AI candidate matching scores every applicant against the same written rubric, one criterion at a time, and combines those scores into a single match score. In HyreTech, each criterion gets a score from 1 to 100 with a written justification. The criteria carry weights that add up to 100, and the match score is their weighted average. If you approve the role's must-haves and deal-breakers, missing ones pull the score down. The number is useful for sorting. The per-criterion reasoning is what lets a recruiter answer the client's question: why these five?
"Match score" gets used loosely. In some tools it is a keyword overlap percentage, and in others a similarity number between two blocks of text. Neither can tell you why one person ranks above another, and that is the question a client asks.
Below is how HyreTech builds the number, what moves it, and how to use it when you submit a shortlist.
How The Match Score is Built
Every role has a rubric: a handful of criteria, a weight for each, and a minimum score. HyreTech drafts it from the job description as soon as the role is created. You edit it, and every applicant is scored against it.
Here is the arithmetic for one candidate on a senior backend role:
| Criterion | Weight | Score | Contribution |
|---|---|---|---|
| Ships end-to-end features | 30% | 80 | 24.0 |
| Depth in our stack | 25% | 100 | 25.0 |
| Works without a spec | 20% | 60 | 12.0 |
| Production ownership | 15% | 60 | 9.0 |
| Written communication | 10% | 60 | 6.0 |
| Match score | 76 |
Each of those five scores comes with a sentence or two explaining what in the resume earned it. How weights become a score covers the arithmetic in more detail.
A minimum score on a criterion flags the candidate as below your bar on that line. It is a marker for a person to look at, not a filter.
Must-Haves, Deal-Breakers and Preferred Skills
A weighted average has one weakness: a strong score on four criteria can hide a missing requirement on the fifth. So HyreTech lets you state the role's requirements explicitly.
When you regenerate a role's criteria, you review the requirements it extracted from the job description: required skills, preferred skills and deal-breakers. You approve them, which uses AI credits. After that, three adjustments apply on top of the weighted average:
| Signal | Effect on the score (defaults) |
|---|---|
| Each missing required skill | × 0.75, but several missing skills never push the multiplier below × 0.5 |
| Each failed deal-breaker (for example, location) | × 0.2, with no floor |
| Each matched preferred skill | +1%, capped at +5%, and only if no required skill is missing and no deal-breaker failed |
Applied to the 76 above:
- Missing one required skill (say, payments experience): 76 × 0.75 = 57.
- Missing three required skills: 0.75 × 0.75 × 0.75 is about 0.42, which is under the floor, so the multiplier stays at 0.5 and the score is 38.
- Failing a location deal-breaker: 76 × 0.2 = 15.
- Meeting every requirement plus three preferred skills: 76 × 1.03 = 78.
The multipliers are deliberate. A missing must-have costs a strong candidate more points than a weak one, so the ranking between them survives. The floor stops a job description that lists the same skill five ways from sinking a good applicant.
A deal-breaker has no floor because it is meant to be close to disqualifying. The preferred bonus is small and gated so it rewards exceeding the bar, not patching a hole in it. Each adjustment is listed next to the score, for example "missing required: payments", so you can see why the number moved.
Skip the approval step and none of this applies: the match score is the plain weighted average.
Each role also has two cut-offs, set when its criteria are generated, that sort candidates into Shortlist, Consider and Reject. These are labels you filter on. HyreTech has no auto-reject: advancing or rejecting a candidate is always something a person does.
Match Score vs Keyword Match vs Reverse Matching
| What it compares | What it can explain | |
|---|---|---|
| Keyword filter | Words in the resume vs words in the job post | Which terms appeared |
| Similarity search | The meaning of the resume vs the meaning of the job post | That two texts are alike, not why |
| Rubric match score | Evidence in the resume vs each criterion you wrote | A score and a reason per criterion |
| Reverse matching | Talent-pool sign-ups vs a new role | Who to look at first for the new role |
HyreTech uses the last two together. The rubric score ranks applicants to one role. Reverse role matching runs the other way and re-matches your talent pool when a new role opens. Past applicants are not re-matched automatically; you find them with search, as covered in candidate rediscovery.
Using The Score to Defend a Shortlist
For a recruiter, the shortlist is the product, and the client is judging the reasoning behind it. With 141 applicants on a brief, the five you submit should be chosen after comparing all 141, not after running out of time around number 60. Scoring against one rubric makes that possible, because the applicant who arrived on the last day gets read the same way as the first.
When the client asks why candidate two ranked above candidate three, the answer is already written, criterion by criterion:
- Candidate two: "Six years of Go, led the payments rewrite." Scored high on stack depth and on shipping.
- Candidate three: "Strong distributed systems, lighter on payments." Scored high on stack depth, lower on the payments-domain criterion the client weighted highest.
Three habits make this hold up:
- Agree the rubric with the client before applicants arrive. HyreTech drafts the rubric the moment you create the role and starts scoring as soon as applicants come in. It does not wait for approval. Edit the criteria and weights on the kickoff call, then open the role. First scorecards walks through the order.
- Keep one rubric per search. A rubric belongs to a role and is never shared with another, so editing one client's criteria never rescores another client's applicants.
- Copy the reasoning into your submission. There is no client portal, so the per-criterion lines go into your shortlist email or deck. If you edit the rubric and rescore the role, the scores and reasoning are replaced, so send the shortlist from the version you agreed with the client.
One workspace holds every client's searches, and search and talent-pool matching work across all of them. A strong applicant from one client's search turns up when you search for another client's role.
Roles you import from a CSV stay off the public HyreTech job board unless you list them. How this works for solo recruiters and for agencies is on HyreTech for freelance recruiters and HyreTech for recruiting firms.
What a Match Score Cannot Tell You
A match score measures the evidence in the resume against your criteria. If the evidence isn't written down, the score can't see it. A candidate who undersells a relevant project scores low on that criterion, and the reasoning will say the evidence is missing, which is your cue to ask.
For technical roles, a linked GitHub profile adds evidence. HyreTech reads the public repositories and activity and weighs them as supporting evidence for the resume's claims. Scoring is set up not to count a missing profile against anyone.
A match score also doesn't check whether the resume is internally consistent. On Growth and above, a verification trust score flags overlapping dates and timelines that don't add up. It is a prompt to check before a candidate goes to a client, not proof of anything.
Try it on a Live Search
One credit analyses one applicant. The Free plan includes 20 credits, and credits top up on any plan. The Starter plan covers three active roles with 300 credits a month. Current rates are on the pricing page.
Start free and edit the drafted rubric before your first applicant arrives, or book a demo and we will run it on a brief you already have open.
FAQ
How does AI candidate matching work?
HyreTech scores each applicant from 1 to 100 on every criterion in the role's rubric, with a written justification, then combines those scores into one match score using weights that add up to 100. If you approve the role's required skills and deal-breakers, missing ones lower the score by a fixed multiplier.
Is a match score the same as a keyword match?
No. A keyword match counts shared words between the resume and the job post. A rubric match score rates the evidence in the resume against each criterion you wrote, and explains each rating, so experience described in different words still counts.
Which platforms support exporting shortlists and writing decisions back to our ATS?
HyreTech does neither: there is no shortlist or candidate export, and nothing is written back to your ATS. Imports are one-way, by CSV. Every score carries per-criterion reasoning that you copy into the shortlist you send a client, and if your ATS stays the system of record, you enter the decision there yourself.
Does HyreTech reject candidates automatically?
No. Candidates are labelled Shortlist, Consider or Reject against the role's cut-offs, but nothing is rejected because of a score. A person always takes that action.
Does a missing required skill knock a candidate out?
Not on its own. Each missing required skill multiplies the score by 0.75 by default, and several missing skills never cut it by more than half. Only a failed deal-breaker, at 0.2, is close to disqualifying.