Machine Learning Engineer Interview Experience - Geneva
November 6, 2025
Negative ExperienceNo Offer
Process
Got appointed for a second interview with HM after passing initial HR CV screening. When the interview started, HM said he had not looked at the CV before, as he felt it did not fit what they were looking for. A bit of a waste of resources and time for both parties.
Questions
Why do you think you are a good match for this position?
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Interview Statistics
The following metrics were computed from 2 interview experiences for the Expedia Group Machine Learning Engineer role in Geneva.
Success Rate
0%
Pass Rate
Expedia Group's interview process for their Machine Learning Engineer roles in Geneva is extremely selective, failing the vast majority of engineers.
Experience Rating
Positive0%
Neutral50%
Negative50%
Candidates reported having very negative feelings for Expedia Group's Machine Learning Engineer interview process in Geneva.
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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