Build and maintain ETL/ELT pipelines using Python, SQL, BigQuery, REST APIs, and GCP tooling (Cloud Run, Cloud Scheduler, Secret Manager) to sync data between HubSpot, a People Planner, and Notion. The role owns the flow of operational data (client, revenue, pipeline) into the data warehouse and reverse ETL back into business systems used by Finance, Ops, and Client Leadership teams.

You will work with Python, TensorFlow or PyTorch, and large language models, building custom deep-learning models and training/fine-tuning LLMs on a proprietary dataset. Kangal AI builds frontier AI for governments, national security, and private corporations. As a founding hire, you will own the model stack and scale the research into a team you lead.

You will work with Python, deep learning image models, and large image/tabular datasets. The KYC Global and Onboarding Team performs Know Your Customer checks during consumer and business onboarding, implementing machine learning models to support document verification.

Use SQL, Python, and AI tools to extract and analyze large data sets on private markets and alternative investments. Work with Product and Machine Learning teams to develop data extraction from unstructured text like PDFs. The company automates alternative investment data processes for institutional investors.

Work with Python, containerized pipelines, and cheminformatics representations (SMILES, InChIKey, molecular fingerprints) to build ML evaluation benchmarks for molecular structure prediction from mass spectrometry data. You will design adversarial checks, leakage-safe datasets, and repeatable pipelines that measure model quality for an applied AI drug discovery company.

Use Python, SQL, Databricks and Spark to develop predictive models, statistical analyses and A/B tests for Primark’s Data Science & Measurement team. You will build reusable, scalable data-science solutions that drive commercial decisions and business growth.

Work with Python, PyTorch, Scikit-learn, Hugging Face, MLFlow, AzureML, and CI/CD/MLOps tools like Azure DevOps, Docker, and Kubernetes. The Applied AI team builds production LLM-powered systems, agentic AI, and ML pipelines for Glasswall’s cybersecurity solutions. No team size is stated.

You will work with Go, Rust, Python, Java, C++, headless browsers (Chrome DevTools Protocol, Playwright, Puppeteer), proxy systems, and distributed systems. The team builds infrastructure for massive web data acquisition and operates a distributed crawler via the Grass bandwidth-sharing network, powering dataset creation for AI model training.

Work with Python, Java, or C++, and AI frameworks like TensorFlow, Keras, or PyTorch to develop and implement AI models and algorithms. You will solve real-world problems, integrate AI into existing systems, and research performance enhancements for cutting-edge AI applications at Demo Co.

Millennium Management★3.8 (21)· London

Use Python, C++, and tools like Docker, Kubernetes, Ceph, MongoDB, Kafka, Numpy, Polars, Scikit-learn, and PyTorch to build research tooling and real-time event-driven systems for alpha discovery, signal computation, and trade execution within a small systematic trading team.

dunnhumby★3.9 (87)· London

Python, APIs, microservices, cloud (AWS, GCP, or Azure), large-scale data systems and pipelines, front-end work, and AI-assisted engineering tooling. The role builds dna, a collaborative Retail and Commerce Media service that unifies fragmented market data for retailers, brands, and agencies. You embed with clients to ship production-ready code against real data and workflows.

This role involves building data pipelines, Snowflake data models, and automation using SQL, Python, Java, or R, with practices like version control and CI. The engineer joins Sony Interactive Entertainment's Security Data Analytics team, supporting Information Security with analytics-ready data and AI-enabled workflows.

You will work with Kotlin, Java, Python, or TypeScript on AWS (Lambda, ECS, EKS), using AWS CDK, Terraform, Kubernetes, Datadog, Kafka, Flink, Redis/Valkey, RDS, and Redshift. This role is a 50/50 backend and reliability engineering split on Sparta’s commodity trading platform, building distributed services for real-time market data and owning production observability and resilience.

You'll design, build, and operate production AI/ML applications using Python, Generative AI, LLMs, RAG, agentic systems, backend services, APIs, cloud-native apps, CI/CD, containerisation, MLOps, and LLMOps. The role sits in The AA's newly established AI function creating intelligent products for members, customers, and frontline teams.

WPP★3.7 (378)· London

You will work with Java 17+, Spring Boot, Azure (containerisation and serverless), Terraform, and Python to develop enterprise-quality optimisation and ML solutions. This role leads the creation of a new platform for a major field operations provider, building logistics optimisation products with Satalia’s data science and engineering teams.

Millennium Management★3.8 (21)· London

Work with Python, Java, or Rust on microservices in AWS, GCP, or another public cloud. You'll architect and develop enterprise-scale data ingestion and distribution frameworks for intraday risk, trade, and portfolio management, partnering closely with portfolio managers and risk teams.

You will work with Python, FastAPI/Django, React, and AI tools like Claude Code, Enterprise Claude, Gemini, or Copilot. As a senior technical leader on the Real Assets and Real Estate technology team, you will build platforms supporting the end-to-end asset lifecycle for Macquarie Asset Management’s private markets business.

You will work with C++ and Python to build and maintain core vanilla pricing models for commodities. The Commodities Risk & Pricing team owns the risk and pricing libraries used firm-wide, partnering directly with traders and structurers on live transactions in a front-office role.

You will work with Python, databases, APIs, and microservices architecture. You will build and maintain software tools for risk analysis and reporting, specifically supporting the Fixed Income function of a global quantitative investment manager.

Work with Python, JavaScript, SQL, Kafka, AWS/Azure, PostgreSQL, SQL Server, ClickHouse, MongoDB, and optionally Kubernetes to architect real-time, high-performance trading systems for energy and commodities markets, integrating market data, risk, and back-office platforms.

You’ll build and maintain tools and frameworks in Python for trading risk analysis and reporting, processing large volumes of market and trade data across fixed income products. You will work with databases, APIs, and microservices architecture alongside Fixed Income Risk Managers, Traders, and Researchers on the Risk team at a tech-driven quant hedge fund.

You will use Python for object-oriented design, performance optimisation, and testing of pricing, risk, and P&L analytics libraries, along with SQL and data-intensive systems. As a Senior Quant Developer at Quanteam (a consulting firm), you will build and maintain front-office pricing and analytics libraries for investment banking clients, partnering with traders, quants, and risk teams.

Work with Python, C++ (preferred), tree-based models, deep learning, and NLP to develop predictive features from HFT/intraday market data. As part of a multi-strategy hedge fund’s Equity/Futures team, you will design ML-driven alphas for cash equities and futures.

Use Python, C++, distributed computing, machine learning, and HPC to build tools for quant research and signal generation. This team constructs the infrastructure powering trading signal research, letting researchers and traders test and scale strategies across global markets.

You will work with Python, RPA tools, SQL, and database tooling, along with cloud services. The role involves designing and building regulatory reporting systems that underpin compliance functions, helping to manage risk in a regulated financial environment.