Are Your Job Adverts Gender Neutral?

Job advertisements are used to attract the top tech talent to fill your role, however, to avoid engaging just one specific gender and perhaps discouraging the perfect applicant from applying, gender neutral job ads are the way forward.

The fact that women are underrepresented in the tech industry has been extremely documented. One explanation is due to the ‘degree gap’; only 24% of women who have a degree in engineering work in the industry, in comparison to 30% of men and 56% of women in the tech industry leave their role by mid-career. What discourages women from working in the tech industry? Gender biased job adverts are part of the problem. Almost half (48%) of women in STEM roles report discrimination in the hiring process.

Writing gender neutral job adverts not only gives you access to a wider pool of talent in the industry, but will also increase diversity and inclusion in tech. 

Check out our previous blog, to discover why hiring gender diverse tech teams is financially beneficial. 

What is a gender neutral job ad?

Defined by the Cambridge Dictionary, ‘gender neutral’ refers to something that relates to everyone, as opposed to just men or just women. Gender neutral job advertisements pertain to people and not specific genders. 

How to make your job advert gender-neutral

Below are some things to consider when constructing your job advert.

The use of gendered words in job adverts is a form of unconscious bias, as although it might not be intentional, using specific words can mean that your advertisement is aimed towards a particular gender. For example, according to the augmented writing tool Textio, “exhaustive” and “enforcement” carry a masculine undertone, whereas “in touch with” and “transparent” carry feminine undertones. It is important to use gender neutral language where possible when writing your job description, and to utilise a mixture of both ‘feminine’ and ‘masculine’ words so as to not discourage a specific gender from applying. 

Instead of using the word “competitive” (considered masculine), you could say “motivated”. Alternatively to the word “polite” (considered feminine), use “professional”.

Alongside the use of gender neutral language, it is also worth considering the technical content of your job advert. A study by LinkedIn found that women apply to less jobs than men by 20%, which could be explained by the fact that women think they need to match 100% of the criteria, whereas men apply after satisfying around 60%. The take-away from this, is to be aware that men and women consider applying for roles differently and as such to only include criteria in the job advert that is strictly necessary.

Research conducted at the University of Waterloo and Duke University, found that job advertisements which use words considered more masculine, for example ‘competitive’ and ‘dominant’, had no effect on female respondents perception that they were capable to fill the role, but it did affect their impression of belongingness and the appeal of the role. 

To avoid gender bias in your job adverts, Glassdoor recommends highlighting to your potential candidates that your organisation is dedicated to achieving inclusion in the industry, perhaps through your company values. Emphasise any initiatives you have in place that support diversity in tech, so that candidates who apply feel accepted.

Increasing inclusivity in tech

Not only does research show that gender-neutral job adverts get more responses (by 42%), the roles are also filled two weeks faster, compared to gender biased advertisements.

One company – Atlassian – were able to increase their number of female technical hires by 80%, in just one year. One of the steps they took to achieve this was by using the aforementioned writing tool Textio, to analyse the language they used.

Are you a team leader looking for talented tech professionals to join your team?  Speak with our specialist consultants today.

More Insights

Benjamin Hanrieder

Q&A Benjamin Hanrieder (CTO, Engel & Volkers)

In this interview, Benjamin Hanrieder, CTO at Engel & Völkers Technology, discusses the company’s digital transformation, software development practices, and the role of the Cloud Platform Team in ensuring reliable and secure infrastructure. Mat Roche (MR): Can you give us an overview of your background?  Benjamin Hanrieder (BH):  Hi Mat,

Read more >

Get In Touch

Fill out the form below, and we will be in touch shortly.

Seniority

Software Developers
Senior Engineers
Software Architects
Senior Software Architects
Software Tech Leads
Senior Software Tech Leads
Software Tech Lead Managers
Senior Software Tech Lead Managers
Director of Engineering
VP of Engineering


Software Developers
Senior Engineers
Software Architects
Senior Software Architects
Software Tech Leads
Senior Software Tech Leads
Software Tech Lead Managers
Senior Software Tech Lead Managers
Director of Engineering
VP of Engineering

 

Software Developers
Senior Engineers
Software Architects
Senior Software Architects
Software Tech Leads
Senior Software Tech Leads
Software Tech Lead Managers
Senior Software Tech Lead Managers
Director of Engineering
VP of Engineering

 

Role

Full-stack Developers
Back-end Developers
Front-end Developers
DevOps Engineers
SRE Engineers
Cloud Engineers
Platform Engineer
Security Engineers
Mobile Developers
App Developers
No1 software engineers

Full-stack Developers
Back-end Developers
Front-end Developers
DevOps Engineers
SRE Engineers
Cloud Engineers
Platform Engineer
Security Engineers
Mobile Developers
App Developers
No1 software engineers

Full-stack Developers
Back-end Developers
Front-end Developers
DevOps Engineers
SRE Engineers
Cloud Engineers
Platform Engineer
Security Engineers
Mobile Developers
App Developers
No1 software engineers

Skills

React.js
Node.js
AWS
JavaScript
React
Native
PostgreSQL
Ruby on Rails
Java
Angular
Golang
PHP

React.js
Node.js
AWS
JavaScript
React
Native
PostgreSQL
Ruby on Rails
Java
Angular
Golang
PHP

React.js
Node.js
AWS
JavaScript
React
Native
PostgreSQL
Ruby on Rails
Java
Angular
Golang
PHP

Sample By Job Family

Data Scientists
Data Engineers
Data Architects
Data Modelers
BI Developers
Data Analysts
Data Quality Engineers
Data Governance Experts MLOps Engineers
DataOps Engineers
Data Product Managers
Executive Data Leadership

Data Scientists
Data Engineers
Data Architects
Data Modelers
BI Developers
Data Analysts
Data Quality Engineers
Data Governance Experts MLOps Engineers
DataOps Engineers
Data Product Managers
Executive Data Leadership

Sample of Platforms & Tools

Cloud DW: Snowflake, BigQuery, Redshift ETL/ELT Tools: Airflow, dbt, Fivetran, Talend Big Data Frameworks: Spark, Hadoop, Kafka BI & Visualization: Tableau, Power BI, Looker ML Libraries: TensorFlow, PyTorch, Scikit MLOps Platforms: MLflow, Kubeflow, W&B Data Governance: Collibra, Alation Data Observability: Monte Carlo, Databand Data Lakes: Lake Formation, Delta Lake SQL/NoSQL DBs: PostgreSQL, MongoDB Real-time Analytics DBs: Apache Pinot, Druid Gen

Cloud DW: Snowflake, BigQuery, Redshift ETL/ELT Tools: Airflow, dbt, Fivetran, Talend Big Data Frameworks: Spark, Hadoop, Kafka BI & Visualization: Tableau, Power BI, Looker ML Libraries: TensorFlow, PyTorch, Scikit MLOps Platforms: MLflow, Kubeflow, W&B Data Governance: Collibra, Alation Data Observability: Monte Carlo, Databand Data Lakes: Lake Formation, Delta Lake SQL/NoSQL DBs: PostgreSQL, MongoDB Real-time Analytics DBs: Apache Pinot, Druid Gen

Sample of Skills Delivered

Programming in Python, R, and SQL
Python
Data Modeling Statistical
Analysis ETL/ELT
Pipeline Development
Database Design & Administration
Machine Learning Fundamentals
Cloud Data Warehousing
Data Ethics & Compliance
Data Security
Data Visualization & Storytelling
Real-Time Data Streaming

Programming in Python, R, and SQL
Python
Data Modeling Statistical
Analysis ETL/ELT
Pipeline Development
Database Design & Administration
Machine Learning Fundamentals
Cloud Data Warehousing
Data Ethics & Compliance
Data Security
Data Visualization & Storytelling
Real-Time Data Streaming

Sample By Job Family

AI Software Engineers
Gen AI Engineers
Machine Learning Engineers
Deep Learning Engineers
Prompt Engineers
AI Research Scientists
NLP Engineers
Computer Vision Engineers
MLOps Engineers
AI Product Managers
AI Governance Specialists
AI Ethics Office
AI Architects Executive

AI Software Engineers
Gen AI Engineers
Machine Learning Engineers
Deep Learning Engineers
Prompt Engineers
AI Research Scientists
NLP Engineers
Computer Vision Engineers
MLOps Engineers
AI Product Managers
AI Governance Specialists
AI Ethics Office
AI Architects Executive

Sample of Platforms & Tools

LLMs: Open AI, Anthropic, Llama, Gemini ML Platforms: SageMaker, Vertex AI, Azure ML ML Libraries: scikit-learn, XGBoost, LightGBM DL Frameworks: TensorFlow, PyTorch, Keras LLM Frameworks: LangChain, LlamaIndex Vector Databases: Pinecone, Weaviate, Milvus Data Science Notebooks: Jupyter, Colab, Kaggle Data Visualization Libraries: Matplotlib, Seaborn NLP Libraries: Hugging Face Transformers CV Libraries: OpenCV, Pillow, Albumentations MLOps Tools: MLflow, Kubeflow, W&B

LLMs: Open AI, Anthropic, Llama, Gemini ML Platforms: SageMaker, Vertex AI, Azure ML ML Libraries: scikit-learn, XGBoost, LightGBM DL Frameworks: TensorFlow, PyTorch, Keras LLM Frameworks: LangChain, LlamaIndex Vector Databases: Pinecone, Weaviate, Milvus Data Science Notebooks: Jupyter, Colab, Kaggle Data Visualization Libraries: Matplotlib, Seaborn NLP Libraries: Hugging Face Transformers CV Libraries: OpenCV, Pillow, Albumentations MLOps Tools: MLflow, Kubeflow, W&B

Sample of Skills Delivered

Generative AI Development
Machine Learning Algorithms
Deep Learning Architectures
NLP and NLG
Computer Vision
Data & Feature Engineering
MLOps & Model Deployment
Statistical Analysis & Hypothesis Testing
Python and R Programming Expertise
Model Evaluation & Performance Tuning
AI Governance & Compliance
Autonomous AI Agent Engineering
Building Agentic AI Applications

Generative AI Development
Machine Learning Algorithms
Deep Learning Architectures
NLP and NLG
Computer Vision
Data & Feature Engineering
MLOps & Model Deployment
Statistical Analysis & Hypothesis Testing
Python and R Programming Expertise
Model Evaluation & Performance Tuning
AI Governance & Compliance
Autonomous AI Agent Engineering
Building Agentic AI Applications