Companies rely on machine learning engineers to help design and improve the systems that allow their software to improve on its own, rather than being specifically programmed. During the interview process, be prepared to be tested heavily on both computer science and data science knowledge with an emphasis on recognizing patterns and trends. A bachelor's degree in computer science or a related field will be required.
What did you do in your previous job? What tools did you use? Did you process large datasets? Do you write code and how do you ensure clean code practices? What are your MLOps capabilities? What problems did your solutions have in your previous work and how did you fix them? They also asked classic ML questions: When is classical machine learning better than deep neural networks? What is boosting, bagging, XGBoost, random forest, and decision trees in general?
They asked me to explain a machine learning system I had worked on end-to-end and justify the design choices.
Could you please descrive the challenges you had during your recent projects and how you solved them?
a linear algebra coding problem (x = ABC given x, B,C find A)
Guide me through a project that used unstructured datasets
Build a movie recommendation engine
asked about python transformer architecture
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In a typical epoch, how is GPU usage across each stage. Is Forward pass more GPU intensive than Backward pass?
Ordinary Least Squares question, with 3 non collinear points (1,0) (0,0) and (1,1). Define y intercept and slope.
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