The Server Management DevOps team is responsible for the end-to-end lifecycle management of servers across ByteDance’s self-built data centers in the United States and Europe.
Our scope covers new hardware introduction, data center delivery, production operations, hardware maintenance, configuration and firmware changes, capacity migration, asset decommissioning, data sanitization, and hardware reuse.
The team serves as a central engineering and coordination point across multiple functions, including:
- Hardware New Product Introduction (NPI)
- Server and data center operations
- Field maintenance and infrastructure management
- Hardware vendors and service providers
- Supply chain and asset management
- Infrastructure platform and automation engineering teams As ByteDance continues to expand its AI infrastructure, the team is taking on an increasingly important role in introducing, productionizing, and operating high-density GPU platforms at scale.
About the Role
We are looking for an experienced and hands-on Senior Production Systems Engineer with deep expertise in large-scale GPU infrastructure, Linux systems, hardware lifecycle management, automation, and production operations.
In this role, you will lead the introduction and productionization of current- and next-generation AI infrastructure, including rack-scale and high-density GPU platforms such as
NVIDIA GB200/GB300 NVL72, HGXor DGX B200/B300, Vera Rubin NVL72, and comparable accelerator systems.
You will own critical work across platform evaluation, system and firmware integration, data center readiness, deployment validation, fleet onboarding, monitoring, incident response, and long-term operational reliability.
You will also work directly with AI training and inference environments to ensure that the underlying infrastructure meets real workload requirements.
This is a senior individual-contributor role requiring strong technical judgment, hands-on engineering ability, and the capacity to lead complex global infrastructure initiatives across organizational boundaries.
Key Responsibilities
- Advanced GPU Platform Introduction: Lead the evaluation, qualification, integration, and production rollout of next-generation GPU platforms, including
GB200/GB300 NVL72, B200/B300 systems, Vera Rubin, and future rack-scale AI infrastructure.
- End-to-End Production Readiness: Define launch criteria and readiness plans spanning server hardware, firmware, BMC, operating systems, drivers, GPU software stacks, networking, storage, security, telemetry, and operational tooling.
- Rack-Scale Integration and Fleet Operations: Partner across hardware, data center, network, storage, power, cooling, and vendor teams to resolve system-level challenges and improve GPU fleet availability, utilization, serviceability, and lifecycle management.
- Systems, Performance, and Reliability Engineering: Diagnose complex Linux, hardware, firmware, PCIe, NVLink/NVSwitch, network, storage, and memory issues, while developing qualification, burn-in, benchmarking, health-check, and regression-testing frameworks.
Improve the availability, utilization, serviceability, and lifecycle management of large GPU fleets across multiple data center regions.
- Automation, Observability, and AI-Assisted Operations: Build scalable automation and actionable telemetry for provisioning, configuration, monitoring, fault detection, remediation, repair, and lifecycle operations;
apply AI technologies to incident triage, troubleshooting, knowledge retrieval, and automated remediation.
- Technical and Cross-Functional Leadership: Establish engineering standards, operational procedures, and long-term support models;
mentor engineers and drive complex infrastructure programs across internal teams, supply-chain partners, and external vendors.
- On-Call and Global Operations: Participate in a global on-call rotation and provide senior-level leadership during critical production incidents.
Occasional travel to data centers, integration facilities, or vendor sites may be required.
Lead the investigation of complex and high-impact production incidents, coordinate mitigation across teams and vendors, perform root-cause analysis, and ensure that preventive actions are implemented and measured.