Research Intern - Foundations of Generative AI

Microsoft Microsoft · Big Tech · New York, NY +2 · Applied Sciences

Research Intern position at Microsoft Research AI Frontiers lab focusing on expanding AI capabilities, efficiency, and safety through innovations in foundation models and learning agent platforms. The role involves developing, improving, and exploring LLMs and Multimodal AI models, with research areas including reasoning, new architectures, action models, multi-agent systems, and evaluation.

What you'd actually do

  1. As a Research Intern in MSR AI Frontiers Lab, you will perform cutting-edge research in collaboration with other researchers, engineers, and product groups.
  2. As a member of a world-class research organization, you will be a part of research breakthroughs in the field and will be given an opportunity to realize your ideas in products and services used worldwide.
  3. Embody our culture and values.

Skills

Required

  • Accepted or currently enrolled in a PhD program in Computer Science or related STEM field.

Nice to have

  • Experience publishing academic papers as a lead author or essential contributor in the field of Artificial Intelligence.
  • Experience participating in a top conference in relevant research domain.
  • Demonstrable ability to define an ambitious, original research agenda.
  • Ability to collaborate, communicate effectively, and work as part of a multi-disciplinary team.
  • Keen interest in real-world applications and impact.
  • Experience in Machine Learning, Computer Vision and related fields.
  • Research demonstrated by publications at the following conferences: NeurIPS, ICML, ICLR, ACL, NAACL, CVPR, COLT, ECCV, ICCV, EMNLP.

What the JD emphasized

  • expanding the pareto frontier of Artificial Intelligence (AI) capabilities, efficiency, and safety
  • innovations in foundation models and learning agent platforms
  • Small Language Models (e.g. Phi, Orca)
  • agentic AI systems (e.g. AutoGen, MagenticOne, OmniParser)
  • new transformer architectures (e.g. Belief State Transformers)
  • optimizers for scalable Large Language (LLM) Training (e.g. Dion)
  • advancement of Generative AI and Large Language Model Technologies
  • developing, improving, and exploring the capabilities of LLMs and Multimodal AI models
  • Reasoning method for LLMs and multimodal models
  • New model architectures and training methods
  • Action models for automating web and computer tasks
  • Orchestration and multi-agent systems: automated orchestration between multiple agents incorporating human feedback and oversight
  • Evaluation and Understanding of model and agent capabilities
  • cutting-edge research
  • research breakthroughs

Other signals

  • foundation models
  • learning agent platforms
  • LLMs
  • Multimodal AI models