Responsibilities - Conduct research and development in DPU-based cloud acceleration and large-scale AI infrastructure. - Explore software-hardware co-design opportunities for AI/ML infrastructure, leveraging DPUs, GPUs, and custom hardware to optimize distributed training and inference. - Develop new techniques for accelerating distributed AI training and inference, including communication, data movement, resource management, and memory or cache systems. - Perform end-to-end performance analysis and optimization across hardware, device drivers, operating-system kernels, communication libraries, and user-space runtimes. - Build prototypes and evaluate proposed designs using representative cloud and AI workloads at scale. - Collaborate with hardware architects, systems engineers, and AI infrastructure teams to transition research ideas into production. - Contribute to technical proposals, architecture designs, publications, patents, and longer-term research direction Qualification Minimum Qualifications - Currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field. - Able to commit to 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 of: software-hardware co-design, computer architecture, distributed storage systems, high-performance networking, or AI/ML systems. Preferred Qualifications - Record of research demonstrated through publications, technical reports, open-source contributions, or substantial research projects. - Experience designing, implementing, and evaluating production-quality or research prototype systems. - Familiarity with one or more of the following: - DPDK, RDMA, eBPF, or high-performance communication libraries - Hypervisors, kernel bypass, device virtualization, or hardware offload - LLM serving, disaggregated inference, or KV-cache management - Distributed data loading, preprocessing, or storage systems - Performance modeling, profiling, or benchmarking - Strong analytical, creative problem-solving, and communication skills. - Ability to work effectively across research, software, and hardware teams. Job Information 【For Pay Transparency】Compensation Description (Hourly) - Campus Intern The hourly rate range for this position in the selected city is $57- $57. Benefits may vary depending on the nature of employment and the country work location. Interns have day one access to health insurance, life insurance, wellbeing benefits and more. Interns also receive 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year). Interns who are not working 100% remote may also be eligible for housing allowance. The Company reserves the right to modify or change these benefits programs at any time, with or without notice.