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# Senior Research Engineer Machine Learning, AI for Science

- **Company:** Microsoft
- **Location:** Cambridge, UK
- **Experience:** Expert
- **Salary:** £74,700.0 - £122,600.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Software Quality, Collaborative Software, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, Graphics Processing Unit (GPU), Pytorch, Agentic-AI, Information Technology, Machine Learning Operations, Data Pipelines
- **Published:** November 3, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5951522468

## About the Role

* Completed MSc in computer science, machine learning, AI or a related area
 * Proficiency in collaborative software engineering in Python
 * In-depth understanding of machine learning frameworks such as PyTorch and/or Jax
 * Experience in designing, developing and deploying ML systems
 * Experience with agentic software development
 * Experience building and optimizing distributed systems and large-data applications, including those using tensor accelerators or GPUs.
 * Ability to work in an interdisciplinary collaborative environment, through effective communication of technical concepts to non-experts from different technical backgrounds

Qualifications that are considered a plus, but are not strictly required:

 * PhD degree in computer science, machine learning, AI or a related field, or comparable industry experience in working with machine learning and large datasets.
 * Experience working with major cloud platforms and/or HPC
 * Experience developing high-performance scientific software.
 * Experience designing or developing agentic frameworks, orchestration systems, or infrastructure for building and running AI agents.

## Description

* Develop and maintain efficient, reliable, and scalable infrastructure for large-scale machine learning training, evaluation, and benchmarking.
 * Architect, design, and implement large-scale data pipelines for heterogeneous scientific data, supporting efficient model training, experimentation, evaluation, and benchmarking.
 * Build and optimize distributed data processing, model training, evaluation, and benchmarking pipelines.
 * Develop software and agentic workflows to automate and improve model development, training, evaluation, and benchmarking.
 * Continuous monitoring and improvement of the stability of data, training, evaluation and benchmarking workflows
 * Work cross-functionally with machine learning researchers, engineers, and researchers from the natural sciences.
 * Maintain high standards in code quality and software design.
 * Document and share practices across the organization.