Research Intern - Training Methods for LLM Efficiency

Microsoft Microsoft · Big Tech · Mountain View, CA +1 · Applied Sciences

Research intern to design and apply training algorithms for improving the quality/efficiency trade-offs of large language models, focusing on resource-constrained environments. Potential research directions include quantized model fine-tuning, improving token efficiency for reasoning models, and scaling training under resource constraints.

What you'd actually do

  1. Research Interns put inquiry and theory into practice.
  2. Alongside fellow doctoral candidates and some of the world’s best researchers, Research Interns learn, collaborate, and network for life.
  3. Research Interns not only advance their own careers, but they also contribute to exciting research and development strides.
  4. 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.
  5. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer.

Skills

Required

  • Currently enrolled in a PhD program in Computer Science or a related field.
  • At least 1 year of experience working on AI/Machine Learning.

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.

What the JD emphasized

  • Currently enrolled in a PhD program in Computer Science or a related field.
  • At least 1 year of experience working on AI/Machine Learning.
  • Publication track record in ML conferences.

Other signals

  • 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