SAP·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

SAP data science interview with a pretty hands-on ML engineering question about modifying PyTorch training code. One question, fairly deep, felt more like a coding screen than a standard ML theory chat.

Questions Asked (1)

Given this PyTorch pseudo-code for a classification model with cross-entropy loss, how would you modify it to use negative sampling instead of computing loss over all classes, assuming you already know the positive IDs?

Technical Trade-offsAlgorithms & Data StructuresSystem Design

Author's notes

This one took me a second to parse because the pseudo-code mixes numpy and PyTorch conventions, which felt a bit sloppy on their end but whatever.

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AI HintsAI Generated

Suggested Approach

First, clarify the goal: replace the full softmax cross-entropy loss with a binary classification objective that distinguishes positive class IDs from a set of randomly sampled negative class IDs. Then, outline the modifications: for each example, gather the positive class logits and a fixed number of negative class logits, apply a sigmoid (binary cross-entropy) loss, and optionally correct for sampling bias. Finally, discuss trade-offs like computational efficiency, memory savings, and potential impact on accuracy.

Pro tip: Mention that negative sampling reduces the softmax normalization to a binary problem, but you must account for the fact that sampled negatives are not true negatives—this introduces bias that can be corrected with techniques like log-uniform sampling or noise contrastive estimation.

1. Understand the baseline

Explain that the original code computes cross-entropy over all classes, which is expensive for large output spaces. Identify the key components: logits, softmax, and loss.

2. Define negative sampling strategy

Decide how many negatives to sample per positive (e.g., 5-20) and the sampling distribution (uniform, log-uniform, or based on class frequency). Mention that the positive IDs are known, so you can focus on sampling negatives.

3. Modify the forward pass

Instead of computing logits for all classes, compute logits only for the positive class and the sampled negative classes. This reduces the output dimension from num_classes to 1 + num_negatives.

4. Change the loss function

Replace cross-entropy with binary cross-entropy (or logistic loss) where the positive class is labeled 1 and negatives are labeled 0. Optionally, apply a correction to account for the sampling probability.

5. Discuss trade-offs and implementation details

Highlight benefits: faster training, lower memory. Mention challenges: potential bias, need for tuning number of negatives, and possible use of techniques like NCE or sampled softmax.

Key Points to Mention

Computational efficiency: reducing from O(num_classes) to O(num_negatives) per example
Binary cross-entropy loss with sigmoid activation instead of softmax
Sampling distribution: uniform vs. log-uniform vs. frequency-based
Bias correction: importance sampling or noise contrastive estimation (NCE)
Hyperparameter tuning: number of negatives, sampling strategy
Memory savings: smaller output layer and gradient computations

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