Research Intern - Memory & Orchestration in Large Language Models

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. 

Our Societal Resilience team is seeking Research Interns to join us in pushing the boundaries of AI capabilities with large language models (LLMs) and multimodal models. Our mission is to prepare for the unknown challenges of the future by developing resilient systems and technologies that can support societal and individual resilience during times of crisis. 

The research focuses on memory and orchestration within LLMs and multimodal models. We are particularly interested in developing new methods for leveraging and training various types of embeddings, such as graph embeddings and multimodal embeddings, creating advanced retrieval augmented generation (RAG) systems, and conducting specialized context-specific fine-tuning to build more capable and adaptive models. We believe that these capabilities will play a critical role in building resilience by amplifying the ability of individuals and organizations to respond to uncertainty. 

Required Qualifications

• Currently enrolled in a PhD program in Computer Science, Machine Learning, Artificial Intelligence, or a related STEM field. 

Other Requirements

• Research Interns are expected to be physically located in their manager’s Microsoft worksite location for the duration of their internship.
• In addition to the qualifications below, you’ll need to submit a minimum of two reference letters for this position as well as a cover letter and any relevant work or research samples. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter. 

Preferred Qualifications

• Experience with large language models and multimodal models, including training and fine-tuning. 
• Familiarity with embeddings, including graph embeddings and multimodal embeddings.
• Familiarity with the architecture of generative models, such as variational autoencoders and diffusion models.
• Experience building and deploying retrieval augmented generation systems. 
• Experience working in interdisciplinary teams, with a focus on AI research and development. 
• Strong programming skills in Python and familiarity with AI/ML frameworks such as PyTorch or TensorFlow. 
• Previous experience publishing academic research in top-tier conferences or journals. 

The base pay range for this internship is USD $6,550 - $12,880 per month. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $8,480 - $13,920 per month.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: 

Microsoft accepts applications and processes offers for these roles on an ongoing basis.

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. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer.

Additional Responsibilities

• Conducting hands-on research into systems for memory and orchestration of LLMs and multimodal models. 
• Investigating new embedding techniques, including graph embeddings and multimodal embeddings. 
• Developing advanced retrieval augmented generation systems to enhance LLM capabilities. 
• Specializing in context-specific fine-tuning for creating adaptable AI systems. 
• Collaborating with interdisciplinary teams of researchers and engineers on challenging and impactful projects. 
• Presenting research findings and participating in research discussions.