlled vocabularies, and governed data models. Collaborate with business and technical teams to evaluate technologies, provide technical guidance, influence product design, and standardize operational and analytical data management across the Corporate Investment Bank.

Leads the mid-market business development function, including team building, territory strategy, prospecting, negotiation, closing, pipeline management, and account acquisition and retention across pharma and biotech. Partners with marketing, strategy, and commercial operations on go-to-market execution. Represents the company at industry events and develops relationships with clinical trial sponsors while selling complex AI-powered SaaS solutions.

Leads a 25+ person organization of data scientists, machine learning engineers, and analytics engineers across Identity and Financial Crime. Owns data quality, governance, lineage, observability, fraud and financial-crime models, production performance, monitoring, retraining, and domain data products. Partners with engineering, product, risk, compliance, legal, and central data teams; influences upstream teams, develops managers and senior ICs, handles incidents, and scales hiring and technical strategy.

Build and deploy production machine learning systems for classification, retrieval, ranking, personalization, and evaluation. Develop training and inference pipelines, improve model quality and efficiency, create evaluation datasets and metrics, monitor production performance, debug issues, and ship reliable ML-powered features. The role emphasizes balancing accuracy, relevance, latency, cost, privacy, and reliability while collaborating with engineering and product teams.

Twelve-week machine learning internship focused on implementing research papers, building custom libraries for petabyte-scale model training, optimizing ML operations, profiling architectures, and evaluating emerging ML hardware and software. Interns will collaborate with quantitative researchers and engineers on advanced machine learning and scientific computing challenges.

Own and build Oversee’s product marketing function, including ICP definition, positioning, persona messaging, competitive and win/loss research, product content, sales enablement, and end-to-end product launches. Partner closely with product, sales, growth marketing, and executives to translate technical AI and travel technology capabilities into compelling commercial messaging, establish repeatable GTM processes, and measure launch performance.

Develop statistical and machine learning models using soccer tracking and event data to support player recruitment decisions. Build spatio-temporal models, metrics, analyses, and interpretation tools; validate models; communicate findings to professional club clients; and partner with engineering on cloud deployment and production reliability. The role also includes code review, model maintenance, and advancing analytical methods.

As a Lead AI Engineer, you'll architect and deploy generative AI systems, optimize large models, and lead teams in AI engineering across financial services.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Optimize large-scale ML inference systems for throughput, latency, cold-start time, and cost per token. Tune SGLang, vLLM, and TensorRT-LLM across GPUs and distributed clusters; improve batching, scheduling, KV caching, quantization, weight loading, JIT caching, and multi-node inference. Profile and instrument systems to validate performance gains, eliminate storage and networking bottlenecks, and establish technical direction through reproducible benchmarks and experiments.

Deploy and operate production AI and machine learning architectures, including batch processing systems, real-time microservices, continuous retraining, and observability. Design sustainable machine learning solutions across the full lifecycle using Python, SQL, modern modeling frameworks, cloud platforms, containers, and deployment pipelines. Apply generative AI and LLM tools while communicating technical trade-offs to technical and business stakeholders.

Build and scale a predictive modeling platform supporting multiple problem types and tenants. Develop embedding-based, temporal survival, and gradient-boosted models for social-domain risk prediction. Create training datasets, evaluation pipelines, and production ML infrastructure, while researching and safely deploying emerging ML and LLM techniques. Implement containerized APIs and Kubernetes services, and incorporate responsible AI, ethical, and regulatory considerations throughout development.

Designs scalable AWS cloud, data, and AI solutions for a major aviation operator. Partners with business and technical stakeholders to translate objectives into architectures, leads technical planning and implementation scoping, communicates architectural decisions, promotes emerging technologies, provides cybersecurity guidance, and ensures solution quality, compliance, performance, and alignment with business outcomes.

Build and operate inference services and APIs for production world models. Responsibilities include deploying research models, optimizing GPU utilization, memory, batching, cold starts, reliability, and cost at scale. Collaborate with research and product teams to deliver new AI capabilities and customer-specific configurations. Requires strong Python, production API ownership, containerized GPU services, and hands-on inference performance optimization.

Leads Tide’s global engineering organization for ongoing monitoring, fraud detection, financial crime, sanctions, and regulatory-risk platforms. Owns engineering strategy, shared decisioning capabilities, ML model lifecycle, agentic AI governance, platform scalability, and measurable risk and efficiency outcomes. The role manages distributed engineering, data science, and analytics teams while aligning risk, compliance, operations, payments, product, and technology stakeholders across multiple markets.

Design, build, test, and maintain scalable regulatory reporting applications and data pipelines. Translate regulatory requirements into technical solutions, investigate reporting exceptions, reconcile data, optimize processing frameworks, and support the full software development lifecycle with monitoring and automation.

Lead analytics for the Financial Crime Platform, partner with product and engineering to build metrics, pipelines, visualisations, and features for fraud/AML risk models, drive discovery and data-driven decisions balancing risk and customer experience.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Build and operate high-volume ingestion pipelines for procurement and supply-chain data. Design schemas, catalogs, warehouses, lakehouses, and feature stores; establish data quality, monitoring, observability, governance, and lineage standards. Collaborate with AI, Platform, and Product teams to provision reliable datasets for analytics and machine learning. Own end-to-end data solutions, CI/CD, integrations, and architecture supporting enterprise customers.

Build, test, deploy, and optimize machine learning models, controllers, and infrastructure for autonomous industrial control systems. Collaborate across research, engineering, and customer teams to deliver scalable solutions that improve plant efficiency and reduce carbon emissions. Contribute to ML innovation, software architecture, development tooling, engineering best practices, and end-to-end product delivery.

Lead product marketing for a B2B SaaS productivity platform serving financial services. Own positioning, messaging, sales enablement, competitive intelligence, pricing and packaging, customer communications, and adoption initiatives. Manage and develop one product marketer while partnering closely with Product, Sales, Marketing, and executive leadership. The role is hands-on and offers potential progression into broader marketing leadership.

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.