💼AI Job Description Writer
Writing a job description that actually attracts the right candidates is harder than it looks. Most hiring managers spend an hour on the role brief, then five minutes copying last year's template, replacing a few bullets, and posting it to LinkedIn and Indeed without a second pass. The result is a generic listing stuffed with cliches like "rockstar", "results-driven", and "passionate self-starter", which research from LinkedIn, SHRM, and the Harvard Business Review consistently shows reduces qualified applications, especially from women, candidates over 40, and people from underrepresented backgrounds. The same studies also show that descriptions stuffed with hard years-of-experience minimums and long lists of required skills suppress applications from candidates who would have grown into the role and stayed three to five years. The FixTools AI Job Description Writer takes a short brief about your role, the requirements, and the company context, and produces a complete, structured, inclusive job description ready to post. The output includes a job title, a one-paragraph About the Role summary, six to eight specific responsibilities, four to six required qualifications framed as skills rather than gatekeeping years, three to four nice-to-haves to encourage borderline candidates to apply, a four-bullet benefits framing focused on what the candidate gains, and a short equal-opportunity statement that meets EEOC guidance. The tool runs an inclusive-language pass over every section, scrubbing gendered words, age-coded phrases, and jargon-heavy filler. Hiring managers, recruiters, founders, HR generalists, and external talent agencies can all use it to produce a strong first draft in seconds rather than the typical thirty to sixty minutes of template editing. The free tier accepts a brief up to 600 characters, which is enough for most individual contributor roles. The paid tier extends to 5,000 characters for senior leadership roles, technical specialist roles, and executive searches with detailed context. Every output is generated in under five seconds.
Why most job descriptions repel the candidates you actually want
The Harvard Business Review published a widely-cited finding that women apply to jobs only when they meet 100 percent of the listed qualifications, while men apply when they meet about 60 percent. Subsequent research from LinkedIn Talent Solutions has refined this picture: the gap is less about confidence and more about how candidates read requirements language. When a job description says "must have 7+ years of experience", a candidate with 5 years and demonstrably strong work assumes they will be auto-rejected by an applicant tracking system before a human ever reviews their application. When the same description says "we are looking for someone with several years of relevant experience, ideally in a senior individual contributor role", the same candidate considers themselves a fit and submits. The generator defaults to skill-based framing, ranges instead of hard minimums, and a Nice-to-Haves section that explicitly tells candidates which items are bonuses rather than blockers. This single structural choice has been shown by Textio, Gild, and Applied to widen the applicant pool by 20 to 40 percent for the same role, without changing the hiring bar or compromising on quality. The hiring manager still evaluates the same candidates against the same internal scorecard. The change is simply in who self-selects into the funnel, and that change reliably surfaces strong candidates who would have otherwise filtered themselves out before ever clicking apply.
Gendered language in job descriptions is well-documented as a suppressor of female applications. Words like "aggressive", "dominant", "competitive", "fearless", and "driven" are statistically associated with male job-seeker preference, while words like "supportive", "nurturing", and "collaborative" skew the opposite direction. A balanced job description uses neither category unless the role genuinely requires it, and instead names the specific behavior the company wants. Instead of "we need an aggressive sales hunter", the generator produces "we are looking for someone who can identify and close net-new accounts in a long sales cycle". The behavior is named, the value is preserved, and the gendered code is removed. The same pass scrubs age-coded phrases like "young dynamic team", "fresh graduate energy", and "digital native", which trigger EEOC age-discrimination concerns and reduce applications from candidates over 40, who are often the strongest applicants for senior roles. EEOC enforcement actions in recent years have increasingly targeted job postings as the first evidence of pattern-and-practice discrimination, so the language in your description is not just a recruiting question, it is a legal exposure question for the company posting it.
The jargon problem is just as costly. Words like "rockstar", "ninja", "guru", "wizard", and "hero" started as Silicon Valley shorthand for "we are a fun startup" and have become so overused that they signal the opposite to experienced candidates. SHRM reports that job postings using these terms see lower application rates from candidates with more than ten years of experience, who read the jargon as a flag for immature management culture or low compensation. The generator removes all such terms and replaces them with concrete role descriptions. "We need a backend rockstar" becomes "we are hiring a senior backend engineer who can own service-level reliability for our payments pipeline". The specificity also helps the role rank in candidate search, because real candidates search for "senior backend engineer", not "backend rockstar". Job board ranking algorithms on Indeed and LinkedIn weight title clarity heavily, so a well-named role surfaces in more relevant candidate searches and earns more impressions per dollar of paid promotion. The same principle applies to the body of the description: specific responsibilities rank better than vague filler.
The Equal Opportunity Statement is not boilerplate. The EEOC publishes specific guidance on what an inclusive statement should include, and Indeed Hiring Lab data shows that job postings with a substantive equal-opportunity statement see 12 to 18 percent more applications from candidates who self-identify as underrepresented in the role. The generator produces a short, two-to-three sentence statement that names the company as an equal opportunity employer, welcomes applicants regardless of background, and explicitly invites candidates who do not meet every listed qualification to still apply. This last sentence is the highest-leverage one, because it directly addresses the qualifications-gap research and tells borderline candidates that the company has read the studies and behaves accordingly. The result is a posting that signals competence to candidates and reduces legal exposure for the company in jurisdictions with active hiring-discrimination enforcement. Companies operating in California, New York, Washington, Colorado, and Illinois face particularly aggressive enforcement on hiring transparency, including pay-range disclosure rules that the generator can incorporate when the brief includes compensation ranges, so the same posting can serve compliance and recruiting goals at once.
How to use AI Job Description Writer
- 1
Write a short brief of the role
Paste a brief that includes the role title, the team or company context, the core responsibilities, the required skills, and any benefits you want highlighted. A brief like "Senior frontend engineer for a remote-first fintech, React and TypeScript, accessibility focus, 5+ years experience, equity and full benefits, flexible hours" gives the generator enough to produce a strong description. Vague briefs like "we need a developer" will trigger a clarifying question instead of a guess.
- 2
Optionally specify constraints
If you want a specific tone, mention it in the brief: "warm and conversational" or "formal and corporate". If you want a remote, hybrid, or on-site model, include it. If compensation or equity is part of the offer, include real numbers or ranges and the generator will incorporate them faithfully into the What You Will Get section. The generator will never invent salary figures you did not provide.
- 3
Run the generator
Click Generate and the model produces a complete, structured job description in five to eight seconds. Output includes the job title, an About the Role paragraph, six to eight responsibilities, four to six required qualifications, three to four nice-to-haves, four benefits, and a short equal-opportunity statement. Every section is formatted in clean markdown so you can paste it directly into LinkedIn, Indeed, Greenhouse, Lever, Ashby, or your careers page CMS without reformatting.
- 4
Review the inclusive-language pass
The generator automatically removes gendered terms, age-coded phrases, and overused jargon like "rockstar" and "ninja". Skim the output to confirm every responsibility and qualification still accurately reflects what the role requires. If you spot a phrase that was softened too far, swap it back to the specific language your team uses internally. The inclusive pass is a default, not a constraint, so you have full editorial control.
- 5
Post and track applications
Paste the final description into your applicant tracking system or job board. Track applications-per-view and the demographic spread of applicants over the first two weeks. If the volume is lower than expected, regenerate with a stronger benefits section. If the demographic spread is narrower than expected, regenerate with extra emphasis on the equal-opportunity statement and the nice-to-haves framing.
Real-world use cases
Startup founder hiring their first product designer
A two-person startup founder needs to hire their first product designer and has never written a real job description before. Their first draft includes phrases like "we need a design rockstar who can wear many hats" and "must have 7+ years of experience in a fast-paced startup environment". They paste the role brief into the generator and receive a clean description: a senior product designer responsible for end-to-end product design across a small team, with skills in interaction design, prototyping in Figma, and a portfolio that demonstrates shipped consumer work. The years-of-experience requirement is reframed as "several years of relevant product design experience, ideally including time at an early-stage company". The posting goes live on three job boards and produces 40 qualified applicants in the first week, double what a previous template-based posting produced for the same role. The founder reports spending fifteen minutes total on the description, down from the four hours they had budgeted for the task.
HR manager at a mid-sized company refreshing 30 stale descriptions
An HR manager at a 400-person company inherits a careers page where every job description was last refreshed in 2019. Half of them include gendered language flagged by an internal DEI audit, and the company has been told by their employment lawyer to update everything before the next compliance review. The manager runs each existing description through the generator as a brief, getting a refreshed version that scrubs gendered words, modernizes the qualifications framing, and adds a substantive equal-opportunity statement. The 30-description batch takes one afternoon instead of the two weeks the HR business partner had originally scoped. Internal legal review approves all 30 on the first pass with no rework, and the careers-page bounce rate drops by 22 percent in the following quarter as candidates land on descriptions that read as current and intentional rather than legacy templates from a different company era.
Recruiter posting to multiple job boards at once
An external recruiter is filling a senior backend engineer role for a fintech client and needs the same role described slightly differently for LinkedIn, Indeed, AngelList, and the client careers page. The recruiter runs the role brief through the generator once, then regenerates with small tone tweaks: more conversational for AngelList, more corporate for the client careers page, and slightly punchier for LinkedIn where the algorithm rewards engagement language. The four resulting descriptions all share the same core responsibilities and qualifications, but read appropriately for each platform's audience and search ranking signals. Application volume increases by 30 percent compared to the recruiter's previous practice of copy-pasting one description across all four boards, and the client signs off on all four within a day because the core role framing stays identical across platforms. The recruiter now treats every search as a four-version generation and includes the variant set in their standard client deliverable.
Engineering manager opening a backfill role
An engineering manager at a 50-person SaaS company needs to backfill a senior engineer who just gave notice. They have two weeks to post the role before the departure becomes public, and they want a description that reflects the actual day-to-day of the role rather than the generic template HR maintains. They write a 400-character brief covering the team context, the specific systems the new hire will own, the on-call expectations, and the seniority level. The generator produces a description that names the actual services, frames the on-call expectation honestly so candidates self-select on willingness, and lists the benefits the company already offers. The manager makes minor edits and posts the role the same afternoon, two weeks ahead of schedule. The first qualified applicant arrives in 36 hours and the role closes in three weeks, which is faster than the company average for backend hires by roughly a month.
Pro tips
💡 Include real benefits, never invent them
The generator only includes compensation and benefits you explicitly mention in your brief. If you say "we offer health insurance, 401k matching, and unlimited PTO", those will appear faithfully. If you do not mention compensation, the What You Will Get section is framed around qualitative benefits like growth, flexibility, and mission rather than fabricated dollar figures. This protects you from accidentally publishing a description that overstates the offer.
💡 Frame years of experience as ranges, not minimums
Instead of writing "5+ years of experience" in your brief, write "ideally 4 to 7 years of experience". The generator preserves your framing, and the range signals to candidates that the company evaluates fit holistically rather than gatekeeping at a hard cutoff. Research from Textio shows that ranges produce 22 to 35 percent more applications from candidates near the lower bound, who are often the strongest hires because they grow into the role rather than coasting.
💡 Mention the team size and reporting structure
Candidates evaluating senior roles want to know who they will report to and how many people they will work with. Include "reports to the head of engineering, joins a team of six" in your brief and the generator will weave it into the About the Role paragraph. This single detail measurably improves application quality at the senior level, because candidates self-select based on whether the structure fits their preferred working style.
💡 Run the same brief twice with different tones
Generate one version with a formal corporate tone for your careers page and a separate version with a warmer conversational tone for LinkedIn and AngelList. The core content stays consistent, but the voice differs for each audience. This is what good recruiting teams do manually for senior roles, and the generator makes it a five-minute exercise instead of a two-hour rewrite.
Frequently asked questions
Will the generator invent benefits or salary I did not mention?
No. The generator only includes compensation and benefits you explicitly provide in your brief. If you mention "health insurance, 401k match, four weeks PTO", those exact items appear in the output. If you do not mention compensation, the What You Will Get section is framed around qualitative benefits like growth opportunities, ownership, flexibility, and mission alignment. The model is instructed never to fabricate dollar figures, equity percentages, or benefits that were not in the input.
How strict is the inclusive-language pass?
Strict by default. The generator scrubs gendered terms like "aggressive" and "nurturing", age-coded phrases like "young energetic team" and "digital native", and overused jargon like "rockstar", "ninja", "guru", "wizard", and "hero". It also avoids cliches like "results-driven", "passionate", "self-starter", and "team player". If your industry genuinely requires one of these terms (for example "competitive" for a sports retail role), you can include it in the brief and the generator will preserve it where it fits the role accurately.
Can I generate descriptions for non-English roles?
Yes. Write your brief in English but specify the target language, for example "this role is for a Spanish-speaking team in Madrid". The generator produces the description in the target language while applying the same structure, inclusive-language pass, and equal-opportunity statement. Local employment law differs by jurisdiction, so have a local employment lawyer review the equal-opportunity language before publishing in countries with specific statutory requirements.
Does the output meet EEOC and SHRM guidance?
The structure is built on published EEOC and SHRM recommendations for inclusive job descriptions, including a skills-first qualifications section, a Nice-to-Haves section to encourage borderline candidates, and a substantive equal-opportunity statement. The tool does not constitute legal advice, and you should have your employment lawyer review descriptions for high-stakes roles or jurisdictions with active hiring-discrimination enforcement. The generator gives you a strong baseline, not a final compliance artifact.
How is this different from ChatGPT or Claude?
You can ask ChatGPT or Claude to write a job description, but you will not get the consistent eight-section structure, the automatic inclusive-language pass, the skills-first qualifications framing, or the substantive equal-opportunity statement without writing a detailed prompt yourself. The generator runs a tested prompt that produces the same structure every time, so a hiring manager who has never written a job description gets the same quality output as a seasoned recruiter. It is purpose-built for one task and tuned for it.
Will the descriptions read as obviously AI-generated?
No. The output is written in plain, specific business prose without the cliched AI tells like overuse of em-dashes, "in conclusion" framing, and generic filler phrases. The structure follows the same shape that experienced recruiters produce manually, so candidates will read the description as a normal job posting. The inclusive-language pass also removes the very phrases that signal "low-effort AI output" to senior candidates, which paradoxically makes the generator output read as more human than a hastily edited template.
Can I use this for executive and C-suite roles?
Yes, with the paid tier 5,000-character limit. Executive role briefs typically need more context, including the company strategy, the board structure, the predecessor situation, and the success criteria for the first year. Paste a longer brief covering all of these and the generator produces an executive-grade description with the same eight-section structure and inclusive-language pass. For confidential searches, omit the company name and use a placeholder; the description will work fine, and you can add the company name when you syndicate the listing to your retained search firm.
What happens if my brief is too short?
If the brief is too sparse to write a meaningful description, the generator asks one clarifying question rather than guessing. For example, if you paste "we need a manager", you will get back "what kind of manager, and what team or function will they own?" rather than a generic invented description. This protects you from publishing a posting that misrepresents the role. Paste at least the role title, the team context, and two or three core responsibilities for a clean first generation.
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