Machine Learning Engineer Interview Experience - New York, New York
December 18, 2025
Positive ExperienceNo Offer
Process
I applied through a recruiter.
It was a five-round interview: one screening round plus a full loop of four rounds. All in all, there were six LeetCode-style questions, a behavioral interview, and a system design interview. Everything was online, and everyone was friendly.
Questions
Pretty standard LeetCode style questions.
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Interview Statistics
The following metrics were computed from 9 interview experiences for the Meta Machine Learning Engineer role in New York, New York.
Success Rate
0%
Pass Rate
Meta's interview process for their Machine Learning Engineer roles in New York, New York is extremely selective, failing the vast majority of engineers.
Experience Rating
Positive33%
Neutral33%
Negative33%
Candidates reported having mixed feelings for Meta's Machine Learning Engineer interview process in New York, New York.
Meta Work Experiences
4.0
Highly recommend for early career
Machine Learning Engineer • Former Employee
New York, New York, United States
November 7, 2024
Pros: Autonomy on projects and objectives.
Well-compensated.
Cons: * Too many parallel initiatives
* Mass layoffs
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.
3.0
Hacky and toxic
Machine Learning Engineer • Current Employee
New York, New York, United States
September 6, 2024
Pros: Pay,
projects,
working with talented people.
Cons: No incentive to collaborate, toxic review project spawning short-term projects only, culture too volatile.
- 4.0Highly recommend for early careerMachine Learning Engineer • Former EmployeeNew York, New York, United StatesNovember 7, 2024Pros: Autonomy on projects and objectives. Well-compensated.Cons: * Too many parallel initiatives * Mass layoffs
- 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.
- 3.0Hacky and toxicMachine Learning Engineer • Current EmployeeNew York, New York, United StatesSeptember 6, 2024Pros: Pay, projects, working with talented people.Cons: No incentive to collaborate, toxic review project spawning short-term projects only, culture too volatile.
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