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# Data Engineer, AI Infra

- **Company:** Microsoft
- **Location:** Headley Heath, United Kingdom
- **Experience:** Expert
- **Salary:** £93,500 - £161,800
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Big Data, Information Engineering, Data Infrastructure, Data Systems, Distributed Computing Environment, Distributed Systems, Software Engineering, Apache Spark, AI Platforms, Information Technology, Data Pipelines
- **Published:** November 11, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5961474565

## About the Role

* Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent experience.
 * 6+ years of experience in software engineering, data engineering, data science, or analytics.

Preferred Qualifications

 * Experience building large-scale data platforms or analytics systems.
 * Experience working with AI/ML data, infrastructure, or distributed systems.

Data Engineering IC5 - The typical base pay range for this role across United Kingdom is £ 93,500.00 - £ 161,800.00 per year. Certain roles may be eligible for benefits and other compensation

## Description

Help build the world's most advanced multimodal dataset at Microsoft AI

We are on a mission to create the largest and most advanced multimodal dataset in the world. This dataset, spanning all modalities from across the web and beyond, will power the training of the world's most capable AI frontier models, pushing the boundaries of scale, performance, and product deployment.

The AI Data Infra team at Microsoft AI is responsible for building data infrastructure to help MAI teams to generate the biggest and best training dataset. Our work involves data pipelines, Spark, Ray, Vector Databases, and all other aspects of data infra.
Responsibilities

 * Build and scale data pipelines that power training and evaluation of multimodal AI models across text, image, audio, and video.
 * Develop and operate large-scale data infrastructure and platforms for processing and managing petabyte-scale datasets.
 * Own critical data systems and distributed computing frameworks that support model development and research.
 * Partner with model training teams to improve data quality, curation, and experimentation that drives model performance.