Nationwide Building Society★3.7 (1.1k)· London

You will work with Python, Azure cloud, machine learning, deep learning (transformers, autoencoders), large language models, Generative AI, Natural Language Processing, and prompt engineering. As part of Nationwide’s AI Centre of Excellence, you will build scalable classifiers, predictive models, and GenAI applications to optimize business decisions and automate processes.

Wells Fargo★3.6 (48k)· London

You'll work with Python, SQL, cloud and DevOps practices, AI/ML and Large Language Models, plus Axiom ControllerView on the Enterprise Functions Technology team. The role enhances and supports regulatory reporting platforms used by Wells Fargo's Finance Controllers organization.

Anaplan★2.7 (15)· London

Develop ML models (LightGBM, XGBoost, PyTorch) and pipelines using Python, Pandas, SQL, dbt, Snowflake, MLflow, and Optuna on AWS/GCP/Azure. The role designs and deploys custom analytic “engines” that integrate into Anaplan’s platform to solve enterprise business problems like demand sensing and time-series forecasting. No team size is stated.

Work in Python to develop quantitative research and trading frameworks, automate data preparation, and productionise trading strategies. You’ll collaborate with Quantitative Researchers and Portfolio Managers to enhance the research platform supporting QRT’s global quantitative investment strategies.

You will work with Snowflake, SQL, Python/Java/R, REST APIs, JSON/XML, and dbt. The Security Data Analytics team builds data pipelines, models, and automation to turn security signals into analytics-ready data for Information Security teams. This is an individual-contributor backend data engineering role.

Wells Fargo★3.6 (48k)· London

Principal Engineer working with Java, Python, C++, FIX protocol, and low-latency systems within Wells Fargo Securities’ Electronic Trading Technology. The role builds cross-asset electronic trading platforms for Rates, including pricing, market making, and order routing, and defines AI-assisted engineering practices using LLMs and Model Context Protocol (MCP).

You will work with Python, TensorFlow, PyTorch, Spark, Ray, AWS/GCP, Graph Neural Networks, knowledge graphs, LLMs, and Agentic AI to lead machine learning strategies for Tripadvisor’s core discovery engine. The role focuses on building and deploying multi-objective ranking, recommendation, and user modeling systems that power search and itinerary planning for millions of users. As Principal Scientist, you will guide technical roadmap, mentor senior scientists, and bridge research with scalable production systems.

Build and optimize data pipelines using Microsoft Azure, ETL processes, and orchestration to integrate structured and unstructured data. This role is within the UK National Audit Office's Digital Services team, supporting data ingestion, storage, and access to underpin data-driven insights and automation.

You will work across the full model lifecycle, training and fine-tuning PyTorch-based diffusion/DiT models and using diffusers, transformers, torch.distributed, torchrun, Accelerate, and PEFT/LoRA, with Hugging Face datasets and ffmpeg for data. The team builds and scales generative video models for VEED's AI video creation platform, most recently releasing Fabric, a DiT-based speech-to-video model.

The AA★3.1 (797)· London

Design, build, and operate production-ready AI solutions using Python, Generative AI, LLMs, agentic systems, and RAG. As part of a newly established AI function at The AA, you will develop reliable backend services, APIs, and cloud-native applications.

Wise★3.5 (42)· London

Work with Python, deep learning image models, and large image/tabular datasets to train and manage machine learning models. You will join the KYC Global and Onboarding Team, building Document verification systems that mitigate risk during customer onboarding.

You will work with Python, Snowflake, dbt, Apache Iceberg, AWS (Glue, Lambda, Step Functions, ECS, Lake Formation, EMR, Athena), and SQL. The role involves architecting and building data platforms, implementing robust data pipelines, and managing metadata tools.

You'll work with Python, PyTorch, SGLang, LLMs, embeddings/reranking models, PEFT/LoRA, RAG, Docker, microservices, and cloud infrastructure. The team builds AI-powered applications transforming specialized financial markets, developing and deploying LLM solutions with significant autonomy. You'll join a small, experienced engineering team collaborating directly with founders and domain experts.

You will research, design, and develop deep learning models using transformer architectures, PyTorch, TensorFlow, and Python, plus AWS SageMaker and S3. The team builds foundation models from large-scale transactional and behavioural datasets to power customer intelligence for a major UK bank.

Develop and extend investment strategy architecture using Python and Django, implementing and backtesting quantitative trading strategies while exploring machine learning for parameter optimisation. The role bridges software engineering and quantitative research, collaborating with a small engineering team to deliver production-ready solutions.

Durlston Partners· London

Python, Pandas, NumPy, SQL, SQLAlchemy, version control, testing, and CI for building and maintaining a research environment for diversified systematic strategies in commodities. Projects include fixed income pricing, data acquisition, order execution, and monitoring tools for a global investment management fund.

You’ll work with Microsoft Fabric, Python, Power BI, DAX, semantic models, Git, and AI-assisted development tools. You’ll build and optimize data ingestion pipelines, website/marketing analytics, returns reporting, and the wider analytics platform for digital, commercial, and operational teams. No team size is stated.

As a founding data engineer at Squid’s London startup, you will build production data pipelines and DAG-based orchestration using Python and SQL, designing scalable data models and storage for a versioned, auditable grid modeling platform. The team creates an agentic modeling platform enabling humans and AI agents to modernize national power systems, working with industry leaders like National Grid.

This role involves maintaining and building data pipelines using SQL, Python/Java, and cloud tools like Google Cloud Platform, BigQuery, Airflow, dbt, and Apache Spark. The team supports data analytics and reporting for a media company's portfolio. It focuses on integrating data from web analytics, CMS, subscription, and ad tech sources to enable business intelligence.

LiveScore Group★3.4 (14)· London

You will work with Python (pandas, numpy), SQL, and MMM frameworks (Meta’s Robyn, Google’s Meridian, PyMC). The Marketing Analytics team builds and refines statistical models to measure ROI and optimize marketing spend for LiveScore Group’s sports brands.

Microsoft★4.2 (9k)· London

Work with Azure SQL, Azure Database for PostgreSQL, Azure Cosmos DB, Microsoft Fabric (Lakehouse, Data Factory, Spark, Power BI, Real-Time Intelligence), and Copilot to help organisations modernise their data estates and ground AI on their data. As a Solution Engineer, you lead strategic customer engagements, design architectures, and mentor peers within Microsoft’s Data & AI team.

Salesforce★4.2 (1.5k)· London

Works with Salesforce Agentforce, Data360, MCP, A2A

Use Python (strong knowledge), Hadoop, Spark, or similar big data tools, cloud platforms (AWS, GCP, Azure) and data warehousing solutions to design and implement models that forecast and manage liquidity for Wise’s Treasury team, ensuring enough cash is in the right place at the right time while controlling liquidity risk.

Capgemini★3.7 (11k)· London

Works with Neo4j, Amazon Neptune, Azure AI Search, Elasticsearch, Pinecone, Python, Azure, AWS, Kubernetes, Docker, and CI/CD. Designs and implements an enterprise knowledge platform using knowledge graphs, RAG, and GraphRAG to enable AI agents across Insurance, Payments, and Healthcare domains. Leads platform strategy and cross-functional engineering teams.

Capgemini★3.7 (11k)· London

Designs and implements enterprise knowledge platforms for AI agents across Insurance, Payments, and Healthcare domains, using graph databases (Neo4j, Amazon Neptune, TigerGraph, ArangoDB), retrieval technologies (Azure AI Search, Elasticsearch, Pinecone, Weaviate, Qdrant), and Python on cloud-native stacks (Azure, AWS, Kubernetes, Docker).