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 builds platform capabilities that support the development and deployment of Autonomous Driving AI models. We work closely with Machine Learning Engineers to improve efficiency, quality, and reliability of the experiment lifecycle, from planning and execution to analysis and deployment readiness.   We are building an LLM-powered agent to help MLEs collect experiment context, analyze experiment progress and results, and surface useful insights across ongoing model development work.   We’re looking for an intern to help build the data foundation for this agent, with a focus on cleaning, organizing, and connecting various data sources, especially noisy chat and meeting data. The intern will help build data pipelines and LLM-assisted data cleaning workflows that allow the agent to correctly retrieve, interpret, and reason over experiment-related information. Depending on progress and interest, the intern may also help fine-tune LLM-based models using curated experiment data to improve agent performance on domain-specific tasks.  

Key Responsibilities

• Build pipelines to ingest and organize experiment-related data from team communications, meeting notes, experiment plans, analysis documents, metrics, and evaluation results.
• Use LLM-based methods to clean noisy unstructured data, extract experiment-relevant information, and convert fragmented discussions into structured records.
• Design data schemas, metadata, and quality checks that make experiment context easier to search, trace, and use in downstream agent workflows.
• Support retrieval and indexing workflows, including semantic search or RAG-style pipelines, so the agent can access relevant experiment context.
• Prepare curated datasets for agent evaluation and, where applicable, LLM fine-tuning or instruction-tuning.
• Work with MLEs and platform engineers to understand experiment workflows, data gaps, and the types of insights most useful for planning and analysis.
• Evaluate whether the agent uses curated experiment data correctly to generate summaries, comparisons, recommendations, and analysis insights.
• Contribute to internal tools, dashboards, or reports that help teams monitor experiment status, outcomes, and trends. 

Qualifications

• Strong skills in Python, SQL, and data processing.
• Experience working with structured and unstructured data, including text-heavy sources such as documents, notes, messages, or logs.
• Familiarity with data pipelines, ETL workflows, or large-scale data processing.
• Interest in LLM development, LLM evaluation, agentic AI systems, RAG pipelines, semantic retrieval, prompt engineering, or LLM-assisted data processing.
• Familiarity with machine learning workflows, model training, evaluation metrics, or MLOps concepts.
• Strong analytical thinking and attention to data quality, consistency, and reliability.
• Comfort working with ambiguous data sources and collaborating with ML and platform engineers to clarify requirements.
• Previous experience building internal tools, automation scripts, or data quality checks.

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.