Machine Learning Engineer Interview Experience - Chicago, Illinois
February 1, 2023
Negative ExperienceNo Offer
Process
The first round was a recruiter phone call, with general fit-based questions.
The second round was a live coding round for Python, focusing mainly on Machine Learning algorithms. The Hiring Manager also asked some fit-based questions.
Questions
Why Expedia?
Why do you want to change?
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Interview Statistics
The following metrics were computed from 1 interview experience for the Expedia Group Machine Learning Engineer role in Chicago, Illinois.
Success Rate
0%
Pass Rate
Expedia Group's interview process for their Machine Learning Engineer roles in Chicago, Illinois is extremely selective, failing the vast majority of engineers.
Experience Rating
Positive0%
Neutral0%
Negative100%
Candidates reported having very negative feelings for Expedia Group's Machine Learning Engineer interview process in Chicago, Illinois.
Expedia Group Work Experiences
5.0
Good WLB, average growth
Machine Learning Engineer • Current Employee
Gurgaon, Haryana, India
September 18, 2025
Pros: Good WLB, average growth in established teams.
Cons: not good for ML engineers
5.0
Good Balance, Profit Over Users
Machine Learning Engineer • Current Employee
Seattle, Washington, United States
July 29, 2025
Pros: Work-life balance
Travel discount
Working from a different office
Cons: Success metrics are mainly on profit, not experience.
Not much features are based on user feedback.
4.0
Good and somewhat stable company
Machine Learning Engineer • Current Employee
London, England, United Kingdom
August 15, 2024
Pros: Good life-work balance, reasonable salary, good growth opportunities, and interesting projects.
Cons: - The tech stack is constantly changing, so we need to keep migrating services, which is time-consuming.
- Technical decisions can take a long time to be made because several levels of leadership need to sign off on a project.
- Communication between teams can be challenging and involve politics depending on the project.
- 5.0Good WLB, average growthMachine Learning Engineer • Current EmployeeGurgaon, Haryana, IndiaSeptember 18, 2025Pros: Good WLB, average growth in established teams.Cons: not good for ML engineers
- 5.0Good Balance, Profit Over UsersMachine Learning Engineer • Current EmployeeSeattle, Washington, United StatesJuly 29, 2025Pros: Work-life balance Travel discount Working from a different officeCons: Success metrics are mainly on profit, not experience. Not much features are based on user feedback.
- 4.0Good and somewhat stable companyMachine Learning Engineer • Current EmployeeLondon, England, United KingdomAugust 15, 2024Pros: Good life-work balance, reasonable salary, good growth opportunities, and interesting projects.Cons: - The tech stack is constantly changing, so we need to keep migrating services, which is time-consuming. - Technical decisions can take a long time to be made because several levels of leadership need to sign off on a project. - Communication between teams can be challenging and involve politics depending on the project.
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