Perplexity AI Interview Questions

Debajyoti B. - "As per my understanding, Success of the AI product/feature has to be measured in 2 aspects: success of the problem it solves success of the model used Success of the problem- start with the overall business goal of the problem(acquisition, retention etc) & the high level metric(essentially north star) then comes the product/feature level goals & metrics(conversion, engagement, clicks etc) Success of the model used- general: precision, recall, latency ethical: bias, safety busine"See full answer

Bipin R. - "As you know, this is the most important question for any interview. Here is a structure I like to follow, Start with 'I'm currently a SDE/PM/TPM etc with XYZ company.... ' Mention how you got into PM/TPM/SDE field (explaining your journey) Mention 1 or 2 accomplishments Mention what you do outside work (blogging, volunteer etc) Share why are you looking for a new role Ask the interviewer if they have any questions or will like to dive deep into any of your experience"See full answer

Alex N. - "Since question asks about pipeline. I assume the question is about metrics across many dimensions not just prediction Model performance. For the ML Model: I can use accuracy, precision, recall, F1 if it is classification model. In case it is regression model RMSE is good metric for many problems. Data: ML system needs good quality data. The system has to track missing data rate. Distribution of features, if there is no drift from original feature distributions during the training. Pipeline h"See full answer

Christopher K. - "Background Context I'm a PM at a venture-backed SaaS in the legal tech space. We provide end-to-end contract management experience for our customers with the following features: Upload - Contracts signed in DocuSign, etc. automatically uploaded to our system through API integrations. Data Extraction - Contract terms such as payment terms and key dates are automatically OCRed and extracted using ML. Analytics - Contract terms are searchable, filter, and customize for reporting. Work"See full answer

Rohith K. - "Clarification - I will first clarify what is the data insights we are talking about? Is it a feature in the platform or are we talking about data insights that we use in our work? First listen and not judge - I will first try to understand what the engineer is talking about, what is the disagreement that he is raising. Context - Then I will try to ask the engineer if he has the overall context about the feature/solution/functionality he is talking about. If he has all the cont"See full answer