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 ‘fifth paradigm’ 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! 

Required

• Master's degree or equivalent work experience in Computer Science, Physics, Engineering, Chemistry, Mathematics or a related field. 
  
• Strong familiarity with Linux, git and the open-source ecosystem. 
  
• Proficient experience working with research software, large datasets, and databases. 
  
• Experience building and maintaining cloud infrastructure (e.g., Azure). 
  
• Experience building processing pipelines and web apps in Python. 
  
• Strong analytical, problem-solving, and communication skills. 
  
• Passionate about pushing the boundaries of science.  Prior experience developing high-performance scientific software is not required, but preferred. 

#Research #AI for Science

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.  We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

• 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.