Jota Feldmann

Posted on Feb 5

A brief history of tech interviews

#interview #career

Since I've been doing tech interviews for 10+ years and professionally for 5, I decided to look for previous works in that area.

It's a common topic for tech professionals, but finding material is difficult.

This post intends to start a series about this topic.

Let's start with a small recap AFAIK:

Early Days (1950s-1970s) The Birth of Computing & Hiring Engineers

Companies like IBM, Bell Labs, and Xerox PARC were pioneers in hiring computer scientists.

Interviews were academic in nature, focusing on logic, mathematics, and electrical engineering principles.

Hiring was often referral-based, with informal questioning rather than structured interviews.

Many interviewers were scientists and engineers rather than dedicated HR professionals.

The rise of companies like Microsoft, Apple, and Sun Microsystems brought more structured hiring processes.

Microsoft famously introduced puzzle-based interviews, testing problem-solving skills.

Whiteboard coding and algorithm-heavy interviews became the standard, with books like Programming Pearls influencing interview techniques.

Dedicated technical recruiters and specialist interviewers emerged, distinct from hiring managers.

Google revolutionized hiring by implementing data-driven hiring and rigorous coding interviews.

LeetCode-style algorithmic questions and system design interviews became industry norms.

Behavioral interviews like "Googleyness" checks were introduced to assess cultural fit.

Many companies adopted bar-raiser programs (inspired by Amazon), where trained interviewers had veto power over hiring decisions.

The rise of remote interviews with tools like CoderPad, CodeSignal, and online assessments.

AI-powered resume screening and technical assessments became common.

Companies focused on reducing bias in interviews, incorporating structured rubrics and blind hiring practices.

Pair programming interviews and take-home projects grew in popularity.

AI-assisted interviews (e.g., AI grading coding challenges) and machine learning to predict candidate success.

Shift away from pure algorithmic interviews toward real-world problem-solving and system design.

Emphasis on diversity, equity, and inclusion (DEI) in hiring practices.

Live coding interviews and asynchronous video interviews continue to evolve.