Research Intern - Hardware/software Codesign

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

Research intern focused on advancing the efficiency of AI systems through hardware/software codesign, exploring novel designs and optimizations across the AI stack, including models, frameworks, cloud infrastructure, and hardware. The role involves practical implementation skills for efficient, scalable computational kernels and aims to contribute to mid- and long-term product innovations.

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

  • PhD program in Computer Science, Software Engineering, Electrical Engineering, or a related STEM field

Nice to have

  • GPU architecture
  • memory hierarchies
  • parallel computing
  • algorithm optimization
  • GPU programming
  • performance profiling
  • optimization tools
  • CUDA
  • ROCm
  • Triton
  • PTX
  • CUTLASS
  • GPU programming frameworks
  • Advanced C++ programming skills

What the JD emphasized

  • PhD program in Computer Science, Software Engineering, Electrical Engineering, or a related STEM field
  • Proficient understanding of GPU architecture, memory hierarchies, parallel computing and algorithm optimization
  • Hands-on experience in GPU programming, including performance profiling and optimization tools
  • Proficient knowledge of CUDA, ROCm, Triton, PTX, CUTLASS, or similar GPU programming frameworks
  • Advanced C++ programming skills

Other signals

  • advancing efficiency across AI systems
  • novel designs and optimizations across the entire AI stack
  • large-scale production systems
  • practical implementation skills to create efficient, scalable computational kernels