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# Staff Machine Learning Engineer

- **Company:** XPeng Inc.
- **Location:** Santa Clara, United States
- **Experience:** Experienced
- **Salary:** $215,280.0
- **Contract:** Permanent contract
- **Skills:** Systems Engineering, Program Optimization, Python (Programming Language), Software Engineering, Pytorch, Delivery Pipeline, Large Language Models, Deep Learning, ONNX (Open Neural Network Exchange) Format, TensorRT
- **Published:** August 18, 2026
- **Apply:** https://www.dice.com/job-detail/d0622af4-6a40-423a-9fe3-7b40b1ba156c

## About the Role

* Master in CS/CE/EE, or equivalent, with 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.
 * Excellent communication and problem-solving skills, with the ability to thrive in a fast-paced and collaborative environment., * Experience with weight-only, activation, KV-cache, dynamic, static, or mixed-precision quantization.
 * Experience with AWQ, GPTQ, SmoothQuant, or related methods.
 * Strong numerical analysis and systems engineering skills.
 * Experience with one or more LLM runtimes, such as 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 NeurIPS, ICML, ICLR, ACL, or related conferences.

## Description

* Develop VLA inference models, ensure numerical consistency with training models, and productionize LLM quantization methods, including PTQ, QAT, mixed-precision inference, INT8, FP4, and lower-bit techniques.
 * Develop production-quality Python code with strong testing, observability, reproducibility, and failure handling.
 * Build robust model export, calibration, benchmarking, validation, and deployment pipelines.
 * Engage early with the VLA model research team to establish performance estimates and prove model feasibility.
 * Curate evaluation datasets and establish a comprehensive metric suite to systematically benchmark VLA performance.
 * Analyze numerical errors, accuracy regressions, and performance trade-offs.
 * Develop PTQ and QAT orchestration workflows.
 * Serve as the primary interface with field-testing and simulation teams for issue triage and autonomous driving performance sign-off.
 * Collaborate with the in-vehicle software team on latency analysis and issue triage.
 * Collaborate with the training infrastructure team to develop QAT and model distillation.


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