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

ByteDance; Seattle (WA); On-site; USD 201,000 - 268,000; Full time

Job summary

ByteDance, located in Seattle, invites PhD students to join the DPU (Data Processing Unit) team for a 12-week internship. The role focuses on building cloud infrastructure, researching software-hardware co-design, and accelerating AI/ML workloads.

You will contribute to DPU-based cloud acceleration, distributed AI training, and end-to-end performance optimization while collaborating with hardware and AI infrastructure teams to transition ideas into production.

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

Responsibilities

• Cloud virtualization, hypervisors, and operating systems
• High-performance networking, including DPDK and RDMA
• High-speed interconnects, virtual switching, and network offload
• Distributed storage and I/O acceleration
• Orchestration and scheduling for AI/ML workloads
• 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 directions

Education

PhD in Computer Science, Computer Engineering, Electrical Engineering, or related field

Job description

Join us as we work together to inspire creativity and enrich life around the globe.

Location

Seattle

Team

Technology

Employment Type

Intern

Job Code

A127642

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Responsibilities

About the TeamThe ByteDance DPU (Data Processing Unit) team builds foundational cloud and AI computing infrastructure for ByteDance and Volcano Engine. Our mission is to advance the architecture, development, and research of next-generation software-hardware co-design technologies across compute, networking, and storage for cloud and AI computing.

• Cloud virtualization, hypervisors, and operating systems
• High-performance networking, including DPDK and RDMA
• High-speed interconnects, virtual switching, and network offload
• Distributed storage and I/O acceleration
• Orchestration and scheduling for AI/ML workloads

We work at the intersection of systems research, distributed infrastructure, and hardware acceleration. Our technologies operate at cloud scale and help shape the next generation of cloud and AI computing platforms.

We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.

• 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 directions

Qualifications

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