Machine Learning Engineer Interview Experience - United States
November 10, 2025
Neutral ExperienceNo Offer
Process
The interview begins with a deep dive into my resume. The interviewer asked several technical questions based on my previous experience. After that, a medium-level LeetCode problem was given.
Questions
LLM finetuning techniques: SFT, LoRA, etc.
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Interview Statistics
The following metrics were computed from 115 interview experiences for the TikTok Machine Learning Engineer role in United States.
Success Rate
8%
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
Positive36%
Neutral45%
Negative19%
Candidates reported having good feelings for TikTok's Machine Learning Engineer interview process in United States.
TikTok Work Experiences
4.0
Fast Growth, Poor Communication
Machine Learning Engineer • Former Employee
November 26, 2025
Pros: Fast-paced company, grown fast.
Cons: Excess cross-functional cooperation leads to low communication effectiveness.
5.0
TikTok is not all bad. It’s highly team dependent.
Machine Learning Engineer • Current Employee
Singapore, Singapore
November 14, 2025
Pros: I love my team. I think at TikTok it is very, very team dependent. Some teams are horrible; some are really good. Generally, people are really smart, but some teams can have horrible work cultures, such as micromanaging your time or not respecting your off days, etc. I would say it all depends on your team. If you manage to get into a good team with a good leader, even if the work is tough, it's a lot more manageable.
Cons: The pace is definitely fast, but a good team helps to make it better. My team works hard, but we also respect off days and weekends as much as we can.
4.0
Overall acceptable, but expected poor WLB.
Machine Learning Engineer • Current Employee
Singapore, Singapore
October 27, 2025
Pros: quite a lot of challenges, and the colleagues are excellent.
Cons: Need to work overtime very often and have meetings at night.
- 4.0Fast Growth, Poor CommunicationMachine Learning Engineer • Former EmployeeNovember 26, 2025Pros: Fast-paced company, grown fast.Cons: Excess cross-functional cooperation leads to low communication effectiveness.
- 5.0TikTok is not all bad. It’s highly team dependent.Machine Learning Engineer • Current EmployeeSingapore, SingaporeNovember 14, 2025Pros: I love my team. I think at TikTok it is very, very team dependent. Some teams are horrible; some are really good. Generally, people are really smart, but some teams can have horrible work cultures, such as micromanaging your time or not respecting your off days, etc. I would say it all depends on your team. If you manage to get into a good team with a good leader, even if the work is tough, it's a lot more manageable.Cons: The pace is definitely fast, but a good team helps to make it better. My team works hard, but we also respect off days and weekends as much as we can.
- 4.0Overall acceptable, but expected poor WLB.Machine Learning Engineer • Current EmployeeSingapore, SingaporeOctober 27, 2025Pros: quite a lot of challenges, and the colleagues are excellent.Cons: Need to work overtime very often and have meetings at night.
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