woodsideai
Founding CEO (Edtech)
Bay Area (SF/Palo Alto) · Exec
Sponsorship not specifiedDetected 273 days ago
CanvaResearchLeadershipCommunication
About the role
- This person will take the concept, prototype, and research developed by our venture studio and turn it into a thriving company.
- As Founding CEO, you'll be responsible for assembling the team, executing the business plan, and taking the product from early prototype to full-scale deployment.
Responsibilities
- Founded by industry titans and a top AI researcher from OpenAI, Woodside AI combines a proven track record of venture building and technical innovation.
- We're building an AI-powered EdTech company from the ground up - and we're looking for a Founding CEO to lead it.
- You'll have access to the studio's technical resources, top-tier AI talent, and go-to-market connections, enabling you to focus entirely on building and scaling the business.
Nice to have
- Technical or product background (CS degree or equivalent experience)
- Prior experience taking a product from prototype to commercial launch
- Experience leading or working closely with AI-focused teams
Skills
- Deep leadership experience in EdTech or education-focused software
- Experience as a founder, co-founder, or early leader at a venture-backed startup
- Strong business acumen and operational discipline
- Comfort navigating uncertainty and achieving product-market fit
- Excellent communication and storytelling skills
Benefits
- You'll work closely with the studio leadership and founding product and engineering team to define the company vision, drive product-market fit, and lead the organization through its first critical stages of growth.
- This is a rare opportunity to be a fully backed founder - no fundraising required - and to build something transformative in the education space.
Company info
- We are fully self-funded and focused on tackling complex industry challenges unlikely to be solved by traditional startups.
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This listing is sourced directly from woodsideai's careers page and normalized into a canonical job model.