LLM/AI Ops Development Engineer Graduate (Data Center Networking) - 2027 Start

ByteDance; San Jose (CA); On-site; USD 110,000 - 160,000; Full time

Job summary

ByteDance is seeking a development engineer to join the Network Observability team, working on autonomous data center networks enabled by AI/ML and LLM-based agents. You will build a panoramic observability platform, ingesting telemetry data from gNMI, Netconf, IPFIX/NetFlow and SNMP, and implement intelligent diagnostics to quickly identify root causes across the stack.

The role blends network engineering with cutting-edge AI technologies, including autonomous remediation and proactive capacity

Qualifications

• Bachelor's or Master's degree in Computer Science or related discipline.
• Deep understanding of data center network architectures (Spine-Leaf Fabric) and protocols EVPN/VXLAN, BGP/OSPF; Linux network stack.
• Mastery of Go (Golang).

Responsibilities

• Design a closed-loop AIOps for the NetWork platform.
• Develop a telemetry pipeline aggregating gNMI, Netconf, IPFIX/NetFlow, and SNMP.
• Apply ML/DL for anomaly detection and root cause analysis.
• Explore LLMs and agents for intelligent operations.
• Develop automated remediation and smart runbooks.
• Forecast capacity bottlenecks and risk for proactive maintenance.
• Ensure a scalable, highly available data center network platform.

Skills

Data center networking

EVPN/VXLAN

BGP/OSPF

Linux networking

Go (Golang)

Education

Bachelor's degree in Computer Science

Master's degree in Computer Science

Tools

gNMI

Netconf

IPFIX/NetFlow

SNMP

Job description

Responsibilities

About the team

Networking brings together innovative ideas and technologies from network architecture, software defined networking (SDN), network virtualization, switch software and hardware co-design, and high-speed networking, to create hyper-scale data-center networking solutions that power several of the most popular apps of the world such as Douyin and TikTok which serve hundreds of millions of users around the globe.

Network Observation team is committed to building a world-leading hyperscale data center network infrastructure that supports hundreds of millions of users' real-time access and explosive growth of massive data volumes. We believe that the next generation of network operations will be fundamentally powered by artificial intelligence technologies, particularly Large Language Models (LLMs).

We are seeking a passionate development engineer who combines deep networking expertise with innovative AIOps capabilities to join us in defining and building "autonomous" data center networks. Together, we will transform network operations from a reactive "firefighting" mode into a proactive, data-driven intelligent ecosystem with predictive and self-healing capabilities.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.

Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Responsibilities

As a core member of our team, you will collaborate closely with our NetOps, SRE, and platform engineering teams to tackle the complexities of one of the world's largest data center networks. You will design and implement a closed-loop AIOps for NetWork platform, covering:

• Build a Panoramic Network Observability Platform: Develop a streaming telemetry data pipeline for both physical and virtual networks, integrating multi-source data from gNMI, Netconf, IPFIX/NetFlow, and SNMP to provide a high-quality, real-time data foundation for AIOps.
• Develop an Intelligent Diagnostics and Root Cause Analysis System: Apply machine learning and deep learning algorithms to perform anomaly detection, correlation analysis, and intelligent noise reduction on massive volumes of network metrics, logs, and events. Swiftly pinpoint root causes of failures across the entire stack, from optical transceivers and switch hardware to protocol adjacencies and application traffic.
• Explore Innovative Applications of LLMs and Agents:
• Intelligent Operations Assistant: Build a conversational chatbot powered by Retrieval-Augmented Generation (RAG) that understands natural language queries, automatically queries knowledge bases and monitoring data, and provides precise troubleshooting guidance and network status reports.
• Automated Remediation and Smart Runbooks: Train operational Agents to safely and controllably invoke network change tools and APIs. Empower them to autonomously generate, recommend, or even execute remediation plans and emergency runbooks based on their understanding of failure scenarios.
• Establish Capacity and Risk Prediction Capabilities: Forecast network capacity bottlenecks, high-risk links, and "sub-healthy" devices based on historical data and business growth models, enabling proactive scaling and preventative maintenance.
• Forge a Rock-Solid Engineering System: Adhere to engineering best practices to design and develop a highly available and scalable AIOps platform. Guarantee the stability and performance of the entire pipeline, from data collection and model training to online inference and automated closed-loop actions.

Qualifications

Minimum Qualifications:

• Individuals who are completing or have recently completed a Bachelor's or Master's degree in Computer Science or a related discipline.
• Deep understanding of data center network architectures (e.g., Spine-Leaf Fabric), and proficiency in key protocols such as EVPN/VXLAN and BGP/OSPF. In-depth knowledge of the Linux network stack is essential.
• Mastery of Gol