We are looking for a product manager to define and build general-purpose Agent products, connecting real user needs, product experience, and the capability boundaries of AI Agents. This role will focus on identifying high-value Agent use cases, defining product features and interaction flows, and designing a data flywheel that turns user behavior, failure cases, human corrections, and evaluation results into continuous product and model improvement.
Responsibilities
- Research, analyze, and abstract high-value use cases for general-purpose AI Agents.
- Define product features, user journeys, interaction flows, task success criteria, priorities, and launch plans.
- Translate user behavior, failure cases, human corrections, and evaluation results into scalable, high-quality evaluation and training data.
- Design mechanisms for data collection, annotation, feedback, quality control, and iteration.
- Build a closed-loop data flywheel that connects product usage with Agent and model capability improvement.
- Collaborate closely with engineering, research, and operations teams to drive ideas from PRDs, prototypes, and experiments to production launch and validation.
- Track product usage and model performance, identify key issues, validate hypotheses, and continuously improve product experience and Agent capabilities.
Requirements
- - Basic understanding of LLMs, AI Agents, reinforcement learning, and model training or post-training workflows.
- - Strong interest in general-purpose Agent products and enthusiasm for building product-data flywheels.
- - Experience in product management, data product design, AI product operations, or related roles.
- - Ability to define clear requirements, structure ambiguous problems, and coordinate cross-functional execution.
- - Strong data sense, with the ability to evaluate data quality and connect data decisions to product outcomes.
- - Excellent communication and collaboration skills across research, engineering, data, and business teams.
- - Experience with AI Agents, LLM evaluation, RLHF/RLAIF, data annotation platforms, or model training data pipelines is a strong plus.
Basic understanding of LLMs, AI Agents, and reinforcement learning.
Strong product judgment, with the ability to identify user needs, define problems, and design practical product solutions.
Strong structured thinking and analytical skills, with the ability to break down complex user scenarios and Agent workflows.
Ability to turn user behavior, failure patterns, and evaluation results into actionable data and product strategies.
Strong cross-functional collaboration and project management skills, with the ability to work effectively with engineering, research, and operations teams.
Strong passion for general-purpose Agent products and data flywheel systems.
Experience with AI products, data products, developer tools, automation tools, B2B SaaS, or growth products.
Familiar with model evaluation, user behavior analysis, data annotation workflows, or AI coding tools is a plus.
