Towards Evidence-Based Tech Hiring Pipelines
Chris Brown, Swanand Vaishampayan
TL;DR
The paper argues that current tech hiring pipelines—primarily relying on resume matching and technical interviews—suffer from bias, misalignment with real SE work, and overreliance on intuition. It proposes an evidence-based decision-making framework that integrates contextual, experiential, and research-based evidence to improve candidate evaluations and employer outcomes. A concrete roadmap is outlined, including longer and more contextualized resumes, beyond-resume data sources, scenario- and project-based interview formats, asynchronous assessments, and responsible AI practices. By bridging research with practice through structured transfer and rigorous evaluation, the work aims to enable fairer, more accurate hiring decisions and more reliable assessments of engineers’ potential, while addressing broader challenges around culture, DEI, and data access in the tech industry.
Abstract
Software engineers are responsible for developing, maintaining, and innovating software. To hire software engineers, organizations employ a tech hiring pipeline. This process typically consists of a series of steps to evaluate the extent to which applicants meet job requirements and can effectively contribute to a development team -- such as resume screenings and technical interviews. However, research highlights substantial flaws with current tech hiring practices -- such as bias from stress-inducing assessments. As the landscape of software engineering (SE) is dramatically changing, assessing the technical proficiency and abilities of software engineers is an increasingly crucial task to meet technological needs and demands. In this paper, we outline challenges in current hiring practices and present future directions to promote fair and evidence-based evaluations in tech hiring pipelines. Our vision aims to enhance outcomes for candidates and assessments for employers to enhance the workforce in the tech industry.