Machine Learning Engineer

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.   Our mission is to build strong foundation for LLM deployment and quality sign-off for next-gen XPENG Turing AI chip. This includes and is not limited to: LLM model fine tuning, PTQ, QAT, on-vehicle inference and related fields.  

Key Responsibilities

• 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.

Basic Qualifications

• 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.

Preferred Qualifications

• Experience with weight-only, activation, KV-cache, dynamic, static, or mixed-precision quantization.