Research Intern - AI Safety & Reliability for LLM Systems

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Applied Sciences

Research intern to study LLM-based assistants' behavior with incomplete information, focusing on uncertainty awareness, responsible reasoning, and robustness for enterprise AI systems.

What you'd actually do

  1. The Research Intern will study how large language model (LLM)–based assistants behave when relevant information is incomplete or unevenly available and explore methods for detecting such gaps and adapting system responses accordingly.
  2. Research Interns put inquiry and theory into practice.
  3. Alongside fellow doctoral candidates and some of the world’s best researchers, Research Interns learn, collaborate, and network for life.
  4. Research Interns not only advance their own careers, but they also contribute to exciting research and development strides.
  5. 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.

Skills

Required

  • Currently enrolled in a PhD program in computer science, machine learning, statistics, human-computer interaction or a related field.

Nice to have

  • Proficiency in Python and experience with common ML and data processing libraries
  • Experience with large language models and/or retrieval-augmented generation (RAG) or related approaches
  • Prior research experience in machine learning, NLP, or human-centered AI, demonstrated through publications, preprints, or substantial projects suitable for peer-reviewed venues such as NeurIPS, ICML, FAccT, AIES, CHI, or CSCW.
  • Proficient written and verbal communication skills for presenting and documenting research.
  • Interest in AI reliability, robustness, safety, or responsible AI research.

What the JD emphasized

  • PhD program in computer science, machine learning, statistics, human-computer interaction or a related field
  • Proficiency in Python and experience with common ML and data processing libraries
  • Experience with large language models and/or retrieval-augmented generation (RAG) or related approaches
  • Prior research experience in machine learning, NLP, or human-centered AI, demonstrated through publications, preprints, or substantial projects suitable for peer-reviewed venues such as NeurIPS, ICML, FAccT, AIES, CHI, or CSCW.
  • Interest in AI reliability, robustness, safety, or responsible AI research.

Other signals

  • LLM-based assistants
  • uncertainty awareness
  • responsible reasoning
  • robustness
  • safer and more dependable AI systems