Full-Time

AI Product Manager

Intent Lab

Intent Lab

1-10 employees

Autonomous AI software engineering platform

No salary listed

Sunnyvale, CA, USA

In Person

Category
Product (1)
Required Skills
Product Management
Machine Learning

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Requirements
  • At least 4 years of product management experience, ideally with developer tools, infrastructure, artificial intelligence products, enterprise software, or other technical products.
  • Strong technical fluency across application programming interfaces, developer workflows, system architecture, and artificial intelligence product tradeoffs.
  • Experience working on ambiguous, early-stage products where the roadmap is not predefined.
  • Strong product judgment to distinguish impressive demonstrations from durable user value.
  • Excellent writing and communication skills.
  • Genuine interest in artificial intelligence-assisted software development.
  • Ability to operate independently and bring structure to fast-moving problems.
Responsibilities
  • Define product direction for artificial intelligence-assisted software development workflows.
  • Translate customer needs, technical capabilities, and company strategy into roadmap priorities.
  • Identify the product primitives, workflows, and user experiences that should become core to Intent Lab.
  • Spend time with developers, engineering leaders, and enterprise customers to understand their workflows and pain points.
  • Run discovery with design partners, early users, and internal teams.
  • Turn qualitative feedback and usage patterns into clear product decisions.
  • Work closely with engineering, artificial intelligence, design, Developer Relations, and enterprise teams to ship high-quality product experiences.
  • Write clear specifications, user stories, launch plans, and decision memos.
  • Prioritize across technical, customer, and business needs.
  • Partner with engineering and artificial intelligence teams to evaluate model behavior, reliability, latency, cost, and user trust.
  • Define success metrics, failure modes, and quality thresholds for artificial intelligence-powered features.
  • Help design feedback loops that improve the product over time.
  • Collaborate on product positioning, launch narratives, demonstrations, and customer-facing materials.
  • Support pilots, design partner engagements, and technical customer conversations.
  • Help define onboarding, activation, and adoption strategy for early customers.

Intent Lab builds Fleet, an autonomous AI system that acts as an engineering team to turn high-level intents into production-grade software, handling architecture, coding, deployment, testing, and ongoing optimization. Fleet takes natural-language requests and creates software tailored to a user’s constraints and workflow, instead of just generating code. It differentiates itself by delivering end-to-end lifecycle management with built-in quality and performance checks, resulting in production-ready systems. The company aims to shift how software is created by generating and refining software directly from user intent, producing fully verified solutions customized to each task.

Company Size

1-10

Company Stage

N/A

Total Funding

N/A

Headquarters

San Francisco, California

Founded

2026

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Simplify Jobs

Simplify's Take

What believers are saying

  • Jia's July 28 launch post drew massive attention, signaling strong founder-led distribution.
  • July 2026 demos showed 6.3x GLM-5.2 speedup and 6-million-test database execution.
  • Ashby's July 15 product-manager posting shows Intent Lab is hiring for commercialization.

What critics are saying

  • Intent Lab disclosed no customers, pricing, or revenue, leaving July 2026 traction unproven.
  • OpenAI Codex, Cursor, and Anthropic already own developer mindshare and enterprise distribution.
  • Fleet must prove reliability against production outages, or enterprises will reject autonomous delivery.

What makes Intent Lab unique

  • Yangqing Jia launched Intent Lab on July 28, 2026, bringing Caffe and ONNX credibility.
  • Fleet targets full software delivery, not just code completion, spanning architecture through verification.
  • Early demos include GLM-5.2 inference, one-shot SQLite creation, and verified filesystem automation.

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