You will be asked a wide range of ML-related questions (ML theory, PyTorch, CNNs, etc.). You will also be asked to code towards the end of the 1 hour session (Leetcode medium). Most of these questions have well-defined answers (e.g., how do you disable gradient computation in PyTorch) while others are more open-ended (e.g., how would you use unlabeled data to boost the performance of your supervised tasks). My major complaints are with these open-ended questions. The interviewer had specific answers in mind and would not understand/accept alternative approaches. The depth of the interviewer's ML knowledge is also questionable as the interviewer did not understand how pretrained networks can be used as feature extractors. The interviewer also asked about variational auto-encoder without knowing the underlying probabilistic formulation. Overall, a negative experience.
Learning Coordinator Interview Questions
11,768 learning coordinator interview questions shared by candidates
Resume questions and coding round was on textual entailment.
The types of tests in statistics
How would you build a recommendation system that would benefit the company?
Asked what systems my company was currently using What attributes my favorite manager had The interviews were more conversational
Typical learning design questions. Also experience in K-12 education.
What crayon would you pick?
End-to-end ML method design in a case study.
Example Questions: Q: How would you visualize and present a particularly complex data set? Q: How would deal with a high dimensional data set? Q: What ML techniques are you familiar with and what are the tradeoffs between them?
What has been your experience with leading a team of instructional designers, faculty engagement specialists, and QA specialists?
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