Process
It was for an L6 position. After the first chat with the recruiter, I had a 45-minute screening interview, which consisted of discussing my past experiences and some behavioral questions. I was then invited to the virtual onsite.
During the virtual onsite, I had two coding sessions, each lasting 45 minutes. All problems were from the top 20 LeetCode medium problems for Facebook. Following that, I had one ML design session, which involved designing a content recommendation system.
I was supposed to have two behavioral sessions, but one was canceled. The last interview was intended to be ML Specialized. However, the format was not as expected, and the interviewer agreed. Overall, it wasn't as organized as anticipated from a large firm like Facebook.
Questions
Explained in the text above.
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Interview Statistics
The following metrics were computed from 14 interview experiences for the Meta Machine Learning Engineer role in Canada.
Success Rate
Meta's interview process for their Machine Learning Engineer roles in Canada is extremely selective, failing the vast majority of engineers.
Experience Rating
Candidates reported having very good feelings for Meta's Machine Learning Engineer interview process in Canada.
Meta Work Experiences
- 5.0Making cool stuffMachine Learning Engineer • Current EmployeeBurlingame, California, United StatesJune 12, 2024Pros: Meta Reality Lab is a really cool place for people to make cool products. It has plenty of GPUs to engineer.Cons: No work-life balance. A little bit of messy management.
- 2.0Good company, bad behaviorMachine Learning Engineer • Current EmployeeTel Aviv-Yafo, IsraelOctober 25, 2020Pros: Good company, bad behavior. See title.Cons: Some cons, some pros, but mostly cons.
- 4.0Good Pay, Wfh, Limited DevelopmentMachine Learning Engineer • Former EmployeeSan Francisco, California, United StatesJuly 28, 2025Pros: Good pay, WFH, career advancement.Cons: I was the only non-Ph.D. on a fully Ph.D. team, which meant I worked on a lot of the ML implementation and productionization, versus model development.
Meta Interview Questions
