AI Intern – Scenario Analysis

United States·Santa Claraentry

OtherAi Intern

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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

Analyze large-scale global driving data to identify long-tail and region-specific scenarios. Build workflows for vector search / semantic retrieval over scenario-related data.

Requirements Summary

Strong skills in Python, SQL, and data analysis. Experience with machine learning pipelines, clustering , classification, or unsupervised learning. Familiarity with embedding models, vector search, semantic retrieval , RAG, or prompt engineering.

Technical Tools

pythonsqldata-analysismachine-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.

Our team is working on the deployment of next-generation Vision-Language-Action (VLA) models for global autonomous driving products, and we’re looking for an intern to support data analysis, vector search, scenario classification, and long-tail scenario mining.

This role will focus on helping us identify and understand global region-specific driving scenarios, especially long-tail cases that matter for real-world model performance and deployment.

Responsibilities

~1 min read

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Analyze large-scale global driving data to identify long-tail and region-specific scenarios.
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Build workflows for vector search / semantic retrieval over scenario-related data.
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Apply ML and LLM-based methods for scenario classification, clustering, and tagging.
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Use embedding models, prompt engineering, and RAG-style pipelines to organize unstructured data and surface useful patterns.
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Support data mining, data cleaning, and scenario curation for VLA model improvement.
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Generate analyses, dashboards, and summaries to help the team understand failure cases and regional data gaps.

Requirements

~1 min read

Strong skills in Python, SQL, and data analysis.
Experience with machine learning pipelines, clustering, classification, or unsupervised learning.
Familiarity with embedding models, vector search, semantic retrieval, RAG, or prompt engineering.
Strong analytical thinking and comfort working with large datasets.
Interest in autonomous driving, multimodal ML, embodied AI, or VLA model.

What do we provide:

A fun, supportive and engaging environment.
Infrastructures and computational resources to support your work.
Opportunity to work on cutting edge technologies with the top talents in the field.
Opportunity to make significant impact on the transportation revolution by the means of advancing autonomous driving.
Competitive compensation package.
Snacks, lunches, dinners, and fun activities.

We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.