Process
The interview lasted 1 hour.
It was divided into:
- 45 minutes for a resume deep dive.
- 15 minutes for coding.
A lot of questions about Transformers were asked. The coding question was medium-level on LeetCode.
The questions were progressive, and I felt the interviewer carefully considered my experience.
Questions
Illustrate the architecture of the Transformer model.
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Interview Statistics
The following metrics were computed from 125 interview experiences for the TikTok Machine Learning Engineer role in United States.
Success Rate
9%
Pass Rate
TikTok's interview process for their Machine Learning Engineer roles in the United States is extremely selective, failing the vast majority of engineers.
Experience Rating
Positive34%
Neutral46%
Negative20%
Candidates reported having good feelings for TikTok's Machine Learning Engineer interview process in United States.
TikTok Work Experiences
4.0
Comfortable, Growing, Lacking Diversity
Machine Learning Engineer • Former Employee
San Jose, California, United States
July 22, 2024
Pros: A comfortable environment and a rapidly growing business.
Cons: A large amount of the employees are Chinese, a little bit bad in diversity.
5.0
Great place for making impacts
Machine Learning Engineer • Former Employee
Seattle, Washington, United States
April 9, 2025
Pros: A fast-moving culture promotes rapid development and experimentation, and you can propose and execute end-to-end projects with minimal red tape.
Cons: The rapid deployment of projects leads to a lack of overall engineering rigor and mentorship. Lots of ML knowledge can be half-baked, and results are not understood well.
2.0
Better be able to speak Mandarin
Machine Learning Engineer • Current Employee
San Jose, California, United States
April 29, 2024
Pros: You can only speak Mandarin at work.
Cons: It's hard to work here without speaking Mandarin.
- 4.0Comfortable, Growing, Lacking DiversityMachine Learning Engineer • Former EmployeeSan Jose, California, United StatesJuly 22, 2024Pros: A comfortable environment and a rapidly growing business.Cons: A large amount of the employees are Chinese, a little bit bad in diversity.
- 5.0Great place for making impactsMachine Learning Engineer • Former EmployeeSeattle, Washington, United StatesApril 9, 2025Pros: A fast-moving culture promotes rapid development and experimentation, and you can propose and execute end-to-end projects with minimal red tape.Cons: The rapid deployment of projects leads to a lack of overall engineering rigor and mentorship. Lots of ML knowledge can be half-baked, and results are not understood well.
- 2.0Better be able to speak MandarinMachine Learning Engineer • Current EmployeeSan Jose, California, United StatesApril 29, 2024Pros: You can only speak Mandarin at work.Cons: It's hard to work here without speaking Mandarin.
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