Process
The interview process consists of three rounds of technical interviews.
Two of these rounds are with peers, and one round is with the tech lead.
Each round includes LeetCode-style questions, machine learning foundations, and a review of past projects from your resume.
Questions
Cross-entropy loss function.
Was this helpful?
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
3.0
Good Learning, Bad Balance
Machine Learning Engineer • Current Employee
Mountain View, California, United States
July 11, 2021
Pros: You can learn things there.
Cons: Bad work-life balance there.
5.0
Kind Team, Demanding Workload
Machine Learning Engineer • Current Employee
March 5, 2025
Pros: good, kind, warm, high-payment, nice place
Cons: In person, time tight, workload.
2.0
Good for new grads, but worst culture
Machine Learning Engineer • Current Employee
San Jose, California, United States
November 14, 2024
Pros: You will learn rocket-fast because you are always pushed by overloaded tasks. A fresh NG will handle several senior-level jobs under limited guidance.
Cons: 0 work-life balance. Work, work, work if you are a Chinese speaker. You will be working with people in China during the night for sure.
But for non-Chinese speakers, your life will be much better because the leadership will have 0 expectations from you. You can basically chill all day.
- 3.0Good Learning, Bad BalanceMachine Learning Engineer • Current EmployeeMountain View, California, United StatesJuly 11, 2021Pros: You can learn things there.Cons: Bad work-life balance there.
- 5.0Kind Team, Demanding WorkloadMachine Learning Engineer • Current EmployeeMarch 5, 2025Pros: good, kind, warm, high-payment, nice placeCons: In person, time tight, workload.
- 2.0Good for new grads, but worst cultureMachine Learning Engineer • Current EmployeeSan Jose, California, United StatesNovember 14, 2024Pros: You will learn rocket-fast because you are always pushed by overloaded tasks. A fresh NG will handle several senior-level jobs under limited guidance.Cons: 0 work-life balance. Work, work, work if you are a Chinese speaker. You will be working with people in China during the night for sure. But for non-Chinese speakers, your life will be much better because the leadership will have 0 expectations from you. You can basically chill all day.
TikTok Interview Questions
Explore Interview QuestionsTaro 75Google Interview QuestionsMeta Interview QuestionsAmazon Interview QuestionsApple Interview QuestionsNetflix Interview Questions
Explore Interview ExperiencesOpenAI Interview ExperiencesAnthropic Interview ExperiencesPerplexity Interview ExperiencesWindsurf Interview ExperiencesMistral AI Interview Experiences
