I had applied online and had the initial recruiter call.
She gave me the option of choosing between general SWE or ML SWE. I moved forward with the ML SWE role.
She scheduled a phone interview. It was an open-ended, probability-based coding question.
I then went onsite. The onsite comprised of 5 interviews:
...and lunch.
After that, since team matching was taking some time, the recruiter forwarded my package to the hiring committee.
I was rejected, with the feedback stating that while the ML feedback was good, the algorithms and coding feedback was not up to the bar of general software engineering.
The entire process was smooth, and the recruiter kept me updated the whole time.
Coding rounds were similar to general software engineering questions based on trees. ML was like ML design rounds.
The following metrics were computed from 3 interview experiences for the Google Machine Learning Software Engineer role in United States.
Google's interview process for their Machine Learning Software Engineer roles in the United States is extremely selective, failing the vast majority of engineers.
Candidates reported having very good feelings for Google's Machine Learning Software Engineer interview process in United States.