Applied intuition·Machine Learning Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Coding round at Applied Intuition for an MLE role. One problem, but it was a deceptively tricky config validation thing that required a lot of back-and-forth with the interviewer just to understand the scope.

Questions Asked (1)

Implement a config validator that takes a set of descriptors (potentially nested, e.g. a PersonDescriptor with name, email, and an AddressDescriptor) and validates a JSON input against them.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The problem sounds manageable until you realize all the descriptor classes are custom and you have to figure out the contract yourself.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and defining a clear descriptor schema that supports nesting, then design a recursive validator that traverses the schema and input in parallel, accumulating errors with paths. Discuss trade-offs between strictness, performance, and extensibility, and consider how this applies to ML configs (e.g., model hyperparameters).

Pro tip: Mention that you would separate schema definition from validation logic to allow reuse and testing, and that you'd consider using a library like Pydantic or JSON Schema for production, but implement from scratch to demonstrate understanding.

1. Clarify requirements and scope

Ask about expected input size, error reporting needs, strictness (e.g., allow extra fields?), and whether descriptors are provided as code or data. This shows you think about real-world usage.

2. Define descriptor structure

Propose a simple class or dict-based descriptor with fields like type, required, default, and nested descriptors. Explain how nesting works (e.g., a field can have its own descriptor).

3. Design recursive validation algorithm

Outline a function that takes a descriptor and a value, checks type and constraints, and for nested descriptors, recurses. Accumulate errors with JSON paths for debugging.

4. Discuss trade-offs and extensions

Talk about performance (e.g., caching, early exit), error handling (collect all vs fail fast), and extensibility (custom validators, coercion). Relate to ML configs where validation is critical.

5. Test and iterate

Mention writing unit tests for edge cases (missing fields, wrong types, deep nesting) and iterating based on feedback. This demonstrates engineering rigor.

Key Points to Mention

Recursive validation with path tracking for nested errors
Separation of schema definition and validation logic
Handling optional fields, defaults, and type coercion
Performance considerations for large configs (e.g., lazy validation)
Extensibility for custom validators (e.g., regex, ranges)
Comparison to existing libraries (Pydantic, JSON Schema) and when to use them

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