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# Senior Machine Learning Engineer - AI Foundation

- **Company:** XPeng Motors
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $174,720 - $295,680
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Nvidia CUDA, Machine Learning, Software Engineering, Reinforcement Learning, Pytorch, Large Language Models, SGLang, Model Inference
- **Published:** October 30, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-machine-learning-engineer-ai-foundation-santa-clara-ca--c47ae006-1836-49b0-9f75-1c37cc45f2d0

## About the Role

Machine Learning, Artificial Intelligence (AI), Artificial Intelligence (AI), Autonomous Driving Systems, Machine Learning, Research & Development (R&D), Robotics, Software Engineering, Team Player, Scientific Research, * Master's Degree in CS/CE/EE, or equivalent, in industry experience.

 * Deep knowledge of PyTorch.
 * Knowledge of model inference framework (e.g. vLLM, SGLang)
 * In-depth knowledge of transformer architecture and ways to accelerate the training and inference of transformer models.
 * Experience of performing large scale distributed training of models.
 * A track record of profiling model and doing detective work to improve model training and inference speed.

Preferred Skill Requirements:

 * Previous experience in the autonomous driving industry.
 * Experience with CUDA language for writing custom ops.
 * Experience with edge computing systems.
 * Knowledge of disributed computing frameworks, such as Ray.
 * A track record of efficiently solving complex problems collaboratively on larger teams

## Description

Our mission is to solve the autonomous driving problem. You will work with a team of talented software engineers, machine learning engineers and research scientists to push the boundary of state-of-art machine learning models which will enable the next-generation E2E solution of autonomous driving., * Design and implement training data pipeline that streams data from hundreds of petabytes of labeled and unlabeled data from a fleet of over a million vehicles.

 * Implement training framework for all physical AI foundation models in XPeng, including VLA 2.0, XWorld, Robotics.
 * Accelerate training with state of the art parallelisms, e.g., FSDP, Expert Parallel, Context Parallel, and data types.
 * Accelerate model inference on the cloud for closed-loop simulation, reinforcement learning, and enterprise LLM/VLM applications.