Machine Learning Engineer Interview Experience - Cupertino, California
October 1, 2023
Positive ExperienceGot Offer
Process
There were 6 rounds of interviews.
The first round was the coding round. The second was the ML coding round. The third round was system design. The fourth round was again an ML round. The fifth round was a talk with the team lead. The last round was the HR round.
Questions
Explain XGBoost and AdaBoost in depth.
Was this helpful?
Interview Statistics
The following metrics were computed from 12 interview experiences for the Apple Machine Learning Engineer role in Cupertino, California.
Success Rate
25%
Pass Rate
Apple's interview process for their Machine Learning Engineer roles in Cupertino, California is very selective, failing most engineers who go through it.
Experience Rating
Positive50%
Neutral50%
Negative0%
Candidates reported having very good feelings for Apple's Machine Learning Engineer interview process in Cupertino, California.
Apple Work Experiences
3.0
Good work-life balance, but projects not deep enough for me
Machine Learning Engineer • Current Employee
Cupertino, California, United States
June 17, 2022
Pros: I've enjoyed a great work-life balance, with kind and cool managers and teammates.
Cons: The data science projects available to me are not intellectually stimulating enough. I wish the data was bigger and more complex, so that it would be necessary to leverage some of the state-of-the-art techniques.
3.0
Good pay. Not great work
Machine Learning Engineer • Current Employee
Cupertino, California, United States
December 11, 2021
Pros: - Stellar pay after stock appreciation.
- Good benefits.
Cons: - Not all teams have interesting problems to solve.
Work gets boring sometimes.
- Talent quality is not consistent.
This would obviously vary team by team. From where I stand, half of co-workers are seriously subpar (bad engineering/problem solving, move very slowly).
4.0
Good Pay, Legacy Code
Machine Learning Engineer • Current Employee
Cupertino, California, United States
January 9, 2022
Pros: Good pay.
Good management.
Work on major, influential products.
Excellent on a resume.
Great work-life balance (varies by team).
Cons: Significant amount of legacy code and infra.
Secrecy and divisions are siloed.
Pay not the best (possibly due to brand).
Mediocre benefits.
Top-down management.
- 3.0Good work-life balance, but projects not deep enough for meMachine Learning Engineer • Current EmployeeCupertino, California, United StatesJune 17, 2022Pros: I've enjoyed a great work-life balance, with kind and cool managers and teammates.Cons: The data science projects available to me are not intellectually stimulating enough. I wish the data was bigger and more complex, so that it would be necessary to leverage some of the state-of-the-art techniques.
- 3.0Good pay. Not great workMachine Learning Engineer • Current EmployeeCupertino, California, United StatesDecember 11, 2021Pros: - Stellar pay after stock appreciation. - Good benefits.Cons: - Not all teams have interesting problems to solve. Work gets boring sometimes. - Talent quality is not consistent. This would obviously vary team by team. From where I stand, half of co-workers are seriously subpar (bad engineering/problem solving, move very slowly).
- 4.0Good Pay, Legacy CodeMachine Learning Engineer • Current EmployeeCupertino, California, United StatesJanuary 9, 2022Pros: Good pay. Good management. Work on major, influential products. Excellent on a resume. Great work-life balance (varies by team).Cons: Significant amount of legacy code and infra. Secrecy and divisions are siloed. Pay not the best (possibly due to brand). Mediocre benefits. Top-down management.
Apple 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
