A persistent gender gap exists in computer science occupations which may be partly driven by the subtle use of gendered language in job advertisements and the biased behavior of job recommendation algorithms. This thesis investigates how gender bias in job ads has evolved over time and how it varies across countries, industries, job roles, and work models. It also explores whether recommendation systems expose applicants differently to masculine- or feminine-coded jobs based on gender. A dataset of nearly 470,000 LinkedIn job advertisements related to computer science from 2014 to 2024 was scraped, filtered, and labelled using a gender bias score based on a curated repository of gender-coded words. Synthetic CVs were generated to simulate male and female applicants and used as users for whom job recommendations were generated using five content-based recommendation models (TF-IDF and Word2Vec variants). Results showed that job advertisements are predominantly masculine-coded, though a decline in masculine bias has occurred since 2018. Variation in bias across country, industry, and role is statistically insignificant with low practical effect sizes. The TF-IDF model exhibited the highest disparity in job exposure, while Word2Vec-SkipGram showed more balanced recommendations. A weak correlation was found between applicant gender and the genderedness of recommended jobs.