Senior Machine Learning Engineer working with C++, CUDA, Python, and PyTorch. The role is within an AI technology company developing next-generation machine learning solutions for complex real-world applications.

You will work with Python and SQL to design, build, and maintain data-driven applications. The role focuses on data engineering and integrating with a modern data lake platform.

You will use Python, SQL, and Databricks to design and build enterprise data platforms, developing ETL/ELT pipelines and data integration solutions within a lakehouse architecture.

JPMorgan★3.9 (20k)· London

Principal Data Engineer at JPMorganChase, working with SQL, Python, Spark, cloud platforms, Databricks, Apache Iceberg, and distributed frameworks like Hadoop and Kafka. The role designs and builds scalable data warehouse solutions, data pipelines, and ETL processes for the firm’s portfolios. Mentors junior engineers and partners with architects on delivery roadmaps.

WTW★3.6 (1.7k)· London

This role shapes WTW’s enterprise-wide AI engineering capability, defining technical direction for production-grade generative AI and agentic systems. You will work with Microsoft Foundry, Copilot Studio, Lyzr, Anthropic Claude, Moveworks, LLM/agent frameworks, and Model Context Protocol while building RAG pipelines, LLMOps practices, and governed integration patterns.

Wise★3.5 (42)· London

Work with Python, big data tools like Hadoop or Spark, and cloud platforms such as AWS, GCP, or Azure, collaborating with engineers on the Treasury team. Build and refine models to forecast and manage liquidity, balancing sufficiency and risk for Wise’s multi-region money movement products.

LSEG★3.7 (403)· London

Work with Azure, AWS, Terraform, Bicep, CloudFormation, Ansible, Docker, Kubernetes, Python, Bash, PowerShell, Go, CI/CD, and generative AI tools. Build scalable AI products and platforms by owning cloud infrastructure, delivery pipelines, and operational engineering. This individual contributor role provides functional leadership, guiding engineers without direct reports.

Wise★3.5 (42)· London

You’ll use Python, SQL, predictive ML models, recommendation systems, and A/B testing to own production-level model pipelines. Wise is a global fintech moving money across borders. In this IC role on the CRM Tribe, you’ll develop learning systems that recommend the right product to the right customer at the right moment.

Wise★3.5 (42)· London

A Lead Data Scientist will work with Python, SQL, and machine learning models (classification, propensity, churn, next-best-action) using gradient boosting, neural networks, or linear models. They will design data pipelines and ML infrastructure for Wise’s Marketing CRM team, building recommendation systems to personalize customer communications. This individual-contributor role involves developing production models, running A/B tests, and influencing product strategy.

Citi★3.9 (21k)· London

You will work with C++, C#, .NET, Java, Python, kdb, and SQL to develop analytics libraries and pricing models using Monte Carlo methods and PDE solvers. The role focuses on creating quantitative models for Citi’s cross-asset XVA and capital analytics, supporting trading business pricing and risk-management. You will collaborate with Traders, Structurers, and technology professionals.

You'll work with Python, NumPy, SciPy, Pandas, PyTorch, JAX, and deep learning frameworks to build models using operator learning, geometric deep learning, or generative models. PhysicsX is building an AI-driven simulation software stack for multi-physics engineering and manufacturing across aerospace, energy, and other advanced industries.

You'll work with Python, SQL, Snowflake, and dbt to transform and model data, build data workflows, and automate processes. This role sits on the Python Infrastructure team at Lendable, a fintech building technology to help people get credit and save money.

JPMorgan★3.9 (20k)· London

Work with KDB, Q, AWS, and optionally Terraform, Kubernetes, Python, C, C++, or Java. Develop and maintain applications storing large Options data sets for real-time and historical access within JPMorgan’s Equities Trading Data & Analytics team, migrating to AWS and re-architecting systems.

You'll work with Python, SQL, Snowflake, and dbt to transform and model data, build data workflows, and automate processes. The role sits on the Python Infrastructure team at Lendable, a fintech company rebuilding consumer finance products like loans, credit cards, and car finance.

Work with Snowflake, dbt, Python, and SQL to transform and model data, build data workflows, and automate processes. You’ll join the Python Infrastructure team at Lendable, a fintech building credit and savings products.

Wise★3.5 (42)· London

You will work with Java, Spring Boot, AWS, Apache Kafka, and potentially Trino. The Pricing Core team builds and maintains the main pricing platform that enables Wise to price its products, processing over 100 million requests daily across critical paths.

You will work with PyTorch, TensorFlow, deep learning models, recommendation and retrieval systems, and MLOps practices on cloud infrastructure. Joining ASOS’s Outfits Discovery team, you will build and operate large-scale production ML systems that power personalised outfit recommendations and AI-driven fashion discovery for millions of customers.

Design and build scalable data pipelines using Python, Scala, Spark, PySpark, Azure Data Factory, Databricks, and Delta Lake. You’ll develop reusable platform components and templates that enable data scientists and engineers across product teams (Forecasting, Recommendations, Marketing, etc.) to deliver high-quality, production-ready data solutions.

Allianz★3.8 (3.4k)· London

You'll build production data/ML systems using Python, Docker, Kubernetes, Terraform, Azure, Azure DevOps or GitHub Actions, plus SQL, PySpark, dbt, and Microsoft Fabric. Joining a small agile team, you'll deliver real-time API endpoints serving 10M+ daily requests and evolve cloud infrastructure and

Develop novel machine-learning methods for molecular and protein discovery, using Python, PyTorch, graph neural networks, transformers, and diffusion models. Working at a life science technology company, the role involves research with engineering proximity, translating ideas into practical tools for scientific discovery. Preferred experience includes 3D and geometric deep learning, molecular generation, and protein modelling.

You will work with Python, PyTorch or JAX, Docker, CI/CD, and cloud platforms to deploy ML models for protein design. The team builds scalable infrastructure for training, inference, and evaluation across molecular and protein datasets, taking research prototypes into production.

Barings★3.8 (43)· London

This Senior Associate role on the Quantitative & Risk Analytics team uses Python, SQL, Excel, and PowerBI to build and maintain quantitative ESG overlays for Barings’ public and private portfolios. You will produce ESG analytics and reporting, develop portfolio overlay strategies for decarbonization mandates, and partner with data vendors and internal teams to improve data quality.

Imperial College London★4.2 (238)· London

You will work with R, Python, Stata, or SAS; SQL; cloud-based or high-performance computing; and Bayesian methods. The Misra Group uses large-scale real-world datasets to investigate metabolic dysfunction in type 1 diabetes and develop clinical risk prediction models for individualised care.

ZS Associates★3.5 (460)· London

Work with Python, R, SQL, or cloud technologies to analyze scientific, clinical and commercial data and support drug discovery decisions. You help pharmaceutical and biotech clients design R&D strategies, accelerate innovation and improve drug development processes as part of ZS's R&D and Drug Development team.

In this Senior Data Engineer role on the Scalable Growth team, you will work with Python, dbt, Airflow, Kafka, Trino, and Iceberg to build high-volume streaming and batch data pipelines. The Marketing Platform team owns the data infrastructure, internal tools, and third-party integrations (e.g., Meta, Google) that power Wise’s global customer acquisition channels.