You will be a part of a team of fellow Googlers working in an environment of respect where we promote equal opportunities to succeed.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Drive pre-sales technical strategy and build go-to-market solutions with customers and data center hosting partners to accelerate business cycles, resolve technical objections, and land large-scale Google Cloud TPU agreements and deployments across APAC.
Serve as a trusted technical advisor on end-to-end architecture and integration strategies across the TPU software stack and customer or partner environments (fleet management systems, job schedulers, IaaS operations tooling, ancillary storage/compute, and data center networking/WAN).
Guide customers and hosting partners through technical qualification, pod layout design, system turn-up, and operational readiness—covering slice lifecycle management, OS and firmware upgrades, telemetry integration, and hardware maintenance workflows.
Conduct workload performance profiling, workload migration, and optimization of large-scale training and inference models and data pipelines specifically for Google Cloud TPU hardware topologies (e.g., 3D Torus over Inter-Chip Interconnect and Optical Circuit Switches).
Minimum qualifications
Bachelor's degree or equivalent practical experience.
10 years of experience with cloud-native or high-performance computing (HPC) infrastructure architecture in a customer-facing pre-sales, solutions architecture, or deployment engineering role.
Experience developing, deploying, and orchestrating distributed AI/ML workloads using modern frameworks (e.g., PyTorch, JAX, vLLM, or SGLang) and job schedulers (e.g., Kubernetes or Slurm) on AI accelerator hardware.
Experience with Linux host software, cluster control planes, and data center infrastructure integration (compute, high-speed networking, and storage).
Preferred qualifications
Experience integrating large-scale accelerator clusters with customer and data center partner software stacks, including fleet management APIs, IaaS operations tools, out-of-band hardware management (BMC/Redfish), and Prometheus telemetry pipelines.
Experience designing, turning up, and operating large-scale AI/HPC infrastructure farms, including data center space, power, liquid cooling (CDU), Optical Circuit Switches (OCS), Top-of-Rack (ToR) networking, and hardware RMA/repair playbooks.
Experience with lower-level performance profiling and kernel optimization tools (e.g., XProf, PyTorch Profiler, Pallas, or MaxText) to diagnose and resolve distributed training and inference bottlenecks.
Ability to communicate in Mandarin fluently to support customers in the region.
Bachelor's degree or equivalent practical experience.
10 years of experience with cloud-native or high-performance computing (HPC) infrastructure architecture in a customer-facing pre-sales, solutions architecture, or deployment engineering role.
Experience developing, deploying, and orchestrating distributed AI/ML workloads using modern frameworks (e.g., PyTorch, JAX, vLLM, or SGLang) and job schedulers (e.g., Kubernetes or Slurm) on AI accelerator hardware.
Experience with Linux host software, cluster control planes, and data center infrastructure integration (compute, high-speed networking, and storage).
