- Insight into how blackbox, smoke, and integration cases combine to provide effective coverage.
- Being able to understand and manage your own infrastructure to rule out environmental false positives.
- Ability to quickly grasp functional changes and come up with a corresponding set of test cases.
- Testing of multi-tenant applications and other SaaS considerations.
- Ability to validate test results by gathering data from tables and logs with automation.
- Some familiarity with validating data quality, testing ETL processes, and other data engineering.
- Of course, experience with ML or testing ML-based applications is great, but not required.
- A culture of growth with the room to learn.
- Medical/dental/vision insurance plans.
- Unlimited PTO.
- 100% remote work options.
- A talented team around you equally committed to success.
- Gym reimbursement.
United States of America
(From Everywhere/No Office Location)
You will also partner with data scientists to understand the typical business performance metrics and service level objectives commonly established for different types of algorithms, helping to create synthetic data sets to thoroughly test advanced model performance metrics, and to validate our methods for ensuring the quality, reliability, and accurate detection of model degradation.