eBay·Machine Learning Engineer·Take-home Assignment·Senior

Senior

Apr 2026

Summary

eBay ML engineer take-home that was basically a mini research project. You had to build a full image-text retrieval pipeline from scratch using ResNet-50 and a sentence transformer, including the joint embedding space, a contrastive loss, and retrieval metrics. More involved than I expected for a single assignment.

Questions Asked (1)

Build a model that projects both image and text encoders into a shared embedding space, train it with a contrastive loss like InfoNCE, and evaluate retrieval quality using metrics like Recall@k and MRR.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and data modalities, then outline the model architecture with separate image and text encoders and a projection head to a shared embedding space. Explain the contrastive training objective (InfoNCE) and how you would evaluate retrieval performance using Recall@k and MRR, while discussing trade-offs and potential challenges.

Pro tip: Emphasize the importance of hard negative mining and large batch sizes for contrastive learning, and mention how you would handle modality gap and alignment issues in practice.

1. Clarify Requirements and Data

Ask about the specific use case (e.g., product search at eBay), data availability (paired image-text data), and scale (number of items, query volume).

2. Design Model Architecture

Propose using pre-trained encoders (e.g., ResNet/ViT for images, BERT for text) and adding projection heads (MLP) to map to a shared embedding space of dimension d.

3. Define Training Objective

Explain InfoNCE loss: for each positive pair, contrast against in-batch negatives, and discuss temperature parameter and symmetric loss.

4. Plan Evaluation Metrics

Describe retrieval evaluation: Recall@k (e.g., R@1, R@5, R@10) and MRR, and how to construct validation sets with ground-truth pairs.

5. Discuss Trade-offs and Improvements

Address challenges like batch size, hard negatives, modality gap, and potential enhancements (e.g., using momentum encoders, distillation).

Key Points to Mention

Choice of pre-trained encoders and projection heads
InfoNCE loss formulation and temperature scaling
Importance of large batch sizes and hard negative mining
Evaluation metrics: Recall@k and MRR
Handling modality gap and alignment
Scalability and deployment considerations (e.g., ANN search)

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