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AI Fund

Engineer in Residence, AICoS

Mountain View, CAcontractmidAdded today

About this role

Join AI Fund's 12-week residency to build AICoS, an AI-powered chief of staff that automates post-meeting coordination work for managers. Transform meeting transcripts into automatically sorted action items, assignments, and follow-ups across existing tools like Asana, Notion, and Slack. If the product validates, transition into a co-founder role.

What you'll do

  • Build the core product that extracts and structures action items from meeting transcripts using LLMs
  • Integrate with existing tools (Asana, Notion, Slack) to enable automatic sorting and assignment
  • Validate product-market fit by testing whether auto-sorting drives daily adoption among managers
  • Conduct user research and iterate based on manager feedback during the 12-week sprint
  • Develop the work graph understanding layer that contextualizes tasks within project structures
  • Ship a working prototype that demonstrates reliable action item extraction and routing

What they're looking for

  • Full-stack product development (frontend and backend)
  • LLM integration and prompt engineering
  • API design and third-party integrations (Asana, Notion, Slack APIs)
  • Product validation and user research
  • Meeting transcript processing and NLP
  • Database design for task management and work context
  • Rapid prototyping and iteration
  • Technical collaboration with non-technical founders

Benefits

  • Work directly with Andrew Ng and AI Fund mentors
  • Potential to become a co-founder if idea validates
  • Backed by $390M fund from top-tier global investors
  • 12-week intensive residency in Mountain View with housing and support
  • Access to AI Fund's network of builders and domain experts
  • Path to capitalization and company formation
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AI Fund

AI Fund builds and invests in AI-powered applications spanning healthcare, productivity, manufacturing, and developer tools. The company is hiring AI Engineers, ML specialists, and full-stack engineers to deploy production machine learning solutions, develop AI-driven platforms for chronic care and business intelligence, and contribute to open-source AI infrastructure projects.

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Likely interview questions

  • Walk us through how you'd architect the system to reliably extract structured action items from diverse meeting formats and transcripts.
  • How would you approach integrating with multiple task management platforms (Asana, Notion, Slack) given their different data models and APIs?
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