1. Call with the Recruiter: The questions were general and non-technical, such as "Tell me about yourself and your experience" and "Why are you interested in Ninety?" Typical questions that you'd expect from a non-technical recruiter. 2. Technical Interview: First part: I was asked to explain a sentiment analysis implementation, walking through the code line by line while thinking out loud. This included discussing how the code works, identifying the hyper-parameters, and suggesting ways to tune and improve performance. Second part: This focused on a broad range of machine learning and data science topics. Some key questions included: Explain Naive Bayes and Bayes' Theorem. How does it work? What is the Transformer architecture? Can you explain each component in detail? How is feature engineering done in Computer Vision tasks? What is the ResNet architecture, and how does it work? The interview covered both breadth and depth across ML/DL topics. Some LLM questions, involving RAG, etc. The other two interview: project that you're proud of? the challenges you face doing a project and how did you resolve it? followed by many more questions about the current project that they had and the challenges and asked me how would I approach them and my resolutions? ...
