Should you apply if you don't meet all the requirements?
Usually yes. Most job descriptions are wish lists, not thresholds.
But "apply anyway" is unhelpful as blanket advice, because it treats every gap as equivalent and they are not. The useful question is not how many boxes you tick. It is which ones you miss.
Missing two nice-to-haves on a list of fifteen is noise. Missing the single capability the entire role is organised around is the difference between a stretch worth taking and an evening you will not get back. Both look like "does not meet all requirements" on the surface, and the surface is exactly where the usual advice stops.
The question was never how many requirements you miss. It is whether the ones you miss sit at the centre of the job or at its edges.
Fit separates those cases by weighting requirement coverage against the must-have spine of the role rather than counting matches. That is also why the score is shown with its factors attached: the number tells you where you landed, and the factors tell you whether you disagree.
What goes into the Fit score?
Four sub-scores, each on the same 1 to 5 scale so they are directly comparable, combined into a weighted average.
Skills
How much of the job's stated requirements your confirmed experience covers, weighted toward the must-haves rather than the full wish list.
Domain
Overlap between the industries and problem spaces you have actually worked in and the one this role sits in. Earned, not claimed.
Level
How the role's seniority compares to yours, blended with years of experience. Catches overshoot as well as undershoot.
Comp
Posted pay against your target, when both are known. A role you would have to take a cut for is a worse fit even when the work matches.
Alongside the number you get a recommendation in plain language: Strong Worth it Stretch Skip, along with the covered-versus-total counts for must-haves and nice-to-haves, so you can see the arithmetic rather than trusting it.
What if a job doesn't post a salary?
That sub-score is dropped, not guessed.
When pay is unknown on either side, comp is excluded from the average entirely. The same happens when a job description lists no domains, or when its requirements could not be parsed cleanly. The score is then built from whatever is genuinely known.
This sounds like a detail and is actually the whole design philosophy in miniature. A missing input reduces how much the score can tell you. It does not reduce the score. Filling the gap with an assumption would produce a more confident-looking number that means less, which is the same failure the Honesty Engine exists to prevent one layer up.
Is the score generated by AI?
No. Fit is computed, not generated, and that is deliberate.
It is ordinary deterministic code: requirement coverage, domain overlap, seniority and pay, combined by fixed weights. The same job against the same Vault always returns the same number, and every factor behind it is listed.
The reason matters. A decision aid you cannot audit is one you cannot sensibly disagree with, and disagreeing with this one is often correct. You know things the score does not: that you have wanted to work at this company for years, that the hiring manager is a former colleague, that the missing requirement is something you could learn in a fortnight. Fit is there to tell you what the evidence says, clearly enough that you can knowingly overrule it.
Does it change if I'm switching careers?
The framing changes. The number does not.
JobShifu tracks which of your target roles are pivots rather than continuations, and a pivot changes how the results are presented: gaps are described differently, and existing skills that carry across are surfaced as transferable rather than counted as misses.
What it will not do is inflate the score because the job matters to you. A pivot genuinely is harder, and a tool that hid that would be flattering you rather than helping. The honest version tells you the fit is a stretch and shows you which of your existing skills to lead with, which is the combination you can actually act on.
Fit and Readiness are different scores
They get confused constantly, so plainly:
| Fit | Readiness | |
|---|---|---|
| Question | Is this job worth applying to? | Does this resume answer this job? |
| Measures | The job, against you | A document, against the job |
| Scale | 1.0 to 5.0 | 0 to 100 |
| When | Before you do any work | While you tailor |
| Changes when | Your Vault changes | You accept or reject a change |
They move independently, and the interesting case is a modest Fit with a high Readiness: the application is as strong as it is going to get, and the remaining distance is real rather than a document problem. That is worth knowing before you write the cover letter, not after.
Frequently asked questions
Should you apply for a job if you don't meet all the requirements?
Usually yes, because most job descriptions are wish lists rather than thresholds. The useful question is not how many boxes you tick but which ones you miss.
Missing two nice-to-haves is noise. Missing the one capability the whole role is built around is the difference between a stretch and a waste of an evening. Fit separates those two cases by weighting requirement coverage against the must-have spine, so you can see whether the gap is at the centre of the job or at its edges.
What is the JobShifu Fit score?
Fit is a 1.0 to 5.0 rating of how well one job matches your confirmed experience, made of four sub-scores: skills, meaning how much of the job's requirements your experience covers; domain, meaning overlap between the industries and problem spaces you have actually worked in and the ones this role sits in; level, meaning seniority fit blended with years of experience; and comp, meaning posted pay against your target, when both are known.
Every score lists the factors that produced it.
Is the Fit score generated by AI?
No. Fit is computed, not generated. It is deterministic code combining requirement coverage, domain overlap, seniority and pay into a weighted average, so the same job and the same Vault always produce the same number and the reasoning is always inspectable.
This is a deliberate choice: a decision aid you cannot audit is one you cannot sensibly disagree with, and disagreeing with it is often the right move.
What if the job doesn't list a salary?
That sub-score is dropped rather than guessed. When pay is unknown on either side, comp is excluded from the average entirely instead of being filled with an assumption. The same applies to a job description that lists no domains or no parsed requirements.
A missing input lowers how much the score can tell you, which is honest, rather than lowering the score itself, which would be wrong.
Does the Fit score change if I'm switching careers?
The number does not change. The framing around it does. JobShifu knows which of your target roles are pivots rather than continuations, and a pivot changes how gaps are described and which of your existing skills are surfaced as transferable.
What it never does is quietly inflate the score because the job matters to you. A pivot is genuinely harder, and a tool that hid that from you would be flattering you rather than helping.
What is the difference between Fit and Readiness?
Fit is about the job and asks whether this is worth applying to, on a 1 to 5 scale, before you do any work. Readiness is about a document and asks whether this resume version answers this job well, on a 0 to 100 scale, while you tailor.
They move independently: a job can score a modest Fit and still produce a Ready resume, which tells you the application is as strong as it will get and the remaining gap is real.