The competition for Machine Learning Engineers in San Francisco is fierce. And if you’re a startup, you’re not just going up against other founders – you’re up against Google, Meta, OpenAI, and an endless stream of companies with billion-dollar war chests and dedicated in-house research teams.
But here’s the thing: startups still win.
Not all the time. But often enough, and with the kind of engineers who aren’t just looking for a comp package, but for something bigger. Something they can help build from scratch.
We’ve seen it happen again and again: early-stage AI companies pulling exceptional engineers out of Big Tech roles with fewer perks and more risk, but a much clearer sense of purpose. Here’s how they’re doing it.
It starts with the founder
If you want to attract top-tier talent, especially in machine learning, your founder story has to carry weight.
Not just a good idea. Not just the fact that you’ve raised money. But a clear sense that you’ve done something before, or are technically capable of doing something meaningful now.
Engineers want to work with people who’ve earned their respect. That might be a PhD in a relevant field. It might be a prior exit. It might be a public record of shipping great work, whether in open-source, academia, or scaled product. Ideally, it’s all three.
Founders who are deeply engaged in the problem space – and who communicate like builders, not just operators – tend to stand out. If you’re speaking at conferences, publishing research, or writing technical blog posts about what you’re learning, it sends a strong signal: this is a company led by someone worth working with.
And when engineers see that? They take the call.
Backing that tells a story
Plenty of startups raise money. But the type of funding matters.
If you’ve been backed by a top-tier VC – think Sequoia, a16z, Lux, or Amplify – engineers notice. Not because of the name alone, but because of what it implies: technical due diligence, strong conviction, and a bigger vision than “an AI wrapper for existing workflows.”
Great funding doesn’t just allow you to offer competitive salaries. It signals to candidates that they’ll be joining something durable – something that’s likely to survive its first 18 months and have a chance to scale.
It also helps with internal storytelling. When you’re able to say, “We’re building this with X, who also backed Y,” it creates a bridge to credibility that early-stage companies often struggle to establish on their own.
A team they’re proud to join
You can’t fake talent density. And the best engineers don’t want to join a team they’ll need to prop up.
One of the strongest signals to an ML Engineer is who else is already there. Who’s writing the core codebase? Who’s setting the research direction? What’s the level of experience across the team?
When engineers see a small, sharp team where everyone’s building it gives them confidence that joining won’t mean slowing down. It also gives them a reason to say yes even when the comp can’t match Big Tech.
If your team includes engineers who’ve worked on LLM fine-tuning at scale, or researchers who’ve published at ICML, you should be talking about that in every job conversation. These are not just technical signals, they’re proof points that your company is the right place to grow.
A hiring process that reflects your values
Interview processes don’t just filter for fit. They also signal what the company actually cares about.
Startups that attract exceptional talent tend to do a few things differently:
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They move fast because they respect engineers’ time.
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They offer clarity on timelines, expectations, and feedback because they know trust starts early.
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And they design interviews that showcase both the candidate’s skills and the team’s culture so the process becomes part of the pitch.
A two-week slog of interviews, take-homes, and vague technical assessments is a surefire way to lose the kind of candidates who have three other offers on the table.
The best interview processes feel more like a working session than an exam. They leave engineers saying, “That was hard – but I’d love to work with those people.”
Personal brand matters, especially the founder’s
Engineers do their research. They’ll look at your website, your product, your GitHub, and yes, your LinkedIn. If they can’t get a sense of what you’re building and why it matters, they’ll move on.
Founders who post about what they’re learning, share updates on their product journey, and engage with technical communities don’t just get more inbound interest, they build credibility without having to over-explain themselves.
We’ve worked with multiple founders who filled their early engineering pipelines purely through LinkedIn and Substack. No paid ads. No external recruiters. Just a clear story, told consistently, with technical depth and a clear sense of momentum.
This isn’t about being a “thought leader.” It’s about building in a way that invites smart people to care.
So what does this all mean?
If you’re a startup founder building in AI, your advantage over Big Tech isn’t cash. It’s clarity. It’s access. It’s the chance for someone to come in early and shape something that doesn’t exist yet.
You have the ability to design roles with real autonomy, to build a team that challenges each other, and to offer a level of trust that no legacy org can replicate.
Engineers aren’t just looking for jobs. They’re looking for missions that feel urgent, teams that feel tight, and leaders they can trust. If you can deliver on that, you won’t just compete with Big Tech. You’ll win.
Looking to hire Machine Learning Engineers?
Codexo helps ambitious AI startups in San Francisco connect with ML Engineers and Researchers who want more than just a big paycheck. We work with you to shape your hiring story, position your team, and close top-tier technical talent.