Staff Machine Learning Engineer - LLM Quantization & Deployment

Santa Clara, CA · full time · Staff

$215,280 – $364,320

Listed on XPeng’s own careers site. You apply with them directly — we never stand between you and the employer.

What this role is

This role involves developing and optimizing large language models for deployment on XPeng's custom AI hardware, with a focus on quantization techniques, inference pipelines, and autonomous driving performance validation. It suits experienced machine learning engineers who enjoy bridging research and production systems, particularly those with deep expertise in model optimization and on-device inference.

Our summary, not XPeng’s wording. The full posting is on their site.

Skills this role names

What they ask for

Required

Master's degree in Computer Science, Computer Engineering, or Electrical Engineering, or equivalent experience
3-5 years of industry experience
Strong understanding of Transformer architectures and LLM inference
Hands-on experience quantizing or deploying deep learning models in production
Proficiency with PyTorch and at least one inference or compilation stack
Strong Python programming and software engineering skills
Ability to work effectively across research, systems, infrastructure, and product teams

Nice to have

Experience with weight-only, activation, KV-cache, dynamic, static, or mixed-precision quantization
Experience with quantization methods such as AWQ, GPTQ, or SmoothQuant
Strong numerical analysis and systems engineering skills
Experience with LLM runtimes including TensorRT-LLM, vLLM, SGLang, llama.cpp, ONNX Runtime, TVM, MLIR, or custom runtimes
Experience deploying LLMs on resource-constrained or heterogeneous hardware
Contributions to model optimization, inference, compiler, or serving projects
Publications at top-tier ML conferences such as NeurIPS, ICML, ICLR, or ACL