Describe a scenario where a project wasn't going to work, what did you do?
Machine Learning Research Engineer Interview Questions
251 machine learning research engineer interview questions shared by candidates
Difference between generative and discriminative classifiers
The questions ranged from basic topics like bias vs. variance trade-offs to advanced topics like LLMs and self-supervised learning. Usually, they will give you a hypothetical scenario and ask you to solve the problem using ML. Based on your answer, you are asked subsequent questions.
Can not disclose due to NDA
typical coding questions such as getting max element from array and optimization suggestions
Have you used GitHub Actions before?
Just questions about my papers and previous research
A long and in detailed discusssion about my research. Recruiter was very well prepared, read my papers and asked about some details
Tell me about deep learning
My experience with PhysicsX was unfortunately very disappointing and frustrating. Despite being informed of a structured interview process consisting of four rounds, only two technical interviews actually took place. The first round felt more like a formality (easy leetcode problems on Coderbyte), while the second involved a technical assessment centered around 3D datasets relevant to the day-to-day work at PhysicsX. It's worth noting that machine learning (ML) hadn't even entered the discussion at this point. The promised third round, which was supposed to delve into PyTorch and ML optimization, never occurred. Instead, I received a rejection, citing the need for stronger experience in PyTorch and optimization. What's particularly disheartening is that these skills were never even evaluated. This experience not only wasted my time but also left me feeling undervalued as a candidate. I would advise others to carefully weigh the potential time investment before considering opportunities with PhysicsX.
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