Dedalus Labs

Dedalus Labs

Product Manager Summer 2027 Intern

San Francisco, CA, US · Intern · Internship

Sponsorship not specified$4k-$8kDetected 20 days ago
Agentic AIFigmaUX ResearchProduct StrategyLogisticsResearchProblem Solving

About the role

  • Please submit your application at: https://www.dedaluslabs.ai/careers?ashby_jid=f38049e1-5804-4ee2-b13c-9e47921e1a76 Only candidates who filled out our form above will be considered.
  • You might be a fit if you Are obsessed with understanding users and how they think.

Requirements

  • Ability to quickly understand complex technical systems.

Nice to have

  • Previous startup, product, or founder experience.
  • Familiarity with developer tools, AI products, or cloud infrastructure.
  • Experience conducting user interviews or customer research.
  • Experience with Figma, Linear, Notion, or modern product tooling.
  • Technical background in computer science or engineering.
  • Hackathon, side project, or entrepreneurial experience.
  • Strong analytical skills and experience using product metrics.
  • You know what good looks like.

Skills

  • Please submit your application at: https://www.dedaluslabs.ai/careers?ashby_jid=f38049e1-5804-4ee2-b13c-9e47921e1a76
  • Only candidates who filled out our form above will be considered.
  • You might be a fit if you
  • Are obsessed with understanding users and how they think.
  • Believe great products begin with asking the right questions.
  • Enjoy talking to users more than making assumptions.
  • Can quickly understand difficult technical concepts.
  • Think good product decisions come from balancing user needs, engineering constraints, and business priorities.
  • Have strong opinions about developer tools, AI products, or where software is headed.
  • Believe simplicity is usually harder than complexity.
  • Care deeply about product quality and the details users notice.
  • Are high agency and fiercely independent.

Compensation

  • $4k-$8k

This listing is sourced directly from Dedalus Labs's careers page and normalized into a canonical job model.