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Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
Learnings from founding a Computer Vision Startup: Chapter 4 Team
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Learnings from founding a Computer Vision Startup: Chapter 4 Team

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  • 1. 4. Team
  • 2. Learnings from founding a Computer Vision Startup Flickr: zacharyparadis Part 1: Founders
  • 3. Learnings from founding a Computer Vision Startup How many? 1-4 “reasonable” size of founder team 1 founder: total control but expensive, lonely and slow 2 founders: the magic number. “one builds, one sells” 3-4 founders: can make a great dev team but leader needed >4 founders: crazy unless some are “passive” “Everyone obsesses with dilution from investors. The biggest dilution comes from co-founders.” – @msuster
  • 4. Learnings from founding a Computer Vision Startup 2 founder dynamics Jobs and Wozniak Allen and Gates Hewlett and Packard Larry and Sergei Yang and Filo “The ideal founding team is two individuals, with a history of working together, of similar age and financial standing, with mutual respect. One is good at building products and the other is good at selling them” – @venturehacks http://venturehacks.com/articles/pick-cofounder Flickr: oskay
  • 5. Learnings from founding a Computer Vision Startup Finding founders & early employees Share ideas, be open Co-founders need to be people you trust, preferably people you worked with before You want broad skills and doers Controversial but interesting: vesting founder shares (VCs will ask for this)
  • 6. Learnings from founding a Computer Vision Startup Size matters Output is not linear in # employees Most efficient (development) team is ~4 Over ~15 “formal” organization and information channels becomes increasingly important Avoid hiring admin staff or positions that don’t “produce” anything (non-coding proj manager, office manager...) as long as possible. Ideally, never hire such persons.
  • 7. Learnings from founding a Computer Vision Startup Flickr: yodelanecdotal Part 2: Hiring staff
  • 8. Learnings from founding a Computer Vision Startup Where to find them? 1. By recommendation (both ways!) 2. Events and conferences 3. LinkedIn! Avoid recruitment agencies at all cost. Waste of time and money. Never hire unless you absolutely must. Are you sure you need to hire now? Flickr: bouldair
  • 9. Learnings from founding a Computer Vision Startup Interviewing and testing Early on, look for people that are bright, broad with potential to grow Always test developers Don’t underestimate team fit (personality) Never hire without trial period Computer vision team should have a mix of skills “theoretical”-”practical” and people that can make demos.
  • 10. Learnings from founding a Computer Vision Startup Stock options and motivation Motivation Money is not motivation (competitors ALWAYS pay more) Good motivators are similar to founders’ motivators (build something, dynamic organization, make a difference, ...) Warning: people with “big-co” motivation (titles, salary, career, benefits, “security”) Stock options Good idea if exit is the goal If possible create options pool post investment Beware: rules and taxes vary a lot between countries!
  • 11. What is special about Vision? In terms of building teams
  • 12. Learnings from founding a Computer Vision Startup What’s special about Vision? Academic research groups can be a great “extension” to your R&D team Small vision teams can go far, no need to overstaff
  • 13. How we did it
  • 14. Learnings from founding a Computer Vision Startup Polar Rose: How we did it 1. Founder + key early employees 2. Built 2 dev teams (vision + infrastructure) Hiring process: Initially friends and connections from university Networks of a few key employees (especially France and Poland) LinkedIn - search, search, search Trial period in ALL contracts Stock options (legal & tax mess with multi-national team)
  • 15. Learnings from founding a Computer Vision Startup Kooaba: How we did it Founders + 1 key early employee (first employee needed to be changed, lost lots of time) Built two dev teams (vision + interfaces (web, mobile)) Was hard to find initial employees Work permit problems Hiring process: Initially friends and connections from university Networks of a employees We are hiring later this year!! LinkedIn - post job offer (299 is cheap) Sales & Marketing hired from customer (EMI Music) Employees are involved in interviewing process Recently: testing day Stocks (promised, formalized these days)
  • 16. Q&A
  • 17. Learnings from founding a Computer Vision Startup Resources Founders: http://venturehacks.com/articles/pick-cofounder (Picking co-founders) Hiring: xxx

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