About the job
This Research Internship will design training algorithms and apply them to improving the quality/efficiency trade-offs of large language models, with a focus on resource-constrained environments. Possible directions of investigation include: designing new algorithms for quantized model fine-tuning; leveraging training to improve the token efficiency of reasoning models; proposing and implementing systems optimizations to scale training under resource constraints.
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.
Qualifications
Minimum
Currently enrolled in a PhD program in Computer Science or a related field. At least 1 year of experience working on AI/Machine Learning. 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
Hands-on experience with ML tools and frameworks such as Pytorch. Experience training and evaluating models. Publication track record in ML conferences. Ability to collaborate effectively with other researchers and product teams.