luma ai·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Luma AI ML engineer screen, one coding problem focused on data preprocessing. Pretty straightforward if you know numpy/pandas, but the combination of imputation and standardization in one clean pipeline is easy to fumble if you're not careful about the order of operations.

Questions Asked (1)

Given a matrix of positive floats where zeros indicate missing values, impute the zeros using the per-column mean of non-zero entries, then standardize each column to have mean 0 and standard deviation 1. You can use numpy, pandas, or sklearn.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The imputation part is fine, masking zeros and computing column means is a few lines.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

First, compute the mean of non-zero entries for each column, handling potential all-zero columns by falling back to a global mean or zero. Then replace zeros with these column means and standardize each column using the mean and standard deviation of the imputed data. Use numpy or pandas for efficient vectorized operations, and validate the output with assertions.

Pro tip: Mention that you would avoid data leakage by computing imputation and standardization statistics only on the training set if this is part of a larger pipeline. Also, discuss how to handle columns with all zeros to prevent division by zero during standardization.

1. Understand the data and constraints

Identify that zeros represent missing values and that all other values are positive floats. Check for columns that are entirely zeros, as these require special handling.

2. Compute column means for imputation

For each column, calculate the mean of non-zero entries. If a column has no non-zero entries, decide on a fallback (e.g., global mean or zero).

3. Impute missing values

Replace all zeros in each column with the corresponding column mean computed in step 2.

4. Standardize each column

For each column, subtract its mean and divide by its standard deviation. Handle columns with zero standard deviation (e.g., all values identical) by setting them to zero or leaving as is.

5. Validate and discuss trade-offs

Verify that imputed columns have mean 0 and std 1 (for non-constant columns). Discuss potential issues like data leakage and the impact of imputation on variance.

Key Points to Mention

Handling all-zero columns: fallback to global mean or zero, and avoid division by zero during standardization.
Data leakage: compute imputation and standardization statistics only on training data if part of a pipeline.
Efficiency: use vectorized operations (e.g., numpy's nanmean with masking, or pandas DataFrame methods) instead of loops.
Choice of library: numpy for raw arrays, pandas for labeled data, sklearn for pipeline integration (e.g., SimpleImputer with strategy='mean' and StandardScaler).
Impact on distribution: imputation reduces variance, and standardization after imputation may not yield exactly mean 0 and std 1 if not careful.
Edge cases: columns with a single non-zero value, or all values identical after imputation.

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