Process
The recruiter told me that Scale's process has 3 stages:
- Take-home Computer Vision or NLP assignment (I got the CV assignment)
- ML Research phone screen
- SWE, ML, and behavioral onsite interviews
Questions
I don't want to give this away for confidentiality reasons.
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Interview Statistics
The following metrics were computed from 23 interview experiences for the Scale AI Machine Learning Engineer role in United States.
Success Rate
4%
Pass Rate
Scale AI's interview process for their Machine Learning Engineer roles in the United States is extremely selective, failing the vast majority of engineers.
Experience Rating
Positive30%
Neutral35%
Negative35%
Candidates reported having mixed feelings for Scale AI's Machine Learning Engineer interview process in United States.
Scale AI Work Experiences
5.0
High-Achieving Place with Great People!
Machine Learning Engineer • Current Employee
San Francisco, CA
September 13, 2022
Pros: - Insanely smart people
- Great career trajectories
- Company growth that is out of this world
- Inspiring CEO
- Friendly culture
Cons: Not many people do work hard, but it is worth it.
2.0
Gigantic Annotation Pipeline
Machine Learning Engineer • Current Employee
San Francisco, CA
June 25, 2025
Pros: Work with frontier labs
* Fast growing environment
* Potential fast career growth
Cons: Possible shipwreck due to Meta’s acquisition
3.0
Fast paced, not for everyone
Software Engineer • Former Employee
San Francisco, CA
March 7, 2025
Pros: - A lot of smart and talented people
- Working on cutting-edge AI training pipelines
- Very motivated people
Cons: - Working hard isn't enough; you need to learn to thrive in a toxic and highly political environment.
- It is a 24x7 grind, and a lot of weekends will be sacrificed.
- 5.0High-Achieving Place with Great People!Machine Learning Engineer • Current EmployeeSan Francisco, CASeptember 13, 2022Pros: - Insanely smart people - Great career trajectories - Company growth that is out of this world - Inspiring CEO - Friendly cultureCons: Not many people do work hard, but it is worth it.
- 2.0Gigantic Annotation PipelineMachine Learning Engineer • Current EmployeeSan Francisco, CAJune 25, 2025Pros: Work with frontier labs * Fast growing environment * Potential fast career growthCons: Possible shipwreck due to Meta’s acquisition
- 3.0Fast paced, not for everyoneSoftware Engineer • Former EmployeeSan Francisco, CAMarch 7, 2025Pros: - A lot of smart and talented people - Working on cutting-edge AI training pipelines - Very motivated peopleCons: - Working hard isn't enough; you need to learn to thrive in a toxic and highly political environment. - It is a 24x7 grind, and a lot of weekends will be sacrificed.
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