Apple·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior

Jun 2026

Summary

Apple ML Engineer system design round, one big open-ended question about building a multimodal RAG system from scratch. Pretty brutal scope for a single session but also kind of interesting if you're into this stuff.

Questions Asked (1)

Design a retrieval-augmented generation system that answers user questions from a knowledge base containing multiple modalities including text documents, PDFs, and images. Cover ingestion, indexing, retrieval, chunking, embeddings, re-ranking, answer grounding with citations, evaluation, and failure modes.

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

This thing had like seven sub-parts and I didn't realize until I was already 10 minutes in talking about chunking strategy that I hadn't even touched ingestion.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (latency, scale, privacy, on-device vs. cloud), then walk through the end-to-end pipeline from ingestion to answer generation, emphasizing multimodal handling and grounding. Structure your answer around the data flow, making explicit trade-offs at each stage and ending with evaluation and failure modes.

Pro tip: At Apple, privacy and on-device processing are paramount—highlight how you would keep sensitive data local, use federated learning or differential privacy, and design for low-latency on-device retrieval. Also, mention the importance of a human-in-the-loop for continuous improvement and the need to handle ambiguous queries gracefully.

1. Clarify Requirements and Constraints

Ask about scale (number of documents, queries per second), latency requirements, privacy constraints (on-device vs. cloud), and the types of questions expected. This shapes architectural decisions.

2. Design Ingestion and Indexing Pipeline

Describe how to process multimodal data: extract text from PDFs, OCR images, generate captions or use CLIP for image embeddings, and chunk text intelligently. Then index into a vector database with metadata for filtering.

3. Outline Retrieval and Re-ranking

Explain hybrid retrieval (dense + sparse) to handle diverse queries, then apply a cross-encoder re-ranker to improve precision. Discuss trade-offs between latency and accuracy.

4. Generate Grounded Answers with Citations

Use a large language model to synthesize an answer from retrieved chunks, ensuring each claim is supported by a citation to the source. Discuss techniques like constrained decoding or post-hoc attribution.

5. Evaluate and Address Failure Modes

Define metrics (retrieval recall, answer faithfulness, citation accuracy) and set up offline and online evaluation. Identify failure modes (e.g., hallucination, modality mismatch) and propose mitigations.

Key Points to Mention

Multimodal embeddings: Use separate encoders (e.g., CLIP for images, text embeddings for documents) and align them in a shared space or use a unified model like ImageBind.
Chunking strategies: Overlapping chunks, semantic chunking, and preserving document structure (e.g., headings, tables) to maintain context.
Hybrid retrieval: Combine BM25 with dense retrieval to handle both keyword and semantic queries, especially for technical documents.
Re-ranking: Use a cross-encoder (e.g., monoBERT) to re-rank top-k results, balancing latency and accuracy.
Answer grounding: Implement citation by linking generated tokens to source chunks, possibly using attention or a separate attribution model.
Evaluation: Use metrics like RAGAS (faithfulness, answer relevance, context precision) and human evaluation; also consider adversarial testing for robustness.

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