In the late 2010s and early 2020s, as Machine Learning (ML) roles exploded in Silicon Valley, Ali Aminian—a seasoned ML Engineer—noticed a recurring problem. While candidates were often brilliant at math and coding, they frequently failed the portion of the interview. Most existing resources focused on traditional software backend design, which didn't account for the unique complexities of ML, such as data pipelines, model monitoring, and online vs. offline evaluation. Crafting the Framework

But that wasn’t the end. Dadi then told them, “Now take your water to the small tulsi plant in the backyard.”

Ali Aminian (with a foreword by a Staff Engineer at Google, usually cited as helping frame the industry perspective).

Download the PDF (legally). Print the trade-off matrix. Take it to a library. Turn off your phone. For two hours, trace every architecture diagram by hand. Do that three times, and you will walk into the interview not as a candidate, but as a system architect.