Research Intern (Inference Infrastructure) - 2027 Start (PhD)

About the Team

The 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. Our technology stack spans:

• 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.

Responsibilities

• Design and build large-scale, container-based cluster management and orchestration systems with extreme performance, scalability, and resilience.
• Architect next-generation cloud-native GPU and AI accelerator infrastructure to deliver cost-efficient and secure ML platforms.
• Collaborate across teams to deliver world-class inference solutions using vLLM, SGLang, TensorRT-LLM, and other LLM engines.
• Stay current with the latest advances in open source (Kubernetes, Ray, etc.), AI/ML and LLM infrastructure, and systems research; integrate best practices into production systems.
• Write high-quality, production-ready code that is maintainable, testable, and scalable.

Qualifications

Minimum Qualifications

• Currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
• Able to commit to working for 12 weeks during Summer 2027
• Strong understanding of large model inference, distributed and parallel systems, and/or high-performance networking systems.
• Hands-on experience building cloud or ML infrastructure in areas such as resource management, scheduling, request routing, monitoring, or orchestration.
• Solid knowledge of container and orchestration technologies (Docker, Kubernetes).
• Proficiency in at least one major programming language (Go, Rust, Python, or C++).

Preferred Qualifications

• Experience contributing to or operating large-scale cluster management systems (e.g., Kubernetes, Ray).
• Experience with workload scheduling, GPU orchestration, scaling, and isolation in production environments.
• Hands-on experience with GPU programming (CUDA) or inference engines (vLLM, SGLang, TensorRT-LLM).
• Familiarity with public cloud providers (AWS, Azure, GCP) and their ML platforms (SageMaker, Azure ML, Vertex AI).
• Strong knowledge of ML systems (Ray, DeepSpeed, PyTorch) and distributed training/inference platforms.
• Excellent communication skills and ability to collaborate across global, cross-functional teams.
• Passion for system efficiency, performance optimization, and open-source innovation.

Pay

Compensation Description (Hourly) - Campus Intern

The hourly rate range for this position in the selected city is $57.

Benefits

• Day one access to health insurance, life insurance, wellbeing benefits and more.
• 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).
• Housing allowance for interns who are not working 100% remote.

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.