Top Python Jobs in London

Design, build, and lead development of AppGlass features; develop AI-first tooling and automation; implement enterprise integrations and deployments across Kubernetes and cloud; and improve reliability, testing, performance, and security for production use.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

The Machine Learning Engineer will research algorithms for networking, develop ML infrastructure, maintain documentation, and assist in technical communication.

Develop and evaluate machine learning models linking DNA sequence and genetic variation to molecular phenotypes and disease. Build sequence-to-function models (variant effect and regulatory activity prediction), integrate sequence representations with transcriptomic and epigenomic data, collaborate with experimental teams to validate hypotheses, communicate results, and contribute to publications and documentation.

Lead and grow full-stack teams owning Diagnose & Prescribe: build AI-heavy services (RAG, LLM scoring), prescription and eligibility logic, recommendations and onboarding, migrate monolith to services, stay hands-on in design and code, ensure engineering standards, observability, and predictable delivery while partnering with Product, Design, and GTM.

The Senior Machine Learning Engineer will develop production-ready trading models, design tooling and frameworks, and optimize performance using Python and modern ML libraries.

The role focuses on building product reports for the Connected Store, managing ETL processes, utilizing BI tools, monitoring KPIs, and collaborating with teams.

The Research Engineer will develop innovative generative 3D machine learning methods, train and optimize models, and refine 3D content generation processes, focusing on geometric accuracy and integrating physics-aware capabilities.

The Software Engineer Consultant will design and deliver secure software for UK Defence, collaborating with stakeholders and applying secure development practices.

Design and build rules-based indices and custom benchmarks, perform quantitative backtesting, author methodology rulebooks, engage clients, and coordinate cross-functional operational integration to launch tradable index products.

Lead design and evolution of core analytical data models, define analytics engineering standards, build and optimize ETL/ELT pipelines, own the BI/semantic layer (Looker/LookML), drive data quality and governance, partner with cross-functional teams, and mentor analytics engineering across the company to enable reliable, scalable decision-making and experimentation.

As a Senior Credit Risk Data Scientist, you'll lead credit risk modeling, develop data-driven lending strategies, and ensure statistical rigor in model performance while writing production-grade code for underwriting systems.

The Pricing Actuary will handle actuarial pricing for Political portfolios, support broker negotiations, and develop pricing capabilities. They will collaborate with the Actuarial team and monitor portfolio performance, leveraging strong IT and technical pricing skills.

This role involves conducting risk and capital analysis for Lloyd's syndicates, focusing on regulatory and economic capital requirements, model development, and stakeholder communication.

Lead technical deal cycles for enterprise and commercial accounts: perform technical discovery, design solutions, build POCs and AI-driven workflows, configure integrations and APIs, ensure security/compliance, and advocate product feedback to internal teams.

As a Principal Applied Data Scientist, lead the development of scalable AI solutions, focusing on NLP and LLMs, while collaborating with teams and ensuring production standards.

Lead product strategy and roadmap for Equity and Multi‑Asset indices and an in‑house AI tool. Gather user feedback, prioritise features, work with research developers to ship, run market analysis, engage clients, and partner with Sales and Marketing to drive positioning and revenue.

Partner with stakeholders to discover AI use cases, configure enterprise GenAI tools, build and maintain high-quality prompts, contexts, and RAG/document-intelligence pipelines, evaluate and improve AI outputs, create reusable playbooks/templates, and enable teams on governed, production-ready AI workflows.

Lead DevOps pipeline design and implementation, advise clients on DevOps best practices, establish DevOps functions, and engage stakeholders to ensure seamless delivery across projects.

The Staff Analytics Platform Engineer will lead the architecture and development of a scalable analytics platform, focusing on Snowflake, ingestion pipelines, and technical mentorship.

Build and maintain low latency trading services using Rust, ensuring efficient order management and performance optimization in a high-frequency trading environment.

Lead portfolio management for a growing unsecured lending product (Flex), building forecasting and monitoring frameworks, risk-based pricing, lifecycle engagement and loss-mitigation strategies. Manage and grow a team of credit analysts, collaborate across product squads, and partner with Legal, Compliance and 2nd Line Risk to scale the portfolio safely while driving commercial outcomes.

Four-month, full-time research fellowship focused on ML systems and performance. Fellows build and evaluate infrastructure-heavy projects (e.g., CPU simulators, accelerator backends, large-scale distributed training), use external models/APIs, receive mentorship, funding for compute, and aim to produce public research outputs.

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

The Staff Backend Engineer will lead the technical vision for Depop's data platform, focusing on data ingestion, scalability, observability, and architecting data solutions while mentoring team members.