UiPath·Machine Learning Engineer·Technical Phone Screen·Senior

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Jul 2026

Summary

UiPath ML Engineer interview, technical phone screen focused on ML fundamentals. The loss function question was the centerpiece and went deeper than I expected.

Questions Asked (1)

What loss function would you use for a regression model, and how do the common options compare in terms of robustness, gradient behavior, and noise assumptions?

Technical Trade-offsAlgorithms & Data Structures

Author's notes

I started with MSE and MAE which was fine, but the follow-up about noise assumptions is where I got a bit shaky.

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AI HintsAI Generated

Suggested Approach

Start by clarifying that the choice depends on the data distribution and business objective, then compare MSE, MAE, and Huber loss across robustness, gradient behavior, and noise assumptions. Conclude with a recommendation for a typical scenario and mention how to validate the choice empirically.

Pro tip: Mention that the loss function should align with the evaluation metric and that you would empirically compare candidates using cross-validation, as this shows practical maturity beyond theoretical knowledge.

1. Clarify the problem context

Ask about the data distribution, presence of outliers, and whether the business metric is sensitive to large errors. This shows you tailor solutions rather than giving a generic answer.

2. Introduce common loss functions

Briefly describe MSE, MAE, and Huber loss, and mention that each encodes different assumptions about noise (Gaussian, Laplacian, or mixed).

3. Compare along key dimensions

Discuss robustness to outliers, gradient behavior (e.g., MSE gradients grow with error, MAE gradients are constant, Huber transitions), and noise assumptions.

4. Recommend based on trade-offs

Give a concrete recommendation: e.g., use MSE if outliers are rare and large errors are costly; MAE if robustness is critical; Huber as a compromise.

5. Validate empirically

Emphasize that the final choice should be validated via cross-validation and by checking residual plots, as theoretical properties may not hold in practice.

Key Points to Mention

MSE assumes Gaussian noise and penalizes large errors heavily, but is sensitive to outliers.
MAE assumes Laplacian noise, is robust to outliers, but has constant gradients that can slow convergence near the optimum.
Huber loss combines MSE and MAE: quadratic for small errors, linear for large errors, with a tunable delta parameter.
Gradient behavior: MSE gradients are proportional to error, MAE gradients are sign-based, Huber gradients smoothly transition.
Choice depends on the data: if outliers are present, MAE or Huber; if not, MSE is efficient.
Align loss with evaluation metric (e.g., if RMSE is the metric, MSE is natural).

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