Role Summary
We are looking for a Product Manager to lead the development of our AI Data Platform, responsible for building scalable data infrastructure that powers next-generation AI and robotics applications.
You will work closely with AI Researchers, Machine Learning Engineers, Data Engineers, and Software Engineers to design end-to-end data workflows—from data collection and annotation to data governance and dataset management—ensuring high-quality datasets for AI model training.
This role is ideal for someone with strong product management experience and a solid understanding of AI data operations and data infrastructure.
Key Responsibilities
- Plan product features for AI data collection, annotation, and data management platforms, including writing PRDs, developing product roadmaps, managing the product development process, and working with the engineering team to define product architecture.
- Plan the full lifecycle of AI training data, including: Data Collection, Data Upload, Data Validation, Data Cleaning, Annotation, Quality Review, Dataset Release, and Dataset Version Management, while continuously optimizing process efficiency and product functionality.
- Establish AI data quality management mechanisms, including: Data Quality Validation, Metadata Management, Annotation Guideline, Dataset Version Control, Data Governance, and Data Traceability, to support the development of high-quality datasets that can be continuously used for AI model training.
- Collaborate with AI Researchers, ML Engineers, and Data Engineers to analyze data requirements for different models, and help define data formats, Label Schemas, Metadata, and Dataset structures.
Requirements
- 3+ years of experience in software product management
- Ability to independently complete PRDs, workflows, and product specification design
- Background in at least one of the following data-related areas: Data Platform / Data Management / Data Governance / DataOps / Dataset Management / AI Data Platform / Annotation Platform
- Knowledge of data-related concepts, including but not limited to Data Quality / Data Validation / Metadata / Dataset Versioning / Annotation Workflow / Data Governance
- Knowledge of AI-related concepts, such as LLM / RAG / Multi-Agent / Embedding / Vector Database / AI Training Pipeline. Model development experience is not required, but the ability to communicate effectively with AI engineering teams is expected.
Preferred Qualifications
- Experience participating in AI Platform, Data Platform, or AI Infrastructure projects
- Familiarity with annotation tools such as Label Studio, CVAT, or Supervisely
- Familiarity with SQL or Python
- Experience participating in Physical AI or Robotics projects
- Experience related to Data Engineering, Data Analysis, or Machine Learning Platforms