Lyft Machine Learning Engineer Interview Questions

I am going through the rounds for Lyft Ml interview. My next round is the ML systems. What am I looking at here and what should I prep? Pls guide if someone has been through this

I applied for a ML position ( training and making for their systems ) for Perception. Phone Interview: Asked some basic Machine learning questions. Some interesting insights about thier system in tracking...

## Problem You are building a training pipeline for an ML model. Given a dataset of labeled samples, implement a stratified split that preserves class proportions in each resulting partition. ```python def stratified_split( data: list[dict], label_key: str, train_ratio: float = 0.7, val_ratio: float = 0.15, test_ratio: float = 0.15, seed: int = 42 ) -> tuple[list[dict], list[dict], list[dict]]: # Returns (train, val, test) pass ``` **Example:** ``` data = [{"x": 1, "y": 0}, {"x": 2, "y": 1}, {"x": 3, "y": 0}, {"x": 4, "y": 1}, {"x": 5, "y": 0}, {"x": 6, "y": 1}] # 3 class-0, 3 class-1 # train=4, val=1, test=1; each split ~50/50 ``` ## Follow-ups 1. Why is stratified splitting especially important for datasets with class imbalance (e.g., 95% negative, 5% positive)? 2. What is the difference between stratified splitting and k-fold stratified cross-validation? 3. If a class has only 2 samples total and you need a 3-way split, how do you handle it gracefully? 4. How do you verify that the split is actually stratified -- what metric do you check per split?