Senior Research Engineer, MSR AI for Science

We are on the cusp of a new frontier in which machine learning and artificial intelligence are transforming scientific discovery. We seek to drive major advances in sciences with machine learning, with a focus on ‘’ scenarios. Through these advances, we aim to empower real-world impact on some of the most pressing problems facing society including climate change, green energy, sustainable materials, and the discovery of new drugs. 

AI for Science is a new global team in Microsoft Research focussing on the opportunity to transform scientific modelling and discovery through large-scale deep learning.  We aim to advance this frontier and to drive real-world impact at a global scale. The AI for Science team encompasses multiple disciplines across machine learning, engineering, and the natural sciences and spans several geographical sites in Europe, Asia, and the US. 

The field of machine learning has evolved significantly in recent years, with many of the most impactful contributions coming from larger teams of people collaborating closely on well-defined and ambitious goals.  Furthermore, AI for Science in particular requires a combination of machine learning, engineering, and natural sciences, which again emphasises the importance of collaboration and teamwork. 

We are seeking a highly motivated Research Infrastructure Engineer who is skilled in cloud infrastructure and software engineering, with a strong affinity for science and machine learning. The ideal candidate will have a deep understanding of cloud infrastructure and be proficient in the design, planning, and implementation of tools and technology to support AI-driven scientific research.

To Apply: If you are excited about making a meaningful impact in AI-driven scientific research and possess the skills and experience outlined above, we encourage you to submit your resume and a cover letter detailing your interest and qualifications for the role. We look forward to reviewing your application! 

• Architect, design, and implement scalable and robust solutions for machine learning and scientific research involving large volumes of heterogeneous data. 
• Build and maintain model evaluation pipelines and web apps. 
• Design, implement, and support tools and technologies that enable the development, deployment, and scaling of machine learning applications. 
• Collaborate with cross-functional teams, including scientists, researchers, and software engineers. 
• Document and share best practices across the organization. 
• Maintain the highest standards in code quality and software design.