Xpengmotors~1mo ago

Research Scientist Intern

United States·Santa Claraentry

Data ScienceOtherResearch ScientistResearch Scientist InternData & AI

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Quick Summary

Overview

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.

Key Responsibilities

Conduct research on designing and implementing large-scale multi-modal architectures (e.g., vision–language–action transformers) for end-to-end autonomous driving.

Requirements Summary

Publication record in top-tier AI conferences (CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, etc). Prior experience building foundation or end-to-end driving models , or LLM /VLM architectures (e.g., ViT, Flamingo, BEVFormer, RT-2, or GRPO-style policies).

Technical Tools

pytorchmachine-learning

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.

About the Role

~1 min read

We are actively seeking a full-time Research Scientist Intern to drive the modeling and algorithmic development of XPENG’s next-generation Vision-Language-Action (VLA) Foundation Model — the core brain that powers our end-to-end autonomous driving systems.

You will work closely with world-class researchers, perception and planning engineers, and infrastructure experts to design, train, and deploy large-scale multi-modal models that unify vision, language, and control. Your work will directly shape the intelligence that enables XPENG’s future L3/L4 autonomous driving products.

Responsibilities

~1 min read

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Conduct research on designing and implementing large-scale multi-modal architectures (e.g., vision–language–action transformers) for end-to-end autonomous driving.
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Design 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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Closely collaborate with researchers and engineers across the modeling and infrastructure team.
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Contribute to top-tier AI/CV/ML conferences publications and present research findings.

Requirements

~1 min read

Currently enrolled in the Master/Ph.D program in Computer Science, Electrical/Computer Engineering, or related field, with the specialization in the CV/NLP/ML.
Experience in multi-modal modeling (vision, language, or planning), with deep understanding of representation learning, temporal modeling, and reinforcement learning techniques.
Strong proficiency in PyTorch and modern transformer-based model design.

Requirements

~1 min read

Publication record in top-tier AI conferences (CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, etc).
Prior experience building foundation or end-to-end driving models, or LLM/VLM architectures (e.g., ViT, Flamingo, BEVFormer, RT-2, or GRPO-style policies).
Knowledge of RLHF/DPO/GRPO, trajectory prediction, or policy learning for control tasks.
Familiarity with distributed training (DDP, FSDP) and large-batch optimization.