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

Senior

Jul 2026

Summary

Snap ML engineer interview with a system design question around building a healthcare agent for medical history summarization. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

How would you design a system for a healthcare agent that summarizes a patient's medical history?

System DesignTechnical Trade-offsData Modeling

Author's notes

I went straight to the model layer and spent too long talking about fine-tuning approaches before realizing they probably wanted to hear about data pipelines, privacy constraints, and how you'd handle unstructured clinical notes.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, such as the input data types, output format, and regulatory needs. Then outline a high-level system architecture covering data ingestion, preprocessing, summarization model, and post-processing with human oversight. Finally, discuss trade-offs in model choice, evaluation metrics, and deployment considerations.

Pro tip: Emphasize patient safety and data privacy by incorporating human-in-the-loop validation and compliance with regulations like HIPAA. Show awareness that summarization must be accurate and unbiased, as errors can have serious consequences.

1. Clarify Requirements and Constraints

Ask questions to understand the scope: What data sources (EHR, notes, labs)? What is the desired summary length and format? Are there regulatory or privacy constraints (e.g., HIPAA)? Who are the end-users (doctors, patients)?

2. Design Data Pipeline and Preprocessing

Outline how to ingest and clean heterogeneous medical data (structured and unstructured). Discuss de-identification, normalization, and handling missing values. Consider using a standardized medical ontology (e.g., SNOMED CT) for consistency.

3. Choose Summarization Approach and Model

Decide between extractive and abstractive summarization. For abstractive, consider fine-tuning a large language model (e.g., BART, T5) on medical data. Discuss trade-offs: accuracy, fluency, computational cost, and explainability.

4. Incorporate Human-in-the-Loop and Safety Measures

Propose a system where the model generates a draft summary that is reviewed and edited by a clinician before finalization. Include safeguards like confidence scores, fact-checking against source data, and bias mitigation.

5. Define Evaluation and Deployment Strategy

Specify metrics (ROUGE, BERTScore, factual consistency, clinician evaluation) and validation on held-out data. Discuss deployment: batch vs. real-time, integration with EHR systems, monitoring for drift, and continuous improvement.

Key Points to Mention

Data privacy and compliance (HIPAA, GDPR) with de-identification and secure storage.
Handling multimodal and unstructured data (clinical notes, lab results, imaging reports).
Model selection trade-offs: extractive vs. abstractive, fine-tuning vs. zero-shot, and computational efficiency.
Evaluation metrics beyond text overlap: factual correctness, completeness, and clinical relevance.
Human-in-the-loop validation to ensure safety and build trust with clinicians.
Scalability and latency considerations for real-time or batch processing in a healthcare setting.

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