Quantiphi Machine Learning Engineer interview experience

The process settled into three to four rounds: an initial aptitude or MCQ screen, followed by technical interviews that leaned heavily on ML fundamentals and what I’d worked on. The questions weren’t presented as deep research problems, but they did span core topics—models like linear and logistic regression, how evaluation metrics work, and standard concepts like bias/variance and train/test split. Coding showed up sometimes as small tasks, but much of the focus stayed theoretical and tied back to my project work and resume.

Across the technical rounds, I talked through practical details