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Magic Leap Computer Vision Engineer Interview Questions
& Process

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

Based on 20 interview experiences · FREE TO READ

3.2 Rounds average
Average Typical difficulty
30% Positive experience

Candidate interview experiences

First-hand accounts from people who interviewed at Magic Leap.

Showing 3 of 20
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Computer Vision Engineer

Engineering · more than a year ago

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

The interview process went really well. First, I spoke with the hiring manager who was super nice and asked some great questions, including ones about real-world deployment skills, so keep that in mind. Then, there was a technical round with a project director. They asked relevant questions about point-clouds and tracking. They also inquired about my work and experience, and then came up with spontaneous questions about potential issues an approach might face. It wasn't necessarily about solving a concrete problem, but more about explaining my approach, understanding, and how I'd tackle it. The tough part was the coding test, which involved a LeetCode medium problem and required me to talk through my solution.

Confirmed questions6 questions
  • Can you describe how to find all possible paths between a starting and ending set?
  • Tell me about your experience with real-world deployment skills.
  • What are your thoughts on point-clouds and tracking?
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Magic Leap

Computer Vision Researcher

Research

Mid Average Neutral experience No offer 6 rounds
Interview process
Recruiter call Technical screen Background check
Interview formats
Coding Behavioral

I applied online for this role in Zurich. A recruiter reached out, and I ended up having 6 interviews, a mix of technical and non-technical ones. Everyone was really nice and helpful, which was a nice change from FAANG interviews. Unfortunately, after the interviews, I didn't get any updates for a while. I checked in with the recruiter a few times, but eventually, I got a generic rejection email. I looked up their employee numbers, and they haven't grown at all in the last two years – 1227 employees in July 2020 and also in July 2022 (1216 in Jan 2022, so only 11 hires this year, globally!). I'm not sure if they're actually hiring in Zurich or if it's all a sham. It was a significant time commitment for me, and I assume for them too.

Confirmed questions6 questions
  • Do you have any experience with NDAs?
  • Given the requirements of this position, can you answer some C++ coding questions?
  • Discuss your experience with computer vision.
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Magic Leap

Computer Vision Engineer

Engineering

Entry Easy Neutral experience No offer 2 rounds
Interview process
Recruiter call Phone screen
Interview formats
Technical Behavioral

I applied online and then HR got in touch to set up an interview. The first step was a casual phone chat with HR to figure out if the role and team were a good fit, followed by a technical phone interview.

Confirmed questions3 questions
  • What are some basic computer vision concepts?
  • Can you explain some fundamental machine learning principles?
  • Can you describe your past projects or experiences?

Magic Leap Computer Vision Engineer Interview Questions

Quoted word for word from Magic Leap interview reports.

Regarding SIFT descriptors, how many bits are in its vector, and what information do these bits convey?

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How is global coordinate estimation done from camera coordinates?

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What is considered a feature in computer vision, and what is its necessity?

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Could you describe the working mechanism of a feature tracker?

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Can you describe how to generate a point cloud for an indoor room?

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And what about some deep learning basics like resnet, loss function, kernel size, pooling, dropout, and batch norm?

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Formats, difficulty and experience

Across all 20 Magic Leap interview reports.

Interview formats

Technical 36.7%
Coding 34.7%
Behavioral 22.4%
System Design 2%
Presentation 2%

Interview difficulty

Easy 10%
Average 65%
Difficult 25%

Candidate experience

Neutral 30%
Negative 40%
Positive 30%