New 
Research Intern - Multimodal Learning
 Microsoft | |
 United States, Washington, Redmond | |
 Sep 08, 2025 | |
|
OverviewResearch 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, computer science, mathematics, economics, physics, healthcare, and the environment. As a Research Intern at Microsoft Research, you will be at the forefront of developing and implementing cutting-edge technology multimodality. This role is ideal for candidates who are passionate about physics informed multimodal learning, generation and multimodal foundation model training, across typical modal vision, text, audio, code and system. You will collaborate with a team of world-class researchers and engineers across MSR lab in Vancouver, Canada, Redmond, US and Cambridge, UK to explore the next generation of multimodal learning and generation paradigm, and unlock the new intelligence capabilities reside in multimodality.
ResponsibilitiesResearch 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. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer. Additional ResponsibilitiesResearch and Analysis: Conduct thorough research on emerging trends in LLM, prompt engineering, multimodal foundation model. Documentation and Reporting: Prepare detailed documentation of simulations, methodologies, and findings. Present results and insights to team members and stakeholders. Innovation and Problem-Solving: Identify challenges and bottlenecks in AI infrastructure and propose innovative solutions. | |