Process
The first round is online coding. There is no terminal to execute the results, so you must be very familiar with what your code outputs to pass.
Unlike LeetCode, where you can try out the output, this is not the case here.
Questions
Coding questions with the following questions: sliding window questions.
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Interview Statistics
The following metrics were computed from 92 interview experiences for the Meta Machine Learning Engineer role in United States.
Success Rate
11%
Pass Rate
Meta's interview process for their Machine Learning Engineer roles in the United States is extremely selective, failing the vast majority of engineers.
Experience Rating
Positive53%
Neutral26%
Negative21%
Candidates reported having very good feelings for Meta's Machine Learning Engineer interview process in United States.
Meta Work Experiences
4.0
Good Benefits, Stressful Balance
Machine Learning Engineer • Former Employee
Seattle, Washington, United States
September 11, 2019
Pros: Good benefit, free food, Lyft credit.
Cons: Work-life balance is not good; it's stressful every day.
4.0
Great, with a few downsides
Machine Learning Engineer • Former Employee
August 3, 2020
Pros: A great company that is growing very quickly.
Cons: A rather tense environment with management.
4.0
Learn a lot in Ads
Machine Learning Engineer • Current Employee
New York, New York, United States
June 27, 2025
Pros: Work with really talented people on the team.
Lot of work to be done (no in-team scope fighting).
Cons: Getting stuff done can be tricky due to XFN teams owning products useful for submitting flows.
A lot of pressure on impact.
Ramp-up can be very difficult for an E5.
- 4.0Good Benefits, Stressful BalanceMachine Learning Engineer • Former EmployeeSeattle, Washington, United StatesSeptember 11, 2019Pros: Good benefit, free food, Lyft credit.Cons: Work-life balance is not good; it's stressful every day.
- 4.0Great, with a few downsidesMachine Learning Engineer • Former EmployeeAugust 3, 2020Pros: A great company that is growing very quickly.Cons: A rather tense environment with management.
- 4.0Learn a lot in AdsMachine Learning Engineer • Current EmployeeNew York, New York, United StatesJune 27, 2025Pros: Work with really talented people on the team. Lot of work to be done (no in-team scope fighting).Cons: Getting stuff done can be tricky due to XFN teams owning products useful for submitting flows. A lot of pressure on impact. Ramp-up can be very difficult for an E5.
Meta Interview Questions
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