When preparing for an interview, especially the mentioned ML Domain round, do you typically only need to prepare for one of the domains (related to role) or are the interviews more wide breadth. For example could the interviewer could ask questions across the listed topics you listed(eg: ranking/recommendations, computer vision) or will they dive deep and really test knowledge on one? I’ve seen google MLE listings that seem both general and team specific.
Yes, it largely depends on your past ML domain experience and the specific role/team opening at Google.
During the initial recruiter call, they usually try to understand:
1. which ML domains you’ve worked in (e.g., ranking/recommendations, CV, NLP), and
2. where you best fit among the current openings at Google.
Based on that, the interview loop is generally aligned to your primary domain. Interviewers typically go deep into the domain you’re strongest in rather than testing you broadly across all ML Domains.
That said, a baseline understanding of core ML concepts is always expected (model evaluation, overfitting, feature selection, scalability, etc.). But in-depth questioning is usually focused on one primary domain, not all of them.
Regarding the topic of the article, it's fascinating how high the bar is for RSE roles. What if, given the hiring challenges, Google starts to heavily invest in upskilling promising engineers from non-traditional backgrounds? That could really broaden the talent pool, even beyond PhDs.
Thanks keep it up very useful, also preferable to have a similar article on startups (those who pay well)
Awesome! Useful summary!
When preparing for an interview, especially the mentioned ML Domain round, do you typically only need to prepare for one of the domains (related to role) or are the interviews more wide breadth. For example could the interviewer could ask questions across the listed topics you listed(eg: ranking/recommendations, computer vision) or will they dive deep and really test knowledge on one? I’ve seen google MLE listings that seem both general and team specific.
Yes, it largely depends on your past ML domain experience and the specific role/team opening at Google.
During the initial recruiter call, they usually try to understand:
1. which ML domains you’ve worked in (e.g., ranking/recommendations, CV, NLP), and
2. where you best fit among the current openings at Google.
Based on that, the interview loop is generally aligned to your primary domain. Interviewers typically go deep into the domain you’re strongest in rather than testing you broadly across all ML Domains.
That said, a baseline understanding of core ML concepts is always expected (model evaluation, overfitting, feature selection, scalability, etc.). But in-depth questioning is usually focused on one primary domain, not all of them.
Regarding the topic of the article, it's fascinating how high the bar is for RSE roles. What if, given the hiring challenges, Google starts to heavily invest in upskilling promising engineers from non-traditional backgrounds? That could really broaden the talent pool, even beyond PhDs.