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Hyperscience Machine Learning Engineer Interview Questions
& Process

Real candidates share what happened, how many rounds they had,
and how the experience turned out.

Based on 8 interview experiences · FREE TO READ

2.8 Rounds average
Average Typical difficulty
62.5% Positive experience

Candidate interview experiences

First-hand accounts from people who interviewed at Hyperscience.

Showing 3 of 8
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Machine Learning Engineer

Engineering · more than a year ago

Entry Average Positive experience No offer 2 rounds
Interview process
Recruiter call Technical screen Offer
Interview formats
Technical

I had a really friendly recruiter call where they scheduled an interview for the next few days. The interview itself was hands-on coding tests with a super friendly and positive team. They gave me helpful feedback with recommendations and the results right away. A few days later, I got another friendly email. Even though I didn't quite meet their criteria, it was honestly the best interview experience ever!

Confirmed questions0 questions

No confirmed questions were included in this interview report.

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Hyperscience

Machine Learning Engineer

Engineering

Mid Easy Negative experience No offer 4 rounds
Interview process
Recruiter call Take home Technical screen Phone screen
Interview formats
Coding Technical Behavioral

It started with a recruiter hitting me up on LinkedIn and we had an initial phone call. Then I got a hackerrank test with 2 problems to do in an hour. After that, I had another call with an engineer. This was kind of wild, he began asking me about my background. His communication was really bad, like a big gap and he couldn't explain what he wanted. He kept doing that for about 30 minutes and then asked me to code. I finished the code in 10 minutes, but they didn't move forward with my profile. I do these kinds of interviews myself, but the company's rep shouldn't be someone who can't get the message across clearly. As an engineer, it's frustrating not to be evaluated properly because someone else couldn't communicate well.

Confirmed questions4 questions
  • Solve the 3 sum problem.
  • Check if two strings are anagrams.
  • Implement Binary Tree insertion.
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Hyperscience

Machine Learning Engineer

Engineering

Mid Average Neutral experience No offer 3 rounds
Interview process
Recruiter call Take home Technical screen Technical screen
Interview formats
Coding Technical

I submitted my application through the SO jobs portal and was contacted by a recruiter within a few days. The recruiter didn't provide any details about the interview process structure. Initially, there was a HackerRank challenge, which I found to be engaging and not overly challenging. Following that, I had a Google Hangouts interview with an engineer. This interview began with some general questions about programming concepts, after which we proceeded to a coding test using HackerRank. I successfully completed this stage and another similar round was arranged with a different engineer. I also performed well on this coding problem. However, a few days later, I received notification that I wouldn't be moving forward in the process. I reached out to inquire about the reasons for this decision, given my perceived performance, but received no response, not even a standard rejection message.

Confirmed questions3 questions
  • Describe programming concepts.
  • Solve a programming problem related to Breadth-First Search (BFS) and Depth-First Search (DFS).
  • Address a problem concerning anagrams.

Hyperscience Machine Learning Engineer Interview Questions

Quoted word for word from Hyperscience interview reports.

Can you explain what dropout is and the reason for its effectiveness?

Read reports

Solve a programming problem related to Breadth-First Search (BFS) and Depth-First Search (DFS).

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Formats, difficulty and experience

Across all 8 Hyperscience interview reports.

Interview formats

Technical 47.6%
Coding 28.6%
Behavioral 14.3%
System Design 4.8%
Presentation 4.8%

Interview difficulty

Easy 25%
Average 75%
Difficult 0%

Candidate experience

Neutral 25%
Negative 12.5%
Positive 62.5%