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.
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