Machine Learning Engineer applicants have rated the interview process at Quantiphi with 2.1 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 33% positive. To compare, the company-average is 34.5% positive. This is according to Glassdoor user ratings.
Candidates applying for Machine Learning Engineer roles take an average of 60 days to get hired, when considering 9 user submitted interviews for this role. To compare, the hiring process at Quantiphi overall takes an average of 29 days.
Common stages of the interview process at Quantiphi as a Machine Learning Engineer according to 9 Glassdoor interviews include:
Phone interview: 25%
IQ intelligence test: 20%
One on one interview: 20%
Presentation: 15%
Personality test: 10%
Other: 5%
Group panel interview: 5%
Here are the most commonly searched roles for interview reports -
I applied through college or university. I interviewed at Quantiphi
Interview
It was succinct, there was a platform engineer round followed by another round then an ML round and finally HR round. The HR round was really good after having cleared other rounds easily
Interview questions [1]
Question 1
Past projects, hyperparameter optimizations, preprocessing etc
I applied through college or university. I interviewed at Quantiphi (Mumbai)
Interview
4 Rounds
1 - Test -> Aptitude, CSE Fundamentals, Coding
2 - Technical Round 1: Resume based questions and some fundamentals of ML and AI
3 - Technical Round 2: Resume based questions and indepth questions on architectures, frameworks and fundamentally important parts
4 - HR
Pretty easy basic ml question and transformer architectures , no leetcode or ml system design questions, Transformer architecture encoder decoder and autoregressive models Transformer architecture encoder decoder and autoregressive models
Interview questions [1]
Question 1
Transformer architecture encoder decoder and autoregressive models
The process has three rounds: one aptitude test, one technical round on ML basics, algorithms, logical reasoning puzzles, project-related questions, and one HR round focusing on reasoning behind ML model choices.
Interview questions [1]
Question 1
ML algorithms and its working based on projects in resume.