Yelp interview notes, page 2 of 2

“This sounds easy until you're actually on the call and realize you have to pick one thing and go deep, not just name-drop buzzwords.” The rest of the author's notes on Software Engineer interview at Yelp, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.

“This one tripped me up a little because the prompt is doing a lot of work simultaneously.” The rest of the author's notes on Data Scientist interview at Yelp, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.

“Classic product sense question and I went way too broad at first.” The rest of the author's notes on Product Manager interview at Yelp, Onsite - Product Sense / Strategy round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.

“This one requires balancing user experience against revenue, which sounds clean until you actually try to quantify it.” The rest of the author's notes on Product Manager interview at Yelp, Hiring Manager Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.

“The 'for engineers' constraint tripped me up more than I expected.” The rest of the author's notes on Product Manager interview at Yelp, Onsite - Product Sense / Strategy round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.

“I fumbled the setup a bit.” The rest of the author's notes on Data Scientist interview at Yelp, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.

“I jumped straight to funnel breakdowns and probably skipped the part where you sanity-check whether the data is even right.” The rest of the author's notes on Software Engineer interview at Yelp, Hiring Manager Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.

“I led with a two-tower retrieval model for candidate gen because it handles cold-start reasonably well if you lean on item content features instead of pure ID embeddings.” The rest of the author's notes on Data Scientist interview at Yelp, Onsite - System Design / Architecture round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.