SAP·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

SAP data science interview with a meaty ML system design problem. One question, but it had enough moving parts to keep me busy for a while.

Questions Asked (1)

You have 1,000 labeled avatar images and 100,000 user descriptions paired with avatars. Design a model that recommends the best matching image from a new pool of 100,000 images for a given user description.

System DesignTechnical Trade-offsData Modeling

Author's notes

My first instinct was to frame it as a retrieval problem using learned embeddings, text on one side, image on the other, train them to agree in a shared space.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and the nature of the data, then propose a two-stage retrieval and ranking system. Use the 100,000 paired descriptions and avatars to train a cross-modal embedding model that maps text and images to a shared space, and leverage the 1,000 labeled avatars for fine-tuning or evaluation. Finally, discuss how to scale the matching to a new pool of 100,000 images using efficient nearest neighbor search.

Pro tip: Emphasize the importance of a human-in-the-loop evaluation and the trade-off between model complexity and latency, especially for a large-scale enterprise application like SAP. Mention that you would start with a simple baseline (e.g., CLIP zero-shot) to validate feasibility before investing in a more complex model.

1. Clarify Requirements and Data

Ask questions to understand the business goal, latency requirements, and data characteristics. Confirm the size of the new image pool, whether descriptions are short or long, and if there are any constraints on model size or inference speed.

2. Design the Model Architecture

Propose a two-tower architecture: one tower encodes user descriptions (e.g., using a transformer like BERT) and another encodes images (e.g., using a CNN or ViT). Train with a contrastive loss to align matching pairs in a shared embedding space.

3. Leverage Labeled Data and Training Strategy

Use the 100,000 paired descriptions and avatars for training, and the 1,000 labeled avatars for validation or fine-tuning. Consider data augmentation and hard negative mining to improve discrimination.

4. Efficient Retrieval and Ranking

For the new pool of 100,000 images, precompute image embeddings and use an approximate nearest neighbor index (e.g., FAISS) to retrieve top-k candidates for a given description embedding. Optionally, re-rank the top candidates with a more complex model.

5. Evaluation and Iteration

Define offline metrics (e.g., recall@k, mean reciprocal rank) and online metrics (e.g., click-through rate). Plan for A/B testing and continuous improvement with user feedback.

Key Points to Mention

Two-tower (dual encoder) architecture for efficient retrieval
Contrastive learning (e.g., triplet loss, InfoNCE) to align text and image embeddings
Use of pre-trained models (e.g., CLIP, BERT, ResNet) and fine-tuning
Approximate nearest neighbor search (e.g., FAISS, Annoy) for scalability
Handling cold-start and out-of-distribution images in the new pool
Trade-offs between accuracy, latency, and computational cost

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