Research Intern - Memory & Orchestration in Large Language Models
Microsoft
Research Intern - Memory & Orchestration in Large Language Models
Redmond, Washington, United States
Save
Overview
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.
Microsoft Research 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 create new LLM memory representation and orchestration systems in the pursuit of innovative and disruptive offerings for both enterprise and consumer contexts.
We are particularly interested in developing new methods for leveraging and training various types of LLM memory representations. This includes structures such as knowledge graphs, graph and text embeddings, and the creation of advanced retrieval augmented generation (RAG) systems. We also look towards understanding research that allows us to measure the temporal changes to these structures over time as we pursue better quality retrieval and the ability to generate temporal explanations.
Qualifications
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, including prompt engineering.
- Familiarity with embeddings, including graph embeddings.
- Experience building and deploying retrieval augmented generation systems.
- Experience working in interdisciplinary teams, with a focus on AI research and development.
- Proficient programming skills in Python.
- 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: https://careers.microsoft.com/us/en/us-intern-pay
Microsoft accepts applications and processes offers for these roles on an ongoing basis.
Responsibilities
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 methods that measure changes over time.
- Developing advanced retrieval augmented generation systems to enhance LLM capabilities.
- Collaborating with interdisciplinary teams of researchers and engineers on challenging and impactful projects.
- Presenting research findings and participating in research discussions.