I had an interview with four team members: three experts in text-to-speech (TTS) and one from human resources. I was asked questions about TTS, deep learning (DL), machine learning (ML), and data structures. For example, I was asked to define regularization and normalization, explain the differences between a dictionary and a list, and describe how to remove repeated elements from a list and so on.
Parenthesis Matching: Write a function to check if parentheses in a string are balanced. Prime Number: Write a function to determine if a number is prime. Discuss the time and space complexity of your solutions. Explore possible optimizations for the implemented solutions. Explain the ML model pipeline from data collection to deployment. What is model quantization and why is it used? Why are you interested in joining Lytx? What are your reflections on past experiences and any mistakes or regrets? What are your key strengths and how do they apply to this role? What challenges do you foresee in the field of computer vision?
Design a CVR prediction model
Describe your best project and all the technical aspects involved.
In the Bar-Raiser Interview, I was asked: What do you think of the transparent compensation policy that Rokt applied?
Como você resolveria o treinamento de uma rede a partir de um celular?
MCQ on Python List slicing, coding question on string manipulation(very easy). ML theory questions: How to avoid overfitting, PCA, Bias-Variance tradeoff.
HR screen basically resume interview and they ask you about your specific machine learning experiences.
Describe the ongoing work experience and how your work affects the company goals?
Mix of general/basic ML and more specific questions.
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