Senior Staff Physical AI Data Algorithm Engineer at XPENG — CAN | Base Career

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Responsible for the design and optimization of the vehicle-cloud integrated data closed-loop architecture: Build and maintain the full-link large closed-loop system from on-vehicle data upload to cloud training and simulation evaluation, ensuring efficient and secure data flow between the vehicle and the cloud to support rapid model iteration.
Build and maintain the data closed-loop toolchain: Lead the selection, development and integration of modules such as data processing links, data mining, collection and annotation tools, and visualization tools to improve the automation level and processing efficiency of data from original collection to usable data sets.
Establish data lineage and version management mechanisms: Design and implement a data lineage tracking system to achieve full-process traceability of data from production, processing to use; establish strict corresponding relationships between data sets, annotation versions, and model versions to support problem attribution and iterative backtracking.
Explore the next-generation AI Agent-centric data closed-loop technology: Research and introduce AI Agent-based automated data processing and mining methods, explore the application of Agents in scenarios such as scene recognition, annotation assistance, and simulation use case generation, and promote the evolution of data closed-loop towards a higher level of intelligence.
Support data work throughout the entire model development cycle: Deeply participate in the entire process of the model from data preparation, pre-training, fine-tuning, evaluation to on-board deployment and continuous optimization, understand the specific data needs of the model at each stage, and provide targeted data strategy support.
Define high-quality data standards and guide data production: According to the key needs of different models at different stages (such as basic capability building, shortcoming repair, generalization improvement, etc.), clarify the characteristics of high-quality data (diversity, representativeness, scarcity, authenticity, etc.), guide data collection, cleaning and annotation work, and ensure model training effects.
Master's degree or above in Computer Science, Artificial Intelligence, Automation, Vehicle Engineering or related majors, with more than 3 years of work experience in multi-modal physical AI or AI data platform. In-depth understanding of the architecture and process of multi-modal physical AI data closed-loop, with integrated practical experience in on-vehicle data upload, cloud data processing, training and simulation integration.
Candidates with experience in large-scale AI training data governance are preferred, including the construction of data standard systems, data quality governance, data asset management, cost and efficiency optimization, as well as practical experience in the implementation of massive multi-modal data production and circulation systems.

Senior Staff Physical AI Data Algorithm Engineer

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