5 DS questions spread over 2 phone rounds ML questions - Precision, Recall, Clustering algorithms Resume Assignment focused on finding document similarity
Senior Machine Learning Engineer Interview Questions
619 senior machine learning engineer interview questions shared by candidates
Experience in C++11/14, what features of the language do you use? Code matrix multiplication optimized for memory access? Optimisation of convolutions in neural networks (no code required, only explain principle)
Hands-on on coding in java (they were very particular about coding in java, no other language), Leetcode Based Coding. Abstract Machine Learning Problems, Variance Bias Decomposition, Boosting, Naive Bayes and other machine learning algorithms and projects
How do the transformers architechture work?
Describe your last project in detail
they asked me ranking techniques in rag? given an image of a person(such as their driver's license), how can we verify their identity for say kyc processing? tell me the entire story of AI, ML, deep learning to transformers architectures
Meet HR through MS Teams, and then you will be asked to finish a case-solving problem. If you pass, you will go to a meeting with the hiring manager, and they will ask you about how you solved this problem. After this round, you will meet with the technical team, they will ask you an easy-to-medium leetcode, some python scenario cases, work experience, etc.
Why do you want to join Apple? Tell me about your past experience. How would you use a large language model to improve Apple customer personalization experience?
ML based take home assignment - follow up and discussion on assignment. Behavioral round with director of engineering for team fit.
You are an engineer working on search. There has been an incident on social media: users are upset that photo results for certain queries have poor gender diversity, eg. “bodybuilder” shows mostly male bodybuilders. More and more users are posting new queries where results lack gender diversity. This is occurring because our content library is skewed: for each female bodybuilder image we have, we might have 50 male bodybuilders. Another team are manually elevating certain results for certain queries, but this is a manual process, and will not scale well. You have been asked to ship a more general mitigation for this problem by end of day. What would you do?
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