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.
Machine Learning Intern Interview Questions
8,208 machine learning intern interview questions shared by candidates
SQL round : 1st question it was to say the output of two queries 2nd Question : to write cte queries (simple one thou) 3rd question : explain dwh ? Coding round : Searching coding (oops)
Typically panel-based, where senior MLEs or data science leaders discuss deeper topics
Whats Convolutional layers, Pooling layers, Bias and Variance
Given a dataset containing telemetry and video data from autonomous vehicles, how would you pull out all examples when the vehicle needed to make an emergency break due to being cut-off by another vehicle on the road.
NLP related question tokenization, fine tuning, ML LIFE CYCLE
How to build a reinforcement agent?
Q: Different types of regularization? Q: BERT model architecture? Q: Your favorite ML algorithm?
Why do you want to join CluePoints?
Questions around linear regression with complex noise, ML take-home assignment.
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