Process
The process included:
- Coding questions, which were LeetCode questions.
- Coding, which was DFS plus a basic calculator.
- AI-enabled coding, which was a maze game problem.
- ML system design, which was to design a system for location recommendation.
- Behavioral questions.
Questions
Why do you go for a PhD?
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Interview Statistics
The following metrics were computed from 161 interview experiences for the Meta Machine Learning Engineer role.
Success Rate
12%
Pass Rate
Meta's interview process for their Machine Learning Engineer roles is extremely selective, failing the vast majority of engineers.
Experience Rating
Positive57%
Neutral23%
Negative20%
Candidates reported having very good feelings for Meta's Machine Learning Engineer interview process.
Meta Work Experiences
5.0
Making cool stuff
Machine Learning Engineer • Current Employee
Burlingame, California, United States
June 12, 2024
Pros: 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.
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.
5.0
Good place to work, Low WLB
Machine Learning Engineer • Current Employee
New York, New York, United States
May 18, 2024
Pros: Nice comp, good project scope.
Cons: Low WLB, pretty stressful evaluation cycle.
- 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.
- 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.
- 5.0Good place to work, Low WLBMachine Learning Engineer • Current EmployeeNew York, New York, United StatesMay 18, 2024Pros: Nice comp, good project scope.Cons: Low WLB, pretty stressful evaluation cycle.
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