A gender-neutral job description is one where the language, framing, and requirements do not signal — consciously or unconsciously — that the role belongs to a particular gender. Research by Gaucher, Friesen, and Kay (2011) established that masculine-coded words in job postings reduce female application rates, even when the role itself is entirely neutral. The fix is not about adding a diversity disclaimer at the bottom. It is about rewriting the post itself, word by word.
This is one of the highest-leverage, lowest-cost changes in inclusive hiring in tech. A revised job description costs nothing. A sourcing pipeline that skews 70% male because the JD language filtered women out costs every subsequent hire.
Why JD Language Affects Who Applies
The mechanism is not that candidates read a word like "competitive" and think "this company does not want me." The effect is subtler: gendered language shifts the mental image of who fits the role. When enough masculine-coded signals accumulate in a posting, the imagined ideal candidate shifts from gender-neutral to male — and female candidates self-select out.
LinkedIn's 2019 Global Talent Trends research found that women apply to jobs only when they meet 100% of the stated requirements, while men apply when they meet 60%. This is not a confidence gap on its own — it is a response to risk calculus. Candidates from groups that have historically been screened out apply more carefully. Inflated requirements amplify this effect: every unnecessary line item increases the probability that a qualified candidate disqualifies themselves.
The combined impact: organizations writing standard, unreviewed job descriptions are systematically narrowing their own applicant pools before the first resume is received.
Key insight: The application pool is already filtered before a single recruiter opens a resume. The JD is the first filter — and most organizations never audit it.
Masculine-Coded Words to Remove
The following categories of language have been documented to skew the perceived target gender of a posting. These are not offensive terms — they are words that have accumulated gender associations through cultural use.
Personality and trait descriptors that code masculine
| Remove | Replace with |
|---|---|
| Aggressive (as in "aggressive growth") | "Fast-paced", "high-output" |
| Dominant | "Strong", "experienced" |
| Competitive | "Performance-driven", "results-oriented" |
| Rockstar / Ninja / Superhero | Remove entirely — describe the role, not a persona |
| Outspoken | "Communicates clearly", "proactively shares perspective" |
| Fearless | "Comfortable with ambiguity", "takes initiative" |
| Independent (as a personality requirement) | "Self-directed", "works effectively with autonomy" |
| "Work hard, play hard" | Remove — describes culture in a way that is both gendered and age-skewed |
Action verbs that code masculine
| Remove | Replace with |
|---|---|
| Dominate | "Excel", "lead" |
| Conquer | "Achieve", "exceed" |
| Spearhead | "Lead", "drive" (not "drive" alone — it is overused) |
| Capture (a market) | "Grow", "develop" |
Feminine-coded language also creates imbalance
Feminine-coded words in technical JDs ("supportive," "nurturing," "collaborative at all costs") can discourage male applicants for roles where collaboration is genuinely required. The goal is neutral, skills-based language — not swapping one coded vocabulary for another.
Requirement Inflation: The Hidden Filter
Language bias gets the most attention, but requirement inflation is equally damaging. It operates as a different kind of filter: not by activating gender associations, but by disqualifying candidates who have the ability but not the resume signal.
Common forms of requirement inflation in tech JDs:
- Year-based experience requirements — "5 years of Kubernetes experience" when Kubernetes became mainstream in 2019. "10 years of React" when React launched in 2013. These are impossible or near-impossible to meet for anyone under 35.
- Degree requirements for non-degree roles — "Bachelor's degree required" for a data analyst role that a well-trained bootcamp graduate or self-taught candidate could perform. IBM, Google, Apple, and Delta Air Lines have removed degree requirements from many roles after finding it increased qualified applicant diversity without reducing performance.
- Stacked certifications — Requiring both AWS and Azure certifications for a role that uses one cloud provider. Signals "we have not thought about what this role actually needs."
- Personality traits as requirements — "Must be a self-starter" and "must thrive in ambiguity" as required qualifications. These are preferences, not requirements, and they screen based on described personality rather than demonstrated ability.
For related analysis, see how blind resume screening addresses the downstream effects of these requirements once resumes are submitted.
The Audit Process
For each requirement in your JD, ask three questions:
- Is this genuinely required on day one? If not, move to preferred.
- Is this a proxy for something we could test more directly? If "strong analytical skills" means "can write SQL queries," say that instead.
- Does this requirement correlate with the job or with traditional candidates? "Degree from a top university" is the latter.
Before/After: Real JD Rewrites
Example 1: Senior Software Engineer
Before:
We are looking for a rockstar Senior Engineer who is fearless in tackling complex challenges, can dominate in a fast-paced environment, and is competitive by nature. You will aggressively drive architectural decisions and own critical systems independently.
>
Requirements:
- 8+ years of software engineering experience
- BS/MS in Computer Science or related field required
- Experience with our entire stack (React, Node.js, PostgreSQL, Redis, Kubernetes, AWS, Datadog)
After:
We are looking for a Senior Engineer who takes ownership of complex technical decisions, communicates clearly across teams, and works effectively without close supervision.
>
Requirements:
- 5+ years of software engineering experience with production systems at scale
- Proficiency in at least two of: React, Node.js, PostgreSQL — experience with others is a plus
- Equivalent experience accepted in lieu of a CS degree
The rewrite does not reduce standards. It removes unverifiable personality claims, reduces the experience bar to a defensible level, and opens the role to candidates without formal CS degrees.
Example 2: Product Manager
Before:
We need a driven, type-A Product Manager who thrives in ambiguity and can assertively push product direction against competing stakeholder opinions. MBA preferred.
After:
We need a Product Manager who can articulate a clear product direction, build stakeholder alignment across functions, and make decisions with incomplete information. Formal business education is a plus but not required.
Same role. More applicants.
Tools That Automate the Language Check
| Tool | Cost | What It Does |
|---|---|---|
| **Textio** | Paid (enterprise) | Real-time language analysis with predicted impact on application rates by demographic. Most comprehensive. |
| **Gender Decoder** | Free | Checks text against Gaucher et al. masculine/feminine word lists. Simple and effective for a quick pass. |
| **Ongig Text Analyzer** | Paid | Adds EEOC compliance checking, readability scoring, and requirement audit features. |
| **Hemingway App** | Free | Not DEI-specific, but reducing sentence length and complexity improves comprehension across diverse reading levels. |
| **LinkedIn Job Description Grader** | Free (within LinkedIn Recruiter) | Shows projected gender split of applicant pool based on JD language. |
For most teams, the workflow is: draft the JD, run it through Gender Decoder (free, 30 seconds), fix the flagged terms, then run through Textio if available. The two-tool pass catches 90% of the most impactful language problems.
Tracking whether these changes actually improve your application diversity requires diversity hiring metrics at the funnel level — specifically application-to-screen conversion broken down by demographic.
How Nextmantra AI Approaches This
The most inclusive JD in the world does not fix what happens after a candidate applies. Candidates who clear a well-written posting then hit another bias point: the first-round interview, where unstructured evaluation by whoever happens to be available introduces the subjective variance that structured hiring is designed to remove.
Nextmantra AI addresses the downstream problem: it takes candidates who have applied — through whatever sourcing and screening process a company uses — and runs each one through a consistent, structured 45-minute voice interview. Every candidate gets the same questions, the same evaluation criteria, and the same scoring rubric. A candidate who came from a non-traditional background and passed a well-written JD does not then get downgraded because the interviewer went to a different university.
The JD gets candidates in. Structured evaluation keeps them in.
See how Nextmantra AI handles this
Frequently Asked Questions
What makes a job description gender-neutral?
A gender-neutral job description uses language that does not signal — consciously or unconsciously — that the role is better suited to one gender. This means removing masculine-coded words ("competitive," "dominant," "aggressive growth"), eliminating degree requirements where none are genuinely needed, and framing requirements around demonstrable skills rather than personality adjectives. The goal is that a candidate of any gender reads the posting and sees themselves reflected in it.
Do gender-neutral job descriptions actually increase diverse applications?
Yes, with measurable effect. Research by Gaucher, Friesen, and Kay (2011) in the Journal of Personality and Social Psychology found that job postings with more masculine-coded words attracted fewer female applicants, even when the described role was neutral. LinkedIn's 2019 analysis found women apply for roles only when they meet 100% of stated requirements, while men apply at 60%. Removing inflated requirements and gendered language has been shown to increase diverse application pools by 20-42% in controlled studies.
What are examples of masculine-coded words in job descriptions?
Common masculine-coded words include: aggressive, dominant, competitive, rockstar, ninja, superhero, assertive, independent (as a personality trait), outspoken, fearless, and "work hard play hard." These are not offensive — they activate cultural associations that read as male-skewing to many candidates. The goal is neutral, skills-based language that describes what the person will do, not what kind of person they should be.
Should I remove all requirements from a job description to be inclusive?
No. The goal is to distinguish genuine requirements from preferences. A role that requires Python proficiency should say so. A role that lists "10 years of experience" for a position a skilled candidate could perform in 3-5 years is inflating requirements. Move genuine must-haves to a required section, everything else to preferred. This makes the role accessible to more qualified candidates without changing the actual evaluation standard.
Is requiring a degree discriminatory?
Degree requirements are not inherently discriminatory, but requiring a degree for roles where no degree is genuinely necessary disproportionately filters out candidates from lower-income backgrounds, first-generation college students who did not complete degrees, and candidates who developed skills through non-traditional paths. IBM, Google, Apple, and Delta Air Lines have removed degree requirements from many roles specifically to increase diversity. The test is: does this role genuinely require a degree, or does it require the skills a degree typically signals?
How do I know if my JD language is biased without using a tool?
Read the description and ask: does this sound like it was written for a specific type of person? Look for personality adjectives (assertive, nurturing, confident) rather than skill descriptors (proficient in SQL, experienced with stakeholder management). Look for culture signals like "move fast," "hustle," "no ego" that may read differently across genders and cultures. If the description would fit perfectly on a sports recruiting poster, it probably has masculine-coded language.
What tools check for gendered language in job descriptions?
Textio is the most comprehensive — it analyzes language in real time and suggests rewrites, with data on how specific phrases affect application rates. Gender Decoder (free, online) checks for masculine/feminine coding based on the Gaucher et al. word lists. Ongig's Text Analyzer adds compliance checking for EEOC language. For basic checks, the free Hemingway App reduces sentence complexity, which also improves clarity across diverse reader populations.
Conclusion
Gender-neutral job descriptions are not about writing that avoids taking a position on anything. They are about writing that describes the role — skills, responsibilities, outcomes — without activating gender associations that cause qualified candidates to self-select out before you can evaluate them. The mechanics are straightforward: audit the language, fix the coded terms, trim the inflated requirements, and run a two-minute check through a free tool.
Every improvement to the JD is a permanent change to every future hire that flows through it.
Ready to build a more inclusive hiring process from the first touchpoint? [See Nextmantra AI in practice](https://nextmantra.ai/platform)
Sources: Gaucher, Friesen & Kay, Journal of Personality and Social Psychology (2011); LinkedIn Global Talent Trends (2019); IBM, Google, Apple press releases on degree requirement removal; Textio platform documentation; Ongig blog, The Top 10 Masculine Bias Words Found in Job Descriptions