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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.
Job Responsibility:
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
Requirements:
Currently enrolled in a PhD program in Computer Science or a related field
At least 1 year of experience working on AI/Machine Learning
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
Nice to have:
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