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%
Group panel interview: 5%
Other: 5%
Here are the most commonly searched roles for interview reports -
I applied through college or university. The process took 3 days. I interviewed at Quantiphi (Visakhapatnam) in Aug 2025
Interview
The interview consisted of 2 technical rounds, followed by a HR round. The interview questions focused mainly on Data Science and Machine Learning. This was from a campus recruitment drive. I got rejected in the very first round.
Interview questions [1]
Question 1
I was asked the following questions:
Introduce yourself.
Explain your ML projects.
Write Python code to turn a list into a dictionary.
Write Python code to find if a number is a power of 2.
What are the different types of Machine Learning? What is the difference between Supervised and Unsupervised Learning?
Explain the bias variance tradeoff??
Have you worked with Object Detection before?
What is LangChain?
What is Docker?
Explain about LLMs.
What do you know about RNNs and GNNs?
Explain how you use the GROUP BY query in SQL.
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.