The Senior Staff Physical AI Data Algorithm Engineer is responsible for designing and optimizing a vehicle-cloud data closed-loop architecture, managing data toolchains, and ensuring high-quality data standards throughout the model development cycle.
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.
Job Description
Define the overall architecture of the vehicle-cloud integrated data closed-loop from a strategic perspective, making it the core infrastructure that supports weekly model iteration, cross-regional large-scale expansion, and safe, compliant, and auditable operations. Lead the design of a highly available and scalable vehicle-cloud integrated data closed-loop system architecture, covering the entire link from on-vehicle data collection, encrypted upload, cloud access, preprocessing, storage, annotation scheduling, training data generation to simulation evaluation and feedback. At the same time, explore the next-generation AI Agent-centric data closed-loop architecture, formulate the architecture evolution roadmap from a strategic level, balance short-term delivery and long-term technical debt, and ensure the architecture has the scalability to meet business scale needs in the next 3-5 years.
Focusing on the development direction of embodied intelligence and model self-training, continuously explore new AI technologies and data Infra & toolchain development technologies to achieve efficient and high-quality flow of the entire link from data collection, data transmission, data processing, data clustering, data mining, data evaluation, data delivery to data effect feedback, and realize the continuous evolution of data lifecycle costs, data architecture and closed-loop engineering system.
Job Responsbilities
- 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.
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 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.
- Familiar with the construction and use of data closed-loop toolchains, including data processing, mining, annotation, visualization and other modules, with relevant development or in-depth use experience.
- Have practical experience in the implementation of data lineage and version management, understand the importance of the association between data sets and model versions, and have a sense of data asset management.
- Have research or practical interest in the direction of AI Agent-centric data closed-loop, and have the ability to explore cutting-edge technologies.
- Familiar with the entire life cycle of model development, and 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, and candidates with experience in guiding data production and annotation are preferred.
- Have good cross-team collaboration ability, able to work efficiently with algorithms, engineering, annotation, testing and other teams to promote the landing of data closed-loop.
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.
The base salary range for this full-time position is $203,450-$344,300, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
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.
XPeng Motors Palo Alto, California, USA Office
Palo Alto, CA, United States, 94301
XPeng Motors San Jose, California, USA Office
San Jose, United States
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