Machine Learning Engineer applicants have rated the interview process at Qualcomm with 3.4 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 29% positive. To compare, the company-average is 62.5% positive. This is according to Glassdoor user ratings.
Candidates applying for Machine Learning Engineer roles take an average of 42 days to get hired, when considering 7 user submitted interviews for this role. To compare, the hiring process at Qualcomm overall takes an average of 22 days.
Common stages of the interview process at Qualcomm as a Machine Learning Engineer according to 7 Glassdoor interviews include:
Phone interview: 25%
Skills test: 17%
One on one interview: 17%
Presentation: 17%
Group panel interview: 8%
Background check: 8%
Personality test: 8%
Here are the most commonly searched roles for interview reports -
I applied online. The process took 2 weeks. I interviewed at Qualcomm (Bengaluru) in May 2025
Interview
Implement Causal Masking in decoder only architecture using Pytorch.
What is grouped query attention.
What is Mixture of Experts.
Difference between encoder only architecture and decoder only architecture.
Explain convolution operation in CNNs.
What is NMS algorithm in YOLO
->One easy and one medium leetcode level problem
->CNN based number of parameters calculation
->About project , what dataset was used ,what model was used
->C++ class based questions , design a simple class
->Filter related questions
Interview questions [1]
Question 1
->One easy and one medium leetcode level problem
->CNN based number of parameters calculation
->About project , what dataset was used ,what model was used
->C++ class based questions , design a simple class
->Filter related questions
I applied online. The process took 4 weeks. I interviewed at Qualcomm in Aug 2024
Interview
2 virtual interview - one with recruiter and then with hiring manager.
4 onsite interviews back to back with 1 hour in between for lunch with hiring manager (technically also an interview)
Asked a lot of machine learning questions from my resume and even fundamental questions as well - sgd v/s gd, random forest, bagging, boosting, backpropagation, image recognition, and also some computer architecture