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Chinese Annotation Jobs (NOW HIRING)

Data Operations Engineer

Mountain View, CA · On-site

$136.30K - $163.60K/yr

... Chinese and English is required, as this role involves frequent collaboration with China-based ... with data annotation, labeling workflows, or dataset preparation for machine learning. • ...

Support data annotation, curation, and quality control processes * Summarize findings into ... English | Italian | Spanish | German | French | Japanese | Chinese (Mandarin) | Korean | Arabic

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Chinese Annotation information

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$15

$24

$53

How much do chinese annotation jobs pay per hour?

As of Jun 4, 2026, the average hourly pay for chinese annotation in the United States is $24.41, according to ZipRecruiter salary data. Most workers in this role earn between $16.83 and $25.48 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Chinese Annotation Specialist, and why are they important?

To thrive as a Chinese Annotation Specialist, you need strong proficiency in written Chinese, attention to linguistic details, and a background in linguistics or a related field. Familiarity with annotation tools, data labeling platforms, and sometimes basic programming or scripting is commonly required. Excellent communication, time management, and a keen eye for accuracy help specialists excel in this role. These skills ensure high-quality, consistent data labeling essential for developing reliable AI and machine learning models.

What are some common challenges faced by Chinese Annotation specialists when working with multilingual datasets, and how can they be addressed?

Chinese Annotation specialists often encounter challenges such as handling language nuances, regional dialects, and context-specific meanings within multilingual datasets. Ensuring consistency in labeling and understanding subtle differences in word usage can be demanding. These challenges are typically addressed through thorough training, clear annotation guidelines, and regular team discussions to resolve ambiguities. Collaboration with linguists and native speakers also helps maintain accuracy and quality in annotations.

What is Chinese annotation?

Chinese annotation is the process of labeling, tagging, or marking up Chinese language data—such as text, audio, or images—for use in training artificial intelligence and machine learning models. This work often involves identifying parts of speech, entities, sentiment, or translating and transcribing spoken Chinese. Annotators need to have strong proficiency in the Chinese language and a good understanding of linguistic nuances to ensure high-quality, accurate data. Chinese annotation is vital for improving natural language processing applications like chatbots, translation tools, and voice recognition systems.

What is the difference between Chinese Annotation vs Data Labeling?

AspectChinese AnnotationData Labeling
Required CredentialsBasic understanding of Chinese language and annotation toolsGeneral knowledge of labeling techniques, sometimes specific to data types
Work EnvironmentData annotation platforms, remote or office-basedData annotation platforms, remote or office-based
Industry UsageNatural language processing, AI training for Chinese language modelsMachine learning, AI training across various data types and languages
Search & Comparison IntentUnderstanding specific language annotation rolesBroader data labeling roles across industries

Chinese Annotation focuses on labeling Chinese language data, primarily for NLP applications. Data Labeling is a broader role involving tagging various data types, including images, audio, and text, across multiple languages. While Chinese Annotation is specialized, Data Labeling covers a wider range of data preparation tasks for AI models.

Senior Staff Physical AI Data Algorithm Engineer

XPENG

Santa Clara, CA • On-site

$124.40K - $169.10K/yr

Full-time

Posted 17 days ago


Job description

Job Summary:
XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles. The role involves defining the architecture of a vehicle-cloud integrated data closed-loop system, leading its design and optimization, and ensuring efficient data flow to support model iteration and compliance.
Responsibilities:
• 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.
Qualifications:
Required:
• 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 research or practical interest in the direction of AI Agent-centric data closed-loop
• 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
• Have good cross-team collaboration ability
Preferred:
• Candidates with experience in large-scale AI training data governance are preferred
• Experience in the 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
• Candidates with experience in guiding data production and annotation are preferred
Company:
XPENG is a leading Chinese Smart EV company that designs, develops, manufactures, and markets Smart EVs that appeal to the large and growing base of technology-savvy middle-class consumers. Founded in 2014, the company is headquartered in Guangzhou, CHN, with a team of 10001+ employees. The company is currently Late Stage.