Amazon·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026

Summary

Amazon ML Engineer technical screen, one coding problem the whole time. The prompt was about building a schema validator for nested JSON structures and I spent most of the session just trying not to miss edge cases.

Questions Asked (1)

Given a JSON-like data structure and a schema definition, implement a validator that checks whether the data conforms to the schema. The schema supports nested objects, lists, and primitive types, and you need to handle missing fields, extra fields, type mismatches, and nullable fields.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

Recursion itself wasn't the hard part.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then outline a recursive validation strategy that handles nested structures and type checks. Discuss trade-offs between strictness and flexibility, and mention how this applies to ML data pipelines at Amazon.

Pro tip: Emphasize the importance of clear error messages and extensibility, as ML engineers often need to debug data issues quickly and adapt schemas over time.

1. Clarify Requirements

Ask about schema format, expected behavior for missing/extra fields, nullable handling, and performance constraints. Confirm whether validation should be strict or lenient.

2. Design Recursive Validator

Outline a recursive function that checks each field against the schema, handling primitives, objects, and lists. Use a type map to dispatch validation logic.

3. Handle Edge Cases

Address missing fields (required vs optional), extra fields (allow or reject), type mismatches, and null values. Discuss how to report errors with paths.

4. Discuss Trade-offs and Scalability

Compare recursive vs iterative approaches, performance implications for large data, and how to extend for new types. Mention potential use of libraries or custom implementation.

5. Relate to ML Context

Explain how this validator ensures data quality for ML training/serving, and how it integrates with Amazon's systems (e.g., SageMaker, data pipelines).

Key Points to Mention

Recursive validation for nested structures
Handling of missing, extra, and nullable fields
Clear error reporting with field paths
Performance considerations for large datasets
Extensibility for new types or custom rules
Integration with ML data pipelines and quality checks

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