Instacart·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

ML concepts interview for a machine learning engineer role at Instacart. Two meaty theory questions, both pretty fundamental but they clearly wanted depth not just definitions.

Questions Asked (2)

Walk me through the differences between linear regression and logistic regression, including when you'd use each, how the models are formulated, what loss functions they use, and how you'd interpret the coefficients.

Technical Trade-offs

Author's notes

Knew this cold or thought I did.

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AI HintsAI Generated

Suggested Approach

Structure your answer by contrasting linear and logistic regression across four dimensions: use cases, model formulation, loss functions, and coefficient interpretation. Emphasize the fundamental difference: linear regression predicts continuous outcomes, while logistic regression predicts probabilities for binary classification. Use concrete examples, ideally from Instacart's domain, to illustrate when each is appropriate.

Pro tip: Highlight that logistic regression coefficients represent log-odds, and exponentiated coefficients are odds ratios, which are more interpretable for business stakeholders. Also, mention that while linear regression uses Mean Squared Error (MSE), logistic regression uses log loss (cross-entropy), and that logistic regression is a generalized linear model (GLM) with a sigmoid link function.

1. Define the problem types

Explain that linear regression is used for regression tasks (predicting continuous values), while logistic regression is used for binary classification (predicting probabilities). Give examples: predicting delivery time (linear) vs. predicting whether a customer will reorder (logistic).

2. Describe model formulation

For linear regression, the model is a linear combination of inputs: y = β0 + β1x1 + ... + βnxn. For logistic regression, the linear combination is passed through a sigmoid function to output a probability: p = 1 / (1 + e^-(β0 + β1x1 + ... + βnxn)).

3. Explain loss functions

Linear regression minimizes Mean Squared Error (MSE) between predicted and actual values. Logistic regression minimizes log loss (binary cross-entropy), which penalizes confident wrong predictions and is derived from maximum likelihood estimation.

4. Interpret coefficients

In linear regression, a coefficient βi represents the change in the expected value of y for a one-unit increase in xi, holding other variables constant. In logistic regression, βi represents the change in log-odds; exponentiating gives the odds ratio, which is multiplicative on the odds scale.

5. Discuss practical considerations

Mention assumptions (e.g., linearity, independence) and evaluation metrics (e.g., R-squared, RMSE for linear; accuracy, precision, recall, AUC-ROC for logistic). Also, note that logistic regression can be extended to multiclass via softmax.

Key Points to Mention

Linear regression predicts continuous outcomes; logistic regression predicts probabilities for binary classification.
Linear regression uses identity link; logistic regression uses logit link (sigmoid function).
Loss functions: MSE for linear regression, log loss (cross-entropy) for logistic regression.
Coefficient interpretation: linear regression coefficients are marginal effects; logistic regression coefficients are log-odds, exponentiated to odds ratios.
Logistic regression is a GLM and can be regularized (L1/L2) to prevent overfitting.
Evaluation metrics differ: R-squared, RMSE for linear; accuracy, precision, recall, F1, AUC-ROC for logistic.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Explain the bias-variance tradeoff. How do model complexity, training data size, and regularization each affect it, and what techniques would you use to manage it in practice?

Technical Trade-offsAlgorithms & Data Structures

Author's notes

This one went longer than I expected.

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AI HintsAI Generated

Suggested Approach

Start by defining bias and variance and the tradeoff between them, then systematically explain how model complexity, training data size, and regularization each shift the balance. Finally, describe practical techniques you use to diagnose and manage the tradeoff, ideally with a concrete example relevant to Instacart's scale.

Pro tip: Emphasize that the goal is not to minimize bias or variance in isolation but to minimize total expected error, and mention that at Instacart's scale, the bias-variance tradeoff often manifests as a choice between a simple, stable model that generalizes across diverse grocery behaviors and a complex model that captures nuanced patterns but risks overfitting to noise.

1. Define bias and variance

Explain bias as error from erroneous assumptions (underfitting) and variance as sensitivity to fluctuations in the training set (overfitting). State that total error decomposes into bias^2 + variance + irreducible error.

2. Explain the tradeoff

Describe how increasing model complexity typically decreases bias but increases variance, and vice versa. The optimal model balances these to minimize total error.

3. Analyze the three factors

For each factor: model complexity (higher complexity → lower bias, higher variance), training data size (more data → lower variance, bias unchanged), and regularization (adds bias but reduces variance). Explain their individual and combined effects.

4. Describe practical management techniques

List techniques such as cross-validation, learning curves, regularization (L1/L2, dropout), early stopping, ensembling (bagging, boosting), and data augmentation. Explain when to use each.

5. Tie to Instacart context

Give a concrete example, e.g., predicting delivery times or product recommendations, and explain how you would diagnose bias/variance and apply the techniques to optimize the model.

Key Points to Mention

Bias-variance decomposition and the U-shaped test error curve
Effect of model complexity: underfitting vs. overfitting
Effect of training data size: more data reduces variance without affecting bias
Regularization: L1/L2, dropout, early stopping, and their role in controlling variance
Diagnostic tools: learning curves, validation curves, cross-validation
Ensemble methods: bagging reduces variance, boosting reduces bias

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.