Research Intern - Gray Systems Lab (GSL)

Research Internships at Microsoft provide a dynamic environment for research careers with a network of world-class research labs led by globally-recognized scientists and engineers, who pursue innovation in a range of scientific and technical disciplines to help solve complex challenges in diverse fields, including computing, healthcare, economics, and the environment.

• Research Interns put inquiry and theory into practice.
• Alongside fellow doctoral candidates and some of the world’s best researchers, research Interns learn, collaborate, and network for life.
• Research Interns not only advance their own careers, but they also contribute to exciting research and development strides.
• During the 12-week internship, Research Interns are paired with mentors and expected to collaborate with other Research Interns and researchers, present findings, and contribute to the vibrant life of the community.

• Currently pursuing a PhD degree in computer science, statistics, mathematics, or related technical field.
• Experience with one or more general purpose programming languages, such as Python, Java, C#, C++, Rust.
• Experience with one of the following: PostgreSQL, MySQL, SQL Server, Spark, Apache Iceberg, or other database or big data systems.
• Experience contributing to research communities and/or efforts, including publishing papers (being listed as author) at academic conferences.
• Take initiative but also follow a research/productization plan and work well as part of a team.
• Excellent critical thinking and communication skills.

• Industry leading healthcare
• Educational resources
• Discounts on products and services
• Savings and investments
• Maternity and paternity leave
• Generous time away
• Giving programs
• Opportunities to network and connect

Gray Systems Lab (GSL) is an applied research lab under Azure Data. We do research and development for databases, big-data, and cloud systems, working in close collaboration with product teams to design innovative solutions for real-world problems.