Most recruiting teams now run applications through some kind of automated screen before a person reads them. It might be a keyword filter in an applicant tracking system, a resume parser that ranks candidates, or an AI model that compares each resume with the role. Yet the job description, the document all of these tools depend on, is usually written the same way it was ten years ago: a list of adjectives, a long wish list, and a paragraph about company culture.
That mismatch causes real problems. When the job description is vague, the screen has nothing precise to compare against, so it falls back on surface signals like job titles and buzzwords. Good candidates who describe their experience differently get ranked low, and keyword-stuffed resumes rise to the top. The fix isn't a better tool. It's a better job description.
Here is a practical way to write one that a hiring manager, a candidate and an AI screener can all read the same way.
Start with outcomes, not adjectives
"Dynamic, self-motivated team player" tells nobody anything. A screener can't check it and a candidate can't prove it. Replace adjectives with the outcomes the person will own in their first six to twelve months.
For a customer support lead, that could be: "Reduce first-response time on the support queue and build a weekly reporting routine for the team." For a backend engineer: "Own the billing service, including on-call, and ship the migration from our old payment provider."
Outcomes give the screen something concrete to match. A resume that says "led migration of payments platform" is clearly relevant, even if it never uses the phrase "dynamic team player".
Separate must-haves from nice-to-haves, and keep the first list short
Most job descriptions list twelve requirements and treat them all as equal. In practice, a hiring manager would happily hire someone who meets five of them well. When everything is "required", an automated screen either rejects nearly everyone or has to guess which items really matter.
Write two lists:
- Must-have (3 to 5 items): things a person genuinely can't do the job without, such as a licence, a language, a specific depth of experience, or the ability to work certain hours.
- Nice-to-have: everything else.
Then make sure the must-haves are checkable. "Strong communication skills" is not checkable from a resume. "Has written customer-facing documentation or run training sessions" is.
Describe skills the way candidates describe them
People rarely use the exact words in your job description. One candidate writes "Postgres", another "PostgreSQL", another "relational databases". A sales candidate says "closed deals" while your posting says "drove revenue".
Modern AI screeners are better at recognising synonyms than old keyword filters, but they still work best when the job description includes the common variants. Read ten resumes of people who are already good at the role and note how they describe the work. Use some of their language in the posting. It helps candidates recognise themselves in the role, and it helps any automated matching.
Be explicit about what doesn't matter
Screens often lean on proxies such as a particular university, years at big-brand companies, or an unbroken employment history. If those things don't matter for the role, say so in the posting and in the screening criteria: "We don't require a degree" or "Career breaks are fine; tell us what you did during them."
This matters for fairness, and it also matters for accuracy. An AI model that is told what to ignore is less likely to pick up irrelevant patterns from past hiring data.
Add the practical facts candidates actually filter on
Candidates screen employers too. The questions they ask first are nearly always the same: salary range, location or remote policy, working hours, notice period expectations, and visa or relocation support. Putting these in the description saves everyone a phone call, and it lets an automated first-round screen confirm the basics quickly instead of discovering a mismatch in the third interview.
If you run a screening call, whether a person or an AI makes it, use the same facts as the job description. A candidate shouldn't hear one salary range in the posting and another on the phone.
Turn the description into a scorecard
The last step is the one most teams skip. Once the job description is written, turn the must-haves and outcomes into a simple scorecard, with three to six criteria, each with a short description of what "strong", "acceptable" and "not yet" look like.
This scorecard becomes the shared reference for everyone:
- The resume screen ranks candidates against these criteria rather than against keywords.
- The screening call asks questions that map to them.
- Interviewers score the same criteria, so their feedback can be compared.
At Talynce, the job description and its criteria are the starting point for everything the AI does: resume ranking, first-round calls and interview scoring all trace back to them. That's why we push customers to spend an extra twenty minutes on the description before switching on AI candidate screening. Those twenty minutes do more for screening quality than any model setting.
A quick checklist before you publish
Before a job description goes live, read it once as each of its three audiences.
As a candidate: Can I tell in thirty seconds what I'd be doing, what it pays, and whether I'm eligible?
As the hiring manager: If someone met every must-have, would I genuinely want to interview them? If not, the list is wrong.
As a screener: Could I score a resume against each must-have using only what is written on the page? If an item needs a conversation to judge, move it to the interview stage.
Why this matters more as AI does more
As more first-round screening becomes automated, the job description is no longer just an advert. It's effectively the instruction you give the software. A vague brief to a person gets clarified over coffee, while a vague brief to an automated screen just produces poor rankings at scale.
The good news is that a job description written for AI turns out to be a better one for people too: specific, honest about what matters, and clear about the practical details. Candidates apply with better information, hiring managers see stronger shortlists, and screening tools have something real to work with.