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Scale·Machine Learning Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePending
Jul 2026Remote

Summary

Did a virtual onsite ML coding round at Scale with two parts: data prep and debugging. Found 2 out of 3 bugs in the debugging section and now I'm just sitting here wondering if that's enough to pass.

Questions Asked (2)

Q1

Given a dataset, perform data preparation tasks as part of an ML coding assessment.

Data ModelingTechnical Trade-offs
Author's notes

This part felt more manageable.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the dataset's structure, target variable, and business context to prioritize preparation steps. Then systematically handle data quality issues (missing values, outliers, duplicates), engineer relevant features, and scale/normalize as needed, while documenting each decision and its rationale. Validate the impact of each step through quick experiments or cross-validation to ensure improvements.

Pro tip: Always split your data into train/validation/test sets before any preprocessing to avoid data leakage, and use pipelines to encapsulate all steps for reproducibility. This demonstrates production-ready thinking and prevents subtle bugs.

1. Understand the Data and Problem

Explore the dataset (shape, types, distributions, missingness) and clarify the prediction task, evaluation metric, and any domain constraints. This guides which preparation steps are most critical.

2. Clean and Preprocess

Handle missing values (impute or drop), remove duplicates, fix inconsistencies, and treat outliers appropriately. Encode categorical variables and parse dates if needed.

3. Feature Engineering and Selection

Create new features based on domain knowledge, interactions, or transformations (e.g., log, polynomial). Select relevant features using statistical methods or model-based importance to reduce noise.

4. Scale and Transform

Apply scaling (e.g., StandardScaler, MinMaxScaler) or normalization to numerical features as required by the model. Consider power transformations for skewed data.

5. Validate and Iterate

Use cross-validation to evaluate the impact of preprocessing steps on model performance. Iterate by adding or removing steps based on validation results, ensuring no data leakage.

Key Points to Mention

  • Data leakage prevention: split before preprocessing and use pipelines.
  • Handling missing data: imputation strategies (mean, median, mode, model-based) and their trade-offs.
  • Categorical encoding: one-hot, label, target encoding, and when to use each.
  • Feature scaling: why it's necessary for distance-based and gradient-based models.
  • Outlier detection and treatment: methods like IQR, z-score, and robust scaling.
  • Reproducibility: using scikit-learn pipelines, random seeds, and version control for data.

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

Q2

Debug a provided ML code snippet and identify all bugs present.

Root Cause AnalysisAlgorithms & Data Structures
Author's notes

Got 2 out of 3 bugs.

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AI HintsAI Generated

Suggested Approach

First, read the code carefully to understand its intended purpose and data flow. Then, systematically check each component (data handling, model, loss, optimization) for common ML bugs, and finally verify the fix by reasoning about expected behavior or running a quick test.

Pro tip: Always consider the broader ML context: bugs often stem from data leakage, incorrect tensor shapes, or improper loss functions. Mentioning how you would prevent such bugs in production (e.g., unit tests, data validation) shows maturity.

1. Understand the code's intent

Identify what the code is supposed to do (e.g., train a classifier, preprocess data) and the expected inputs/outputs. This helps spot logical inconsistencies.

2. Check data handling and preprocessing

Look for issues like incorrect normalization, data leakage, missing values, or wrong tensor shapes. Ensure data splits are correct and no test data leaks into training.

3. Inspect model architecture and initialization

Verify layer dimensions, activation functions, and weight initialization. Check for mismatches between input and output dimensions or inappropriate activations.

4. Examine loss function and optimization

Ensure the loss function matches the task (e.g., cross-entropy for classification) and that the optimizer is correctly configured with appropriate learning rate and parameters.

5. Validate training loop and evaluation

Check for bugs in the training loop (e.g., missing zero_grad, incorrect backprop) and evaluation metrics. Ensure model is set to train/eval mode appropriately.

Key Points to Mention

  • Data leakage and proper train/validation/test splits
  • Tensor shape mismatches and broadcasting errors
  • Incorrect loss function for the task (e.g., using MSE for classification)
  • Missing optimizer.zero_grad() leading to gradient accumulation
  • Improper activation functions (e.g., softmax before cross-entropy loss)
  • Not setting model to eval mode during inference

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