Product Manager AI Native Intelligent Computing Platform
Beijing-based enterprise AI company building foundation-model, agentic-AI, and AI-transformation products, including the TrueNorth enterprise decision hub.
Funding history
Investors
About 01.AI
01.AI develops full-stack enterprise AI solutions, industry-agent applications, and sovereign/industry model-training capabilities.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will own the intelligent computing platform product lifecycle, from user research and positioning through requirements, release, feedback, and iteration. You will improve the platform with AI-native capabilities, validate AI features, collaborate with technical and delivery stakeholders, and package proven internal capabilities for B2B pilots.
Requirements
- At least 1 year of product management experience
- Experience with AI, cloud platforms, SaaS, developer tools, or B2B products is preferred
- Experience independently owning a complete product, subproduct, or significant product area
- Ability to use AI for research, analysis, design, writing, and validation
- Basic understanding of large language models, model training, model inference, GPU computing, or AI agents
- Ability to read basic API documentation, JSON, logs, and error messages
- Ability to communicate effectively with engineering stakeholders
Responsibilities
- Own the full product lifecycle, including research, positioning, requirements, roadmap, delivery, launch, and iteration
- Observe internal users to identify frequent, repetitive, and complex problems
- Drive AI-native platform capabilities using natural language, agents, tool calls, risk detection, and conversational task completion
- Use AI for research, requirements analysis, competitive research, prototyping, documentation, data analysis, and test-case generation
- Validate AI features using task completion, accuracy, failure, latency, token-cost, and human-intervention metrics
- Design products with algorithm, engineering, and operations stakeholders around model preparation, training, evaluation, deployment, and observability
- Measure product adoption and effectiveness among internal users
- Package validated capabilities into standard processes, documentation, demonstrations, and B2B pilot plans
- Coordinate with engineering, algorithm, design, testing, delivery, and customer-success stakeholders