Senior ML Engineer: AI Foundation & Large-Scale Training

Xpeng Inc.; Santa Clara (CA); Hybrid; USD 175,000 - 296,000; Full time

Benefits offered by this job

Snacks

Lunches and dinners

Competitive compensation package

Equipment and computational resources

Job summary

XPENG is seeking a full-time Machine Learning Engineer - AI Foundation to build state-of-the-art ML infrastructure for training large foundation models and speeding training/inference. You will collaborate with software engineers, ML researchers, and scientists to push AI capabilities for autonomous driving and intelligent mobility.

The role emphasizes scalable data pipelines, framework design for large models, and cutting-edge parallelism techniques, with a focus on real-world deployment across

Qualifications

• Master's degree in CS/CE/EE or equivalent industry experience.
• Deep knowledge of PyTorch.
• Knowledge of model inference frameworks (e.g. vLLM, SGLang).
• In-depth knowledge of transformer architecture and acceleration techniques for training/inference.
• Experience with large-scale distributed training.
• Track record of profiling models to improve speed.

Responsibilities

• Design and implement training data pipelines streaming from hundreds of petabytes of labeled/unlabeled data from a fleet of vehicles.
• Implement training framework for physical AI foundation models (e.g., VLA 2.0, XWorld, Robotics).
• Accelerate training using advanced parallelisms (FSDP, Expert Parallel, Context Parallel, data types).
• Accelerate model inference in cloud for closed-loop simulation, RL, and enterprise LLM/VLM applications.

Skills

PyTorch

Inference frameworks

Transformer models

Distributed training

Model profiling

Education

Master's degree in CS/CE/EE

Tools

CUDA

Job description

XPENG is seeking a full-time Machine Learning Engineer - AI Foundation to build state-of-the-art ML infrastructure for training large foundation models and speeding training/inference. You will collaborate with software engineers, ML researchers, and scientists to push AI capabilities for autonomous driving and intelligent mobility.

The role emphasizes scalable data pipelines, framework design for large models, and cutting-edge parallelism techniques, with a focus on real-world deployment across