Machine Learning Scientist applicants have rated the interview process at Flagship Pioneering with 2.3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 75% positive. To compare, the company-average is 47.5% positive. This is according to Glassdoor user ratings.
Candidates applying for Machine Learning Scientist roles take an average of 60 days to get hired, when considering 4 user submitted interviews for this role. To compare, the hiring process at Flagship Pioneering overall takes an average of 30 days.
Common stages of the interview process at Flagship Pioneering as a Machine Learning Scientist according to 4 Glassdoor interviews include:
Group panel interview: 40%
One on one interview: 40%
Skills test: 20%
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first HR, then hiring manager. Smooth and fast. efficient. very open. clear feedback. kind. I enjoyed the conversation. first HR, then hiring manager. Smooth and fast. efficient. very open. clear feedback. kind. I enjoyed the conversation.
I applied online. I interviewed at Flagship Pioneering
Interview
The interview process was very well structured and described in detail; recruiters were very responsive. About four sessions on the technical questions about random topics on ML and the application (standard for an ML position), and a presentation. At the end, I didn't get the job, but I appreciate not getting ghosted, a genuine rejection letter (not an automated email from a no-reply account), and the detailed positive feedback, so it was easy to interpret areas of opportunity.
Interview questions [1]
Question 1
Different edge cases that appear while training, e.g., very scarce, skewed, large, etc.
I applied in-person. I interviewed at Flagship Pioneering (Cambridge, MA)
Interview
5 rounds of interview. The first one was with the HR and one of the subsequent ones was a seminar. Met with most of the people in the startup within the Flagship ecosystem. Questions were related to the problems the startup is trying to solve. There wasn't any hard technical problem.
Interview questions [1]
Question 1
What is representation learning and why is it important?