Hiring Machine
Turns an open role into a vetted Philippine shortlist — sourced, work-sampled, and scored on one published rubric.
The problem
US founders know Philippine talent is the best value on the market and still hire badly, because screening a hundred applicants by hand is nobody's full-time job. So the role stays open for a quarter, or the wrong person gets it.
The solution
Hiring Machine drafts the role, sources candidates, runs a structured work sample, and scores everyone against one published rubric. It never rejects anyone on its own. What it hands you is a defensible shortlist and the evidence behind every rank.
What's inside
Features
Structured, not vibes
Every candidate answers the same work sample and is scored on the same rubric, so a shortlist is comparable instead of a memory of who interviewed well.
Evidence over résumé
Scores come from what a candidate actually produced under the work sample, not from where they went to school or how the CV was formatted.
A human makes every reject
The machine ranks and explains its reasoning. It never sends a rejection or filters a person out by itself — that decision carries legal and human weight.
Built on the SF–Makati seam
Designed around how US startups actually hire Philippine talent, because it is the way New Machine itself is built.
Anything can generate. A machine earns the name when something can automatically reject its work and make it try again.
- Goal
- A ranked, evidence-backed shortlist for one open role.
- Verify
- Every candidate scored against the same published rubric, with a second reviewer agent re-scoring independently; any unscored candidate blocks the shortlist.
- Stop when
- The shortlist clears the bar, or the pool runs dry — then it reports what the market actually has rather than padding the list.
In Development · no client results published yet
Want Hiring Machine for your business?
We're building it in the open — tell us what you need and we'll factor it into the roadmap.