The Startup Playbook for Hiring Your First Engineers and AEs

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Most founders think hiring is about interviewing. But it’s actually about selling.

For Startup School, Juicebox co-founder & CEO David Paffenholz joins YC’s Harj Taggar to share how early-stage founders can find, pitch, and close top engineering and sales talent— from crafting better outreach to winning great hires from Big Tech— even when you’re an unknown startup.

Apply to Y Combinator: https://www.ycombinator.com/apply
Work at a startup: https://www.ycombinator.com/jobs

Chapters:
0:00 – Why your first hires matter
1:17 – What great candidates actually want
4:38 – How to sell your startup to top talent
7:49 – Finding and sourcing great engineers
13:17 – Writing outreach that gets replies
20:58 – Interviewing: sell first, assess second
25:59 – Closing fast and competing with Big Tech
31:06 – Common mistakes founders make
38:52 – When (and how) to hire a recruiter
43:11 – Final thoughts

From: Y Combinator

Date: November 7, 2025

9 thoughts on “The Startup Playbook for Hiring Your First Engineers and AEs”

I want to get into Y Combinator

Anything for candidates on platform?

I built a similar tool “speeduphire”

Thanks for giving specific numbers to determine if your outreach is great, good or terrible
and for the reminder that everything is (quickly) being read on a phone so we should construct our emails accordingly

Normally, how long after starting the company would you get Seed Round and Series A funding for a SaaS company.

This will be useful for my agentic video editor startup im bootstrapping myself.

Without the creator of Napster, there would be no Facebook; it’s all about shares and strategic influence.
The rest is just fairy tales for children.

Of course, only a few startups have a real chance to attract top leaders, because few have such an idea/ vision; and the right founder (like Manus AI).

And in the age of AI, this will be even more extremely difficult- an AI strategist is worth gold and only the biggest companies have bags of gold ;)))

I wish the project success. In my opinion, neural network agents are still at an early stage of development. There’s a lot of work ahead on combining highly specialized neural networks with agents that can flawlessly use tools and follow instructions. At the moment, API calls from LRM are somewhat uncontrollable, so the work on agent–neural network interaction should focus on fine-tuning the network itself. However, learning and practice through LRM have a future—where a person can keep learning continuously, not just spend five years at university and then gain experience at work.