Staff Software Engineer, LLM Inference Platform

About this role

About Zenlayer

Zenlayer is the first massively distributed edge cloud provider, operating 290+ Points of Presence across 70+ countries. With accolades including PTC “Best Networking Innovation”, “Outstanding Cloud, Data Center, or Interconnection Company”, and Inc. 5000, we enable businesses to deploy and run applications at the edge, thus reducing network latency to the end user. Zenlayer focuses on improving connections worldwide, all while nurturing a unique company culture that values service, growth, and trust. As a “Zenplayer”, you’ll be joining a community of success-driven team players that share the dream of powering a better-connected world.

About the Role:

As the technical lead for inference performance, you will own end-to-end responsibility for the platform's Token Performance. For models ranging from hundreds of billions to trillions of parameters (including MoE), you will continuously reduce cost per million tokens and increase per-GPU Goodput while meeting TTFT/TPOT SLOs, and prove the gain from every optimization with reproducible data.

What You Will Work On

1. End-to-End Performance Attribution and Benchmarking

• Establish a unified Token Performance metrics framework (TTFT, TPOT, Goodput, GPU utilization, per-token cost), and build load-testing benchmarks that reflect real production traffic (high concurrency, long context, multi-turn Agent workloads)

• Perform bottleneck attribution for models such as DeepSeek, Kimi, GLM, Qwen, and MiniMax across compute, memory bandwidth, communication, scheduling, and queuing, and let the data determine which layer is the dominant constraint

2. Kernel-Level Inference Engine Optimization

• Dive deep into the source code of vLLM / SGLang / TensorRT-LLM, customizing and contributing upstream on critical paths such as the scheduler, Continuous Batching, Chunked Prefill, CUDA Graph, and KV block management

• Clearly define the responsibility boundary between the inference engine...