Xpengmotors

Xpengmotors

Senior Machine Learning Engineer - Foundation Model

Santa Clara, CA · Senior · Full-time

Sponsorship not specified$175k-$296kDetected 13 days ago
Machine LearningDeep LearningPyTorchNLPLLMsSensorsResearch

About the role

  • 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.
  • Your work will directly shape the intelligence that enables XPENG's future L3/L4 autonomous driving products.
  • Our salary ranges are determined by role, level, and location.

Responsibilities

  • Develop pretraining and fine-tuning strategies leveraging massive labeled and unlabeled fleet data (images, video, LiDAR, CAN bus, maps, human driving behaviors, etc.).
  • Collaborate with infrastructure engineers to scale training across thousands of GPUs using distributed training frameworks (FSDP, DDP, etc.).

Requirements

  • Strong proficiency in PyTorch and modern transformer-based model design.
  • Familiarity with distributed training (DDP, FSDP) and large-batch optimization.

Nice to have

  • PhD in CS/CE/EE or related field, with 1+ years of relevant industry experience.
  • Publication record in top-tier AI conferences (CVPR, ICCV, NeurIPS, ICLR, ICML, ECCV).
  • What do we provide:
  • A collaborative, research-driven environment with access to massive real-world data and industry-scale compute.
  • An opportunity to work with top-tier researchers and engineers advancing the frontier of foundation models for autonomous driving.
  • Direct impact on the next generation of intelligent mobility systems.
  • Opportunity to make significant impact on the transportation revolution by the means of advancing autonomous driving.
  • Competitive compensation package.

Compensation

  • Competitive compensation package.

Benefits

  • Design and implement large-scale multi-modal architectures (e.g., vision-language-action transformers) for end-to-end autonomous driving.
  • Research and integrate cross-modal alignment (e.g., visual grounding, temporal reasoning, policy distillation, imitation and reinforcement learning) to improve model interpretability and action quality.

This listing is sourced directly from Xpengmotors's careers page and normalized into a canonical job model.