Looking for a U.S.-based technical cofounder!

Frame the core claim with selection-science evidence
Use: Schmidt & Hunter (1998), Psychological Bulletin.
Key point: general mental ability + work-sample style methods predict job performance better than unstructured screening.
How to help Ariel: position StepStone as “work-sample/signal-rich assessment” vs resume keyword matching.
Recommend structured, standardized evaluation pipelines
Use: Campion, Palmer, & Campion (1997), Personnel Psychology (structured interview design).
Key point: standardization increases reliability/fairness and reduces noisy decisions.
How to help Ariel: define fixed scoring rubrics, anchored rating scales, and interviewer prompts tied to job competencies.
Add fairness and governance as a product differentiator
Use: Raghavan et al. (2020), FAT* (“Mitigating bias in algorithmic hiring”).
Key point: hiring models can encode bias unless fairness is measured and governed continuously.
How to help Ariel: propose fairness dashboards (selection-rate parity, false-positive/negative gaps), model cards, and audit logs per release.
Push explainability for employer trust/adoption
Use: Doshi-Velez & Kim (2017), arXiv (interpretable ML evaluation framework).
Key point: users trust systems more when explanations are testable and decision-relevant.
How to help Ariel: each candidate score should include human-readable evidence (“signal drivers”) and confidence bands.
Strengthen pilot design so outcomes are publishable
Use: Kohavi et al. (2009), Data Mining and Knowledge Discovery (online controlled experiments).
Key point: A/B and quasi-experimental design is the cleanest way to prove impact.
How to help Ariel: run employer-side experiments comparing StepStone shortlist vs baseline process on interview-to-offer rate, time-to-fill, and 90-day retention proxies.

Hi Ariel,
Your project at StepStone sounds intriguing – a hiring platform tackling talent sourcing with AI-driven scoring and insights is right up the alley of what's needed in this space. As someone building an AI platform myself (focused on letting creators "build by talking" to assemble digital businesses with AI Agents handling the heavy lifting), I've seen how integrating smart agents can streamline things like candidate matching or workflow automation without needing deep coding.
If you're open, I could share some insights on bootstrapping tech partnerships or even explore if our AI tools could prototype something for your hiring flows. Based in [your location, e.g., non-US but remote-friendly]? Feel free to DM if you'd like to chat more – happy to connect and brainstorm.
Best,
Sinkol

Ariel, this comes across as one of the stronger cofounder posts here. You sound like a real founder already moving, not just brainstorming.
The one place I’d sharpen: right now there’s a gap between users and customers. You frame employers as the buyer, but highlight students as early users. That makes it unclear where the real demand signal is coming from. For your own clarity (and for future partners), focus the traction story on the paying side. What employers are committing to, not just what students are testing.

That adjustment would make your pitch even stronger and give you clearer direction on how to grow the product.

Hi Ariel, finding the right cofounder can take months and delay your vision, while outsourcing lets you start building immediately with a skilled team. A cofounder also means giving up equity, whereas we help you scale fast without dilution. Share your requirements with me, and we’ll turn your prototype into a market-ready product.
https://www.softwebsolutions.com/contactus.html