Xpengmotors

Xpengmotors

Senior AI Data Infrastructure/Pipeline Engineer

Santa Clara, CA · Senior · Full-time

Sponsorship not specified$175k-$296kDetected 48 days ago
PythonGoDistributed SystemsGitNoSQLPostgreSQLMySQLMongoDBRedisDockerKubernetesKafkaRabbitMQMachine LearningData EngineeringRoboticsCommunicationCollaborationProblem Solving

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.
  • In autonomous driving systems, the stability and efficiency of the pipeline directly determine the speed of algorithm iteration.
  • 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.

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.
  • Support business operations such as autonomous driving, smart cockpits, overseas data collection, and robotics data collection.
  • Solve bottlenecks in large-scale data transmission, memory management, I/O, etc., 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.
  • Collaborate with the large model team and other technical teams to deeply understand business requirements, respond quickly, and ensure successful implementation.
  • Infrastructures and computational resources to support your work.

Requirements

  • Bachelor's degree or higher in Computer Science, Software Engineering, Artificial Intelligence, or related fields.
  • 3-5+ years of experience in large-scale data processing or data platform development.
  • Proficiency in at least one programming language among Python / Go / Java.
  • Hands-on project experience in at least two of the following areas:
  • Production-level experience with distributed message queues (Kafka / Pulsar / RabbitMQ), familiar with stream processing paradigms.
  • Experience with distributed data lake systems (e.g., Apache Iceberg), familiar with Iceberg's table format, partition evolution, snapshot isolation, etc., with practical performance tuning and deployment experience.
  • Experience with columnar storage formats (e.g., Lance) and related query engines, with practical application in large model training.
  • 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.

Nice to have

  • Familiarity with closed-loop data in the embodied AI industry will be a huge plus.
  • Some understanding of the autonomous driving industry, awareness of data closed loop and data flywheel concepts, and enthusiasm for this field.
  • Experience with AI infrastructure or model training workflows (e.g., data loading, feature engineering, data preparation for model evaluation).
  • Familiarity with data lake / data warehouse systems, with practical experience implementing data version control and data lineage tracing.
  • Open-source contributions on GitHub or a technical blog, with continuous attention to the latest technological trends in big data / AI infrastructure.
  • What do we provide:
  • A fun, supportive and engaging environment.
  • Opportunity to work on cutting edge technologies with the top talents in the field.

Compensation

  • Competitive compensation package.
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