Machine Learning Engineer applicants have rated the interview process at Kensho Technologies with 3 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 30.3% positive. This is according to Glassdoor user ratings.
Candidates applying for Machine Learning Engineer roles take an average of 11 days to get hired, when considering 3 user submitted interviews for this role. To compare, the hiring process at Kensho Technologies overall takes an average of 20 days.
Common stages of the interview process at Kensho Technologies as a Machine Learning Engineer according to 3 Glassdoor interviews include:
Phone interview: 30%
One on one interview: 20%
Skills test: 20%
Group panel interview: 10%
Other: 10%
Presentation: 10%
Here are the most commonly searched roles for interview reports -
I applied online. The process took 1 week. I interviewed at Kensho Technologies in Jun 2024
Interview
I only went through 2 stages of the interview but the recruiter told me there are 4 steps total:
1. Phone screening with the recruiter to walk through your resume.
2. Take home challenge - This is a 4 hour coding problem where you demonstrate how you solve problems. You should know about Time Series Analysis and Text Classification.
3. Coding Interview
4. ML Knowledge Interview to answer conceptual ML questions and meeting with manager
Overall, the take home challenge was difficult and I was not able to get through it.
I applied through a recruiter. The process took 2 weeks. I interviewed at Kensho Technologies (New York, NY) in Jul 2025
Interview
The interviews were Thorough and interesting. Interviewers took time to look into all aspects of my experience, and interests. Very enjoyable overall. I Would recommend based on the interview alone
Adding to the list of reviews here where (a) you get a multi-hour take-home challenge and (b) you get a template rejection with no feedback. Kind of a waste of time.
Interview questions [1]
Question 1
Solve a classification problem using ML methods of your choice.