Embedding VC

Embedding VC

Founder in Residence

Palo Alto, CA · Exec

Sponsorship not specifiedDetected 255 days ago
LLMsResearchLeadership

About the role

  • Comfortable working in-person part of the week in San Francisco Bay Area.

Responsibilities

  • Embedding VC backs engineering, product, and research visionaries building the next generation of AI-native companies.
  • We partner early with founders inventing new paradigms in intelligence - from applied agents to infrastructure to creative computing.
  • We're opening applications for our Founder-in-Residence (FIR) program - a hands-on residency designed for exceptional builders who are ready to start their next AI venture.
  • You'll work side by side with our partners in San Francisco Bay Area, exploring new ideas and building from 0→1 with direct support from the Embedding VC network.
  • Work closely with Embedding VC partners to explore high-potential problem spaces.
  • Prototype rapidly, test assumptions, and validate early traction.
  • Collaborate with our network of AI researchers, engineers, and operators.
  • Access funding, design, and recruiting support once your direction is clear.
  • As a Founder-in-Residence, you'll collaborate with our partners and portfolio founders to define, prototype, and launch a new AI company.
  • You'll have access to capital, technical talent, and co-building support to accelerate your path from idea to company formation.

Nice to have

  • Previous startup founding or early technical leadership experience.
  • Tap into a strong AI ecosystem of technical advisors and investors.

Skills

  • Deep curiosity for AI systems, developer tools, or applied reasoning technologies.
  • Independent thinker who moves fast, iterates often, and thrives in ambiguity.
  • LLM-based systems, agents, or AI developer tools.

Benefits

  • Bonus if you've explored:

Company info

  • If you're ready to build at the frontier of AI, we'd love to meet you.

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