Senior Staff AI Data Infrastructure/Pipeline Engineer

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. As a core member of our AI Infrastructure team , y ou will be responsible for building the end-to-end data pipeline for autonomous driving, covering the entire chain from onboard data upload → cloud-based preprocessing → dataset production → model training / simulation input.

In autonomous driving systems, the stability and efficiency of the pipeline directly determine the speed of algorithm iteration. We look forward to building a reliable, observable, and cost-effective data pipeline that supports the daily flow of petabyte-scale sensor data.

Key Responsibilities

Responsible for the design and construction of core data closed loop pipelines. Develop toolchains for data cleaning, annotation quality inspection, and data mining to support the algorithm team in quickly locating model error cases and driving iterative model optimization. Data Support for Production and R D Processes.

This includes log event tracking, connected vehicle data, internal and external data collection, data synchronization, data cleaning and standardization, data modeling, offline and real-time data processing, data as a service, and data visualization. Support business operations such as autonomous driving, smart cockpits, overseas data collection, and robotics data collection. Responsible for optimizing the performance of the entire data pipeline (collection, cleaning, conversion).

, and build a distributed data processing system with high throughput and low latency. Responsible for building a data management platform covering the entire process from data collection to data lake ingestion to model training. Implement capabilities for data version control, data lineage tracing, metadata management, and fast data retrieval to support unified data access and collaboration across multiple teams.

Requirements

, respond quickly, and ensure successful implementation.

Qualifications

Bachelor's degree or higher in Computer Science, Software Engineering, Artificial Intelligence, or related fields. 5-8+ years of experience in large-scale data processing or data platform development. Proficiency in at least one programming language among Python / Go / Java.

Solid software engineering foundation, good coding standards, and a strong sense of code quality. Hands-on project experience in at least two of the following areas: Design and development of large-scale data pipelines / ETL systems, with end-to-end experience in data cleaning, transformation, and loading. Production-level experience with distributed message queues (Kafka / Pulsar / RabbitMQ), familiar with stream processing paradigms.

, with practical performance tuning and deployment experience. , Lance) and related query engines, with practical application in large model training. Hands-on experience using and optimizing relational databases (MySQL / PostgreSQL) and NoSQL databases (Redis / MongoDB).

Understand metadata management and caching strategies. Experience in performance optimization and troubleshooting for large-scale distributed systems, able to quickly locate and resolve complex performance bottlenecks. Experience with Kubernetes / Docker containerization deployment.