Every application read
and scored
Progressive scoring.
Not one-shot filtering.
CV review, email Q&A, written tests, and AI video pre-screen — each layer lifts confidence. Every score is numbered against the Brief's rubric with reasoning.
Rubric scoring
Alex Chen · Sr. Backend Engineer
Distributed systems at scale, Kafka production experience
Clear async writing, structured responses in Q&A
Strong trade-off reasoning in systems design test
7yr backend, led migration, manages direct report
Excited about event-driven arch, startup-to-scale
Score updates as data arrives
Every candidate starts at baseline and progresses through CV review, Q&A, tests, and video. Confidence tracks with data volume — low, medium, or high — surfaced explicitly.
“Staff-level at FAANG — may expect higher scope than IC4 band”
“Expected $240k vs. budget ceiling $195k — 23% over”
“18-month gap (2021–2022), no explanation provided in CV”
“Current H-1B expires Aug 2026 — transfer required within 60 days”
Red flags with cited evidence
Over-qualification, anomalies, and near-misses surfaced with evidence. Nothing is silently auto-rejected — you decide on every edge case.
Three-tier confidence
Confidence reflects data volume: low, medium, or high. You always know how solid a score is based on what data is available.
Test integrity detection
Monitors screen-leave events, tab switching, webcam, copy-paste, automation, and time anomalies. AI cheating detection in video pre-screens.
Skills & scores visible
Optional blind screening
Hide name, photo, university, and location if you want it. Employer-configurable per role. Reveals only after the shortlist is approved.
Not keyword-matching.
Actually reading.
Resume, cover letter, links, previous work. Eligibility pre-screening before rubric scoring. Custom tests from plain-English descriptions.
Resume
Eligibility pre-screen
Links reviewed
Full application, fully read
Resume, cover letter, links, previous work — not keyword-matching, not just parsing. Eligibility pre-screening runs before rubric scoring so unqualified candidates don't waste pipeline time.
Test event pipeline handling with exactly-once delivery
Generated 5 scenarios · rubric attached
Custom test builder
Tell Rob what to test in plain English. He generates scenarios and rubrics. Built-in library covers English, negotiation, support, and technical roles.
+ Weight “migration experience” higher
+ Reduce penalty for non-traditional path
Override learning loop
Every override you make feeds back into the rubric. Rob's scoring adapts to your pattern over time — getting sharper with every decision.
Surfaces what matters.
You decide the rest.
Rob never auto-rejects. Edge cases are flagged for human judgement. Audit trail on every decision so you can defend the call.
200 applications
All read overnight
Sorted, reasoned pile
In your inbox by morning
You decide edge cases
Nothing silently binned
A sorted pile, not a screening marathon
200 applications read overnight. A reasoned, sorted pile in your inbox by morning. Edge cases flagged for your judgement — every decision has an audit trail.
AI video pre-screen
3–9 questions with per-response evaluation across six dimensions. Candidates can redo any response before submitting.
ADVANCED — Score 88, high confidence (by You)
REJECTED — Salary 2x budget, scope mismatch (by You)
FLAGGED — Near-miss, 1 must-have gap (by Rob)
ADVANCED — Score 91, override accepted (by You)
REJECTED — Bot detected, 0 integrity score (by Rob)
FLAGGED — Employment gap, no explanation (by Rob)
Audit trail on every decision
Every decision is logged with reasoning. “Why was this candidate rejected?” always has a rubric-cited answer you can defend.
What Screen
replaces
No more screening 200 resumes at 11pm, random rejections, or good candidates lost in the pile.
11:47 PM · 200 unread
“Rejected — doesn't have Kafka on resume”
“Good candidate lost in the pile”
Every candidate read, none silently binned
Audit trail with rubric-cited reasoning
Human judgement where it matters
AI cheating and tab-switching detected
From 11pm marathons to morning clarity
Late-night screening, keyword-filtering, and random rejections are replaced by a progressive scoring model that reads every application and gives you a defensible, sorted pile by morning.
No auto-reject
Every candidate gets a human decision
No demographics
Name, age, gender, ethnicity never scored
No hidden dims
All scoring criteria visible and auditable
Flags, not rejects
Edge cases flagged — never silently dropped
No auto-reject, no bias
No demographic factors. No hidden dimensions. No silent binning. The employer is always the safety net — you decide on every edge case.
Eligibility pre-screening
Right to Work, visa status, salary expectations, availability, and location preference — checked before rubric scoring runs so ineligible candidates don't waste pipeline time.
Under the hood
Everything Screen handles, from progressive scoring to test integrity.
Progressive AI scoring
CV → Q&A → tests → video, updating in real-time as data arrives.
Rubric-based scoring
Per-criterion reasoning tied to Brief must-haves and nice-to-haves.
Five scoring dimensions
Technical, communication, cognitive, experience, motivation & fit.
Three-tier confidence
Low, medium, high — surfaced in Rob's framing, not hidden.
Built-in test library
English, negotiation, customer support, and technical roles.
Custom test builder
Plain-English description → Rob generates scenarios and rubrics.
AI video pre-screen
3–9 questions with per-response evaluation across 6 dimensions.
Test integrity detection
Tab switch, webcam, copy-paste, automation, time anomalies.
AI cheating detection
Catches candidates generating answers live in video pre-screens.
Eligibility pre-screening
Right to Work, visa, salary, availability, location — before rubric.
Optional blind screening
Name, photo, university, location hidden. Configurable per role.
Anti-spam and bot detection
Filters inbound applications before they reach your queue.
Override learning loop
Rubrics adapt to your decisions. Rob gets sharper over time.
Red-flag surfacing
Cited evidence, nothing silently binned. You decide edge cases.
No auto-reject
No demographic factors, no hidden dimensions. Employer is the safety net.
Questions
and answers
Will candidates get auto-rejected?
No. Rob never auto-rejects. Edge cases, red flags, and over-qualification are surfaced with cited evidence — you decide on every one. The employer is the safety net.
What if the AI score is wrong?
Every score has reasoning attached. Confidence indicators tell you how solid the score is based on the data available. If you disagree, you override — Rob logs the direction and reason, and the rubric learns from your pattern over time.
Do you screen for things you shouldn’t?
No demographic factors. Ever. Screening logic is built to surface qualifications, not demographic proxies. Bias audits run against every rubric. Decisions are explainable — “why was this candidate rejected?” always has a rubric-cited answer.
Can candidates cheat the AI pre-screen?
AI usage detection catches candidates generating answers live. Test integrity detection monitors screen-leave events, tab switching, webcam, copy-paste, automation, and time anomalies. Candidates can redo any response before submitting, but Rob sees the redo pattern too.
What does blind screening hide?
Name, photo, university, and location — optional, per role. Employer-configurable. Reveals only after the shortlist is approved.
What about roles with specific hard requirements?
Eligibility pre-screening covers Right to Work, visa status, salary expectations, availability, and location preference before the rubric scoring runs. Candidates who can’t take the job don’t waste your pipeline time.
1.0 Brief
Turn a role into a hiring plan
2.0 Source
Find candidates who actually fit
4.0 Interview
Interviews that build on context
5.0 Reference
References that reveal the truth
6.0 Hire
Close the hire, not just the loop
“We stopped chasing candidates and started closing them. Rob handled the entire pipeline while we focused on building.”
Mira Johansson
VP Engineering, Vercel
“Six hires in two months with zero recruiter hours. That's not an optimization — it's a different model.”
Daniel Kraft
Head of Engineering, Ramp
Jobsly powers hiring for fast-growing teams. From early-stage startups to scaling enterprises.
Customer stories