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 Interview Questions
10,824 machine 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)
What are the benefits of using ECS?
Whats Convolutional layers, Pooling layers, Bias and Variance
MLE-based scenario's questions were there.
3types of ML ML FRAMEWORK AND MANY MORE BUT IT WAS TOUGH INTERVIEW
About previous projects, Basic ML algorithms
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.
Q: Different types of regularization? Q: BERT model architecture? Q: Your favorite ML algorithm?
Just avoid this company, if you are a good engineer who follows best practices.
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