By industry · Tech hiring
Tech recruiting software for engineering candidate screening
Tech recruiting software has to solve a problem that is specific to engineering hiring: the applicant pile is enormous and the signal is buried. A single mid-level backend role at a company nobody has heard of can pull four hundred applications in a week, and the resumes are genuinely hard to read. Keyword filters make it worse, not better, because they reward whoever pasted the most frameworks into a skills section while a strong engineer with a four-line resume and three years of relevant production work gets filtered out on a missing string.
Parse · score · rank · recruiter reviews
Held for recruiter review · never auto-rejected
Screen the pile to watch Prescreener parse every resume, apply your criteria and knockouts, and rank the inbound applicants with a transparent reason on each.
Live, interactive · AI assists, you decide · no signup needed
AI assists · you decide · bias-audited (EEOC / NYC Local Law 144)
Human-in-the-loop you decide
EEOC and LL144 bias-audited
Prescreener is the engineering candidate screening layer. It reads every developer resume for what it actually shows, applies the knockout and eligibility criteria you set (required stack, years at the seniority you are hiring, work authorization, location or time zone, on-call expectations), asks the short screening questions you write, scores role-fit against your engineering criteria, and returns the pile ranked with the specific reasons behind each placement visible to the recruiter and the hiring manager.
It is screening-first, not an applicant tracking system, so it runs alongside Greenhouse, Lever, Ashby, Workable or whatever your team already uses, and nothing migrates. It also does not decide. Prescreener ranks and flags, never auto-rejects, discloses AI use to candidates, and is bias-audited to support EEOC standards and NYC Local Law 144. A recruiter or an engineering manager makes every call about who gets a call back.
Why it works
What your team gets with tech hiring
Beyond keyword stuffing
Screening reads real experience and role-fit instead of rewarding whoever listed the most frameworks, so concise strong engineers are not knocked out on a missing string.
Stack and seniority criteria
Knockout rules for required stack, seniority, time zone and work authorization are applied evenly to every applicant, so mismatched candidates do not eat engineering-manager time.
Managers review the top
A ranked shortlist with reasons sends engineering managers straight to the strongest fits instead of a noisy, undifferentiated pile they will not get to until Thursday.
Adjacent stacks are not knockouts
A senior Rails engineer applying to a Django role is usually a real candidate. Related experience is scored on transferable depth rather than binned for the wrong framework name.
Runs next to your ATS
Prescreener is a screening layer, not a system of record. Keep Greenhouse, Lever, Ashby or Workable and add the ranking step in front of the pipeline you already run.
What it handles
Parsed, scored and ranked on autopilot
Prescreener reads every inbound resume, applies your knockout and eligibility criteria, asks the screening questions you set, scores role-fit against consistent criteria, and ranks the applicant pile so your recruiters review the top matches first.
- Reads every engineering resume in the pile, not a sample
- Applies stack, seniority, time zone and eligibility knockouts evenly
- Scores role-fit and ranks with transparent reasons on each candidate
- Scores adjacent-stack experience instead of filtering it out
- Sends hiring managers the strongest matches first
- Bias-audited to support EEOC standards and NYC Local Law 144, with candidate consent and AI disclosure
Why Prescreener
One step that screens the whole inbound pile
Not a full ATS, not a six-figure assessment suite, and not a staffing agency. Parse, score, rank and hand off in one place, shaped to the criteria you already hire on.
Reads every resume
Prescreener parses every inbound application, applies your knockout and eligibility criteria, and asks the screening questions you set. Applicants consent and are told AI is screening their application, so no qualified person sits in a backlog for days.
Scores role-fit consistently
Every applicant is scored against the same criteria, with a transparent reason on each candidate, so the pile is compared consistently and the scoring stays bias-audited to support EEOC standards and NYC Local Law 144.
Ranks the pile
The strongest matches rise to the top of a ranked pile your recruiters review first. Prescreener never auto-rejects, it ranks and flags candidates for review, and your team makes every decision.
In depth
How engineering candidate screening actually works
Which AI screening tool an engineering recruiting team actually needs
The phrase "AI screening tools for engineering recruitment" covers at least four different products, and teams routinely buy the wrong one. A coding assessment platform evaluates candidates who agree to sit a test. A technical interview platform evaluates candidates you already scheduled. A sourcing tool finds people who never applied. A screening layer reads the applications that already arrived and puts them in order. All four get described as screening in marketing copy, and only one of them touches an unread inbox.
The way to tell which you need is to look at where the week actually goes. If engineering managers are burning hours on interviews with people who were never close, the interview stage is not your problem, the filter in front of it is. If a role has four hundred applicants and a recruiter has opened sixty of them, no assessment tool will help, because the other three hundred and forty never reached a stage where an assessment exists.
The distinction matters commercially too. Assessment platforms are usually priced per candidate who completes something, which is fine when the funnel is already narrow and punitive when it is not. A screening layer is priced on the volume arriving, which is the number you actually have a problem with.
| Tool type | What it evaluates | Helps with an unread pile? | Typical trigger |
|---|---|---|---|
| Screening layer | Every inbound resume, against your stack and seniority criteria | Yes. This is the job | 400 applicants, 60 opened |
| Coding assessment | A test the candidate chooses to complete | No. Only candidates who opt in | Weak signal at the technical stage |
| Technical interview platform | A live or recorded interview you scheduled | No. Runs after screening | Inconsistent interviewer scoring |
| Sourcing tool | Profiles of people who never applied | No. Adds volume rather than ordering it | Not enough qualified applicants |
| ATS | Nothing. It stores and moves applications | No. Storage is not triage | No system of record |
Swipe to see the full comparison →
Why keyword filters fail hardest on engineering resumes
Engineering resumes break keyword matching more thoroughly than resumes in any other function, for a reason that is structural rather than accidental. The vocabulary is enormous, it changes every couple of years, and the same capability has several legitimate names. A candidate who writes "Postgres" does not match a filter for "PostgreSQL". Someone who spent four years on ECS and moved to Kubernetes last year may list only the current one. And the strongest senior engineers often write the shortest resumes, because they have stopped trying to prove breadth.
The failure runs the other way too. Keyword density rewards the applicant who pasted thirty framework names into a skills block, which is exactly the resume a careful engineering manager would discount on sight. A filter tuned to catch every relevant candidate lets almost everyone through, and one tuned to be selective removes strong people silently. There is no threshold that fixes this, which is why teams keep re-tuning filters and keep getting the same complaints.
Reading for evidence instead of strings changes the shape of the problem. What matters is whether someone has actually shipped and maintained the kind of system you are hiring for, at the scale and seniority you need, and adjacent stack experience should be scored on transferable depth rather than binned on a framework name. A senior Rails engineer applying for a Django role is usually a genuine candidate. Whatever tool you choose, ask it to show you which criteria drove a specific ranking, in plain language, for one named applicant.
Good questions
Questions about tech hiring
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Learn moreStop reading the inbound pile by hand. Put screening on autopilot.
Set your role and criteria and Prescreener parses every inbound resume, scores role-fit, and ranks the pile for your team. Prescreener ranks and flags candidates, your recruiters make every hiring decision.
Consistent criteria · bias-audited to EEOC and LL144 · human-in-the-loop