Cloud Acceleration Research Intern (DPU & AI Infra) - 2027 Start (PhD)

What you'll do

• Develop and evaluate techniques and prototypes for accelerating distributed AI training and inference at scale.
• Analyze and optimize performance across hardware, drivers, kernels, communication libraries, and runtimes.
• Collaborate across hardware and AI infrastructure teams to transition research into production and contribute to technical proposals, publications, and patents.

What they ask for

• Currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
• Available for a full-time, 12-week internship during Summer 2027.
• Proficiency in C/C++ or Rust, including systems-level development and debugging; strong Linux systems development experience.
• Solid understanding of operating systems, computer architecture, networking, or distributed systems; background in at least one listed systems research area.

Nice to have

• Research record through publications, technical reports, open-source contributions, or substantial research projects; experience building and evaluating production-quality or research prototype systems.
• Familiarity with DPDK, RDMA, eBPF, high-performance communication libraries, hypervisors, kernel bypass, device virtualization, hardware offload, LLM serving, disaggregated inference, KV-cache management, distributed data systems, or performance analysis.

Tools

DPDK, RDMA, eBPF, C/C++, Rust, Linux, DPUs, GPUs

AI focus

AI/ML infrastructure research and acceleration, including distributed training and inference, LLM serving, and KV-cache management.