Superposition for AI Startups
Hire builders who ship AI products, not resume keyword-stuffers.
Hiring for an AI startup in 2026 is uniquely hard
- Retained recruiters.
- LinkedIn Recruiter seats.
- Keyword-matched resumes.
- Templated InMails.
Every engineer's LinkedIn now says 'AI.' Every recruiter pitches 'AI-ready candidates.' The signal-to-noise ratio in AI hiring has collapsed.
Meanwhile, the actual people you need (engineers who've shipped evaluated, production-grade AI systems) are rare and in demand. They're not job-hunting. They're building.
You need sourcing that speaks the language of frontier models and can actually evaluate who's shipped what.
How Superposition finds AI talent
Frontier-fluent agent
Our agent understands MCP, tool use, evals, retrieval, and caching, and evaluates candidates on real shipping signal in those areas.
Intake that speaks the stack
Voice intake captures whether you need a researcher, an applied ML engineer, or an agent builder. Different pools, different sourcing.
Cuts through resume noise
We filter out candidates whose 'AI experience' is a single tutorial. You see people with demonstrable shipped work.
Speed in a hot market
Curated shortlists in weeks, not months. Because in AI, the best candidates don't stay available long.
Questions.
Answered.
By reading the work: repositories with evaluation code, write-ups about a model that regressed, papers with reproducible results. Claims on a profile are not evidence.
If the hard part is product, retrieval and evaluation, you want an AI engineer. If it is models, data and training, you want an ML engineer. Describe month one and we will tell you which.
On the problem and on ownership. The outreach leads with what they would work on and decide, because cash is a fight you will usually lose.
Yes, and the second author who did the engineering is often the better hire and far less contacted than the first.
Hire for your AI startup.
Get engineers who've actually shipped production AI, not resume-updaters.