Machine learning mock interviews
Implement ML building blocks from scratch, explain the theory behind your choices, and design a production ML system from data to monitoring, out loud with Vera asking why.
- ML coding, ML theory, ML system design, behavioral
- Data, training, serving, monitoring
- Regular and lead
The rounds
ML coding
Implement ML building blocks and data code from scratch.
45 min
ML theory & applied ML
Core ML, deep learning, evaluation, LLMs and debugging models.
35 min
ML system design
Design a production ML system: data, training, serving, monitoring.
45 to 50 min
Behavioral
Stories about shipping models, trade-offs, ownership and collaboration.
30 to 35 min
A full loop for one role adds a recruiter screen (12 to 15 min) and a hiring manager conversation (25 to 30 min).
Regular and lead level
- Regular
- Correct fundamentals and working code, explained clearly, with sensible evaluation.
- Lead
- You drive the design, weigh trade-offs in training and serving, and cover cost, operations and team impact.
See the feedback before you start
Full reports from the real report pipeline, for a fictional student and a scripted interview.
Questions
What does the ML theory round cover?
Core ML, deep learning, evaluation, LLMs and debugging models: six questions at regular, five at lead, each probed for depth and practical judgment.
What is asked in ML system design?
Design a production ML system end to end: the data, training, serving and monitoring, then defend it when Vera changes one thing at a time.