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Machine Learning Software Engineer Interview Questions

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8,212 machine learning software engineer interview questions shared by candidates

Discussion on regression, the types of A.I. and their applications, how would I improve the accuracy of an A.I. visual inspection system in a manufacturing plant, etc. Tested basic understanding of A.I. and methods, and expected projects related to A.I. in the resume.
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Machine Learning Engineer

Interviewed at Quantiphi

3.8★
Jan 26, 2021

Discussion on regression, the types of A.I. and their applications, how would I improve the accuracy of an A.I. visual inspection system in a manufacturing plant, etc. Tested basic understanding of A.I. and methods, and expected projects related to A.I. in the resume.

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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Machine Learning Engineer, Research

Interviewed at PhysicsX

4.2★
Apr 4, 2024

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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