XPENG
smart technology
SeniorStaffPhysicalAIDataAlgorithmEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Staff Physical AI Data Algorithm Engineer at XPENG. Skills: multi-modal physical AI, AI data platform, data closed-loop, AI Agent-centric data closed-loop technology, data governance, data asset management, model development. establish strict corresponding relationships between data sets, annotation versions, and model versions. Explore the next-generation AI Agent-centric data closed-loop technology”
What You'll Achieve.
support problem attribution and iterative backtracking; promote the evolution of data closed-loop towards a higher level of intelligence; ensure model training effects; promote the landing of data closed-loop; make significant impact on the transportation revolution by the means of advancing autonomous driving
Industry & Context.
problem attribution; iterative backtracking
What They're Looking For.
Must Have
Master's degree or above in Computer Science, Artificial Intelligence, Automation, Vehicle Engineering or related majors, 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, integrated practical experience in on-vehicle data upload, cloud data processing, training and simulation integration, Familiar with the construction and use of data closed-loop toolchains, including data processing, mining, annotation, visualization and other modules, Have practical experience in the implementation of data lineage and version management, understand the importance of the association between data sets and model versions, have a sense of data asset management, Familiar with the entire life cycle of model development, deeply understand the key role of data in model performance (generalization, robustness, security), Able to analyze the data needs of the model at different stages, have the ability to define and evaluate high-quality data
Nice to Have
experience in large-scale AI training data governance, construction of data standard systems, data quality governance, data asset management, cost and efficiency optimization, practical experience in the implementation of massive multi-modal data production and circulation systems, relevant development or in-depth use experience in data closed-loop toolchains, Have research or practical interest in the direction of AI Agent-centric data closed-loop, have the ability to explore cutting-edge technologies, candidates with experience in guiding data production and annotation are preferred
What You'll Do.
establish strict corresponding relationships between data sets
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
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
evaluation to on-board deployment and continuous optimization
understand the specific data needs of the model at each stage
provide targeted data strategy support
Define high-quality data standards and guide data production
clarify the characteristics of high-quality data (diversity
guide data collection
cleaning and annotation work
ensure model training effects
How You'll Work.
Team & Collaboration
work efficiently with algorithms, engineering, annotation, testing and other teams to promote the landing of data closed-loop
Full Job Description
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 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. Requirements 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
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