Research Intern - Msr Software-hardware Co-design

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

Research intern role focused on pioneering technologies for AI/ML workloads, specifically improving efficiency, security, and robustness of GPU memory systems, agentic AI systems, and software architecture for hardware accelerators. The role involves fast-paced execution, implementation, and evaluation on Azure platforms, with a focus on systems and AI.

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 Ph.D. program in Computer Science or closely related field.

Nice to have

  • Experience with memory systems
  • Experience with distributed systems
  • Experience with operating systems
  • Experience with security
  • Experience building research or engineering systems in systems, AI, security

What the JD emphasized

  • technical skills and backgrounds in systems and AI
  • experience working on large-scale systems
  • fluent in the relevant state-of-the-art research
  • proficient programming skills
  • fast-paced execution and implementation on Azure platforms
  • depth of knowledge and demonstrate experience in experimentation, implementation, and evaluation
  • Experience building research or engineering systems in systems, AI, security, as demonstrated through university projects and/or prior industry experience

Other signals

  • pioneer technologies that power AI and Machine Learning (ML) workloads with unmatched performance, efficiency, and trust
  • understanding and improving the efficiency and security of Graphics Processing Unit (GPU) memory systems used in AI
  • increasing the robustness of and trust in agentic AI systems
  • redesigning the software architecture of Azure services to run on hardware accelerators
  • reducing the cost of deploying and operating AI systems
  • systems and AI
  • fast-paced execution and implementation on Azure platforms
  • experimentation, implementation, and evaluation