Tech interview stage: NLP/AI screening questions, i.e.: You can see that the model's performance isn't improving during training. How do you figure out what is wrong? Loop stage: standard behavioural questions on LP. Loop stage tech questions: more of the same as the tech screening, more generalist AI/ML trivia + some extremely niche unrelated to position areas like clustering.
Description: HouseSigma’s the most used real estate platform in Canada. We are able to collect huge amounts of user activities data. The objective is to build a recommendation engine that is able to recommend relevant real estate listings to users in real time based on the user's past activities. Goal: You are provided sample user activity data in json format. Note that due to the volume of user activity data, it’s stored in Mongodb in Json format. Please use machine learning techniques to build a recommendation engine that recommends relevant listing content to users.
They asked me about the GSM technology used in maha Kumb mela to handle traffic.
What is Positional Encoding? Bagging vs Boosing, transformers architecture. Some SQL questoins.
ML algorithmas and Project work
About My skill and Projects
Explain the differences between random forests and neural nets.
They asked about my Skills ,Projects and interest.
2nd interview was related to the assignment. They asked about the approach and why it would/wouldnt work.
Self-introduction. Why do you want to work in this field? What are your opinions on LLMs? Are you willing to relocate to New York?
Viewing 461 - 470 interview questions