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AI Model Platform Product Manager

This role goes beyond building admin interfaces and scheduling feature work. We’re looking for someone who understands the post-training data business and can translate complex tasks, expert collaboration, and quality requirements into platform capabilities. Your main deliverable will be a platform that supports data production, quality management, and customer delivery. You’ll work with data strategy, algorithms, engineering, and delivery teams to improve data quality and production efficiency and scale delivery through product development.

What you’ll do

  1. 01Own product planning and iteration for the post-training data platform, supporting the full workflow from task definition and data production through quality review and acceptance to delivery.
  2. 02Develop a deep understanding of business needs and expert workflows, and translate common needs across use cases into reusable platform capabilities.
  3. 03Refine the platform experience and collaboration workflows, using business feedback and data analysis to continuously improve quality, efficiency, and automation.
  4. 04Partner with algorithms, engineering, and delivery teams to deliver the product, taking responsibility for its effectiveness in practice and its business results.

What we look for

  1. 01A bachelor’s degree or higher in any field, with experience in platform products, B2B products, data platforms, or AI products.
  2. 02Experience building a platform from 0 to 1 or significantly improving quality, efficiency, and delivery scale through product changes, with the ability to explain key decisions and actual results.
  3. 03Strong business acumen and skills in process abstraction and system design; able to turn ambiguous requirements into clear solutions while balancing different business needs with platform reuse.
  4. 04Strong cross-team collaboration and execution skills; comfortable working at an early-stage startup, setting priorities independently, and driving implementation.

Nice to have

  • Experience in data annotation, expert collaboration, human-machine collaboration, model evaluation, or machine learning platforms.
  • Familiarity with LLM, agent, post-training, eval, and AI data pipeline concepts, with your own perspective on AI data production.
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