Hiring AI Engineers at a Startup: A Founder's Guide

TL;DR: Hiring AI engineers at a startup means winning talent you can't outbid the big labs for — by offering equity, agency, and an in-person mission instead of a top-of-market salary. Based on 200+ PMF Show interviews, the expected financial value of a startup job is often lower than big tech, so founders win engineers with meaningful equity (founding engineers warrant more than the standard 1.5%–2%), culture, and the chance to build from scratch.

After interviewing 200+ founders on the PMF Show, the hardest hiring problem in 2025–2026 is consistent: how do you hire AI engineers when OpenAI, Google, and well-funded labs can pay multiples of what you can? The founders who win don't try to match the salary. They reframe the offer around equity, ownership of hard problems, in-person energy, and where they look for talent. This guide pulls together exactly how they do it.

How do you hire AI engineers when you can't match big-lab salaries?

You don't compete on cash — you compete on a different axis. Robert at Float laid out the discipline: benchmark compensation to your real cohort, not to the companies you can never outbid.

"We have to be thoughtful about cash conservation, but it's also just not worth it because if we don't work with the best people, we're not going to have success anyways." — Robert, Float

Float benchmarked cash salaries to the 80th–90th percentile of its actual peer set — seed and Series A companies — while being clear that this would never match Google, Shopify, or Amazon. The key move is honesty about your comparison set: you're not in a bidding war with OpenAI, so don't price as if you are.

Peter Walker of Carta adds the sobering truth founders must internalize: the expected value of a big-tech job is usually higher than a startup seat. "The expected value is much higher at Google," he noted. That means your pitch has to sell what Google can't — and that starts with equity.

Key stat: Float benchmarks engineering cash comp to the 80th–90th percentile of seed/Series A peers — explicitly not big tech — while acknowledging the expected value is higher at Google.

How much equity should you give a founding AI engineer?

More than the default — especially if you're a solo founder. The standard first-engineer grant on Carta runs about 1.5%–2%, but Peter Walker argues founders sitting on extra equity should deploy it on early technical talent.

"What I hope happens is that these companies built by a solo founder grant equity to their early team in a different way. Higher levels of equity granted to those founding engineers... if you have that extra forty percent of equity, use it on the early team." — Peter Walker, Carta

This is your sharpest tool against the labs. A big lab can offer cash and liquid stock, but it cannot offer a meaningful slice of a company that could become enormous. For an AI engineer choosing between a guaranteed-comfortable big-lab package and asymmetric startup upside, equity is the lever that makes the math interesting. The founders who win the best engineers are the ones who treat early equity as a recruiting investment, not a cost to minimize.

Key stat: First engineers typically receive ~1.5%–2% equity on Carta — but solo founders are advised to grant founding engineers meaningfully more, using their ~40% equity surplus.

Where do the best founders actually find AI engineers?

Two very different but proven sourcing strategies show up on the show. Michael Lingelbach of Hedra hires experienced builders from later-stage, high-growth startups — people who've seen systems built from scratch but also know what a high-growth pace feels like.

"The people that are best for setting up a seed stage company are the people that did a series A to series C stint. Where they've seen completely green fielded systems. They know how to spin up things from scratch, but they also... know how to build a company culture." — Michael Lingelbach, Hedra

Lingelbach noted that early on Hedra hired fresher talent, but as it matured, most hires came from well-known, later-stage high-growth startups — engineers who were there early at the last company.

The opposite end works too. Roy Lee of Cluely makes the case for recruiting straight out of college, where the density of brilliant, available people is unmatched.

"In college, you have the unique opportunity to regularly be forced to interact with like a hundred people, thousands of people your age, and every single one of them could potentially be your... co-founder." — Roy Lee, Cluely

Key stat: Hedra shifted from hiring fresh talent to recruiting engineers from later-stage, high-growth startups — specifically those who joined the previous company at the Series A–C stage.

Does in-person work matter when hiring AI engineers?

For several founders, it's a deliberate edge — not a constraint. Shensi Ding of Merge built an in-person company from day one in 2020, during COVID, because the velocity difference was obvious.

"Every morning I'd bike over to Gil's apartment in San Francisco and we would code, and it was so much fun. And we were moving so fucking fast... then we would do a few days where we would be working remotely, and the progress was just significantly slower." — Shensi Ding, Merge

Ding used that in-person magic as both a productivity strategy and a filter: not everyone was willing to work in person, which naturally selected for the high-intensity engineers Merge wanted. For AI startups racing against well-resourced labs, in-person intensity can be a genuine recruiting differentiator — it signals a team that ships fast and a culture engineers either opt into enthusiastically or self-select out of.

Key stat: Merge committed to in-person work from day one in 2020 after observing that fully remote days produced "significantly slower" progress — using it as both a velocity lever and a hiring filter.

What's the biggest mistake founders make hiring AI engineers?

Moving too slowly on a hire that isn't working — and over-indexing on preserving institutional knowledge. Bhaskar Sunkara of Bicycle AI named it as his clearest hindsight lesson.

"I would say fail fast on hiring and then when you have your core pillars... if you feel like someone's not really working out, or you're afraid of someone having carried the load so far but they're not sort of scaling beyond that level. Just make decisions quickly and don't sort of worry about what's going to happen to the institutional knowledge." — Bhaskar Sunkara, Bicycle AI

In AI startups especially, where a single engineer may own a critical model or system, the fear of losing their knowledge keeps founders frozen. Sunkara's advice is to decide quickly anyway. The corollary on the hiring side: the same speed and decisiveness that helps you exit a wrong hire should also make you aggressive in closing the right one. With expected value favoring big tech, the engineers worth hiring have options — so a slow, indecisive process loses them.

Sunkara frames the real lesson around your "core pillars" — the architectural and leadership roles you delegate to. Get those wrong, or let them linger when they've stopped scaling, and the cost compounds across everything built on top. For an AI startup, the highest-leverage version of "hiring AI engineers" isn't filling seats; it's identifying the two or three pillar roles that define your technical architecture and being relentless about getting the right person into each — paying up in equity, recruiting from the right pool, and replacing fast when the fit is wrong. The founders on the show who built strong engineering teams treated each early technical hire as a pillar decision, not a headcount decision.

Key stat: Founders describe early engineering hires as "core pillars" — architectural and leadership roles where a single wrong hire compounds across the entire system, making decisiveness the highest-leverage hiring skill.

Key stat: Bicycle AI's founder cites "fail fast on hiring" as his top lesson — making people decisions quickly even when an engineer holds critical institutional knowledge.

Key Takeaways: Hiring AI Engineers at a Startup

1. Don't compete on salary. Benchmark to your real cohort (Float's 80th–90th percentile of seed/Series A), not to OpenAI or Google.

2. Lead with equity. A meaningful ownership stake is the one thing a big lab can't match; founding engineers warrant more than the standard 1.5%–2%.

3. Solo founders should spend their equity surplus on engineers. Use that extra ~40% to recruit, per Carta.

4. Source from later-stage high-growth startups. Hedra hires engineers who saw green-field systems and high-growth pace at a prior Series A–C company.

5. Or recruit straight from college. Cluely's founder taps the unmatched density of brilliant, available talent in school.

6. Use in-person work as an edge. Merge's day-one in-person culture drove velocity and filtered for high-intensity engineers.

7. Move fast on wrong hires — and right ones. Bicycle AI's "fail fast" applies both ways; indecision loses engineers who have options.

8. Sell what big tech can't. Agency, ownership of hard problems, and mission — because the expected financial value favors Google.

FAQ: Common Questions About Hiring AI Engineers at a Startup

Q: How do you hire AI engineers when you can't compete with OpenAI on salary?

A: Stop competing on cash. Benchmark salaries to your real cohort (Float targets the 80th–90th percentile of seed/Series A peers), then win on equity, agency, and the chance to own hard problems. The expected financial value favors big tech, so your offer has to sell what a lab can't.

Q: How much equity should a founding AI engineer get at a startup?

A: The standard first-engineer grant is about 1.5%–2% per Carta data, but founders — especially solo founders with extra equity — are advised to grant founding engineers meaningfully more, since equity is the strongest lever against well-funded labs.

Q: Where do startups find great AI engineers?

A: Two proven sources from the PMF Show: experienced builders from later-stage, high-growth startups (Hedra's approach) who've built green-field systems, and brilliant new talent recruited straight out of college (Cluely's approach).

Q: Should AI startups require in-person work?

A: Many high-velocity founders do. Merge built in-person from day one because remote days were "significantly slower," using in-person intensity as both a productivity lever and a way to filter for the engineers they wanted.

Sources: Listen to the Full Founder Stories

• Robert, Float — Benchmarking engineering comp to your real cohort instead of big tech.
• Peter Walker, Carta (data episode) — Why founding engineers warrant more equity, and why expected value favors Google.
• Michael Lingelbach, Hedra — Hiring engineers from later-stage, high-growth startups.
• Roy Lee, Cluely — Recruiting brilliant talent straight out of college.
• Shensi Ding, Merge — Building an in-person engineering culture as a velocity and hiring edge.
• Bhaskar Sunkara, Bicycle AI — "Fail fast on hiring," even when an engineer holds critical knowledge.

Last updated: June 2026