Process
HR screening followed by three rounds of technical interviews.
Each round includes a resume deep dive, focusing on past research experience and innovations in transformer-based models.
This is followed by a medium-to-hard level coding challenge.
Questions
Tell me about a time you made an innovative solution to an ML problem.
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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
5.0
Great Work-life Balance, Long Commute
Machine Learning Engineer • Current Employee
Singapore, Singapore
October 28, 2024
Pros: Good environment. Work-life balance.
Cons: Long distance to go to company.
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.
3.0
Good Pay, Exhausting Culture
Machine Learning Engineer • Current Employee
September 20, 2025
Pros: High package and good restaurant.
Cons: very tired and competitive. No one cared about others.
- 5.0Great Work-life Balance, Long CommuteMachine Learning Engineer • Current EmployeeSingapore, SingaporeOctober 28, 2024Pros: Good environment. Work-life balance.Cons: Long distance to go to company.
- 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.
- 3.0Good Pay, Exhausting CultureMachine Learning Engineer • Current EmployeeSeptember 20, 2025Pros: High package and good restaurant.Cons: very tired and competitive. No one cared about others.
TikTok Interview Questions
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