SDN PhD Intern – Network Verification and Traffic Engineering

Job Description

Research Intern (SDN Traffic Intelligence & Control) | ByteDance

The Tone:
This is a PhD internship at ByteDance, a global technology company dedicated to inspiring creativity and enriching lives through a suite of over a dozen products, including TikTok. The SDN team, a core part of ByteDance’s infrastructure, is responsible for building cross-layer intelligence that enables hyper-scale datacenter interconnection. This involves developing advanced network traffic control, bandwidth management systems, network emulation, and verification models and tools. This role is essential for empowering highly available, secure, and scalable datacenter networks and global backbone network infrastructure. Interns actively contribute to cutting-edge products and research, influencing the organization’s future plans and emerging technologies through a dynamic experience that blends hands-on learning with collaboration with industry experts.

The TL;DR
• Role: Internship (PhD)
• Location: US-based
• Pay: $57 hourly
• Team: The SDN team builds cross-layer intelligence for hyper-scale datacenter interconnection, developing systems for traffic control, bandwidth management, and network verification.
• Mission: To optimize global network traffic control and bandwidth management, and to enhance network design, operations, maintenance, and troubleshooting across ByteDance’s large-scale data center networks through advanced verification platforms.
• Tech Stack: Go, C++, Python

What You’ll Actually Do
• Verification Platform Research: Engage in research and development efforts on Network Verification platforms, including emulation, Control Plane Verification, and Data Plane Verification.
• Network Support: Support network operators and architects in designing, operating, maintaining, and troubleshooting ByteDance’s large-scale data center networks using developed platforms.
• Traffic Engineering Research: Conduct development and research work on Network Traffic Engineering platforms.
• Traffic Optimization: Optimize global traffic control and network bandwidth management through the application of Traffic Engineering platforms.

The Must-Haves
• Background: Currently pursuing a PhD in Computer Science, Computer Engineering, or a related technical discipline.
• Experience: Demonstrated ability to develop distributed software using proficiency in one or several mainstream programming languages such as Go, C++, or Python.
• Skills: Quick learning and adaptability; perseverance and a spirit of deep diving into technical details; strong communication and collaborative exchange skills.
• Bonus: Prior networking research experience from internships, work, or academic publications; a research focus in optimization, network traffic engineering, or network verification; high levels of creativity and effective problem-solving capabilities.