Snapchat·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Snapchat ML engineer interview with a classic puzzle-style coding problem that had a lot more depth than it looked. The follow-up questions pushed into edge cases and complexity analysis pretty hard.

Questions Asked (1)

You're given four numbers and a target value. Using addition, subtraction, multiplication, and division (with parentheses), and using each number exactly once, write a function that returns true if you can form an expression equal to the target. Follow-up: return a valid expression string if one exists. Also discuss division by zero handling, floating-point tolerance, your algorithmic approach, complexity, and how you'd extend this to N numbers or a different set of operators.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew the backtracking approach going in but fumbled explaining the pruning part clearly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then present a recursive backtracking solution that explores all pairwise combinations of numbers and operators. After coding the basic solution, discuss optimizations, complexity, and extensions to N numbers and additional operators.

Pro tip: Demonstrate awareness of floating-point precision by using a tolerance (e.g., 1e-6) and handling division by zero explicitly; also mention memoization to avoid redundant subproblems.

1. Clarify Requirements and Edge Cases

Ask about input format, number types (integers/floats), target type, and whether all numbers must be used. Discuss division by zero and floating-point tolerance upfront.

2. Outline Recursive Backtracking Approach

Explain that you'll recursively pick two numbers, apply each operator, and replace them with the result, reducing the list until one number remains. Check if it equals the target within tolerance.

3. Implement and Handle Edge Cases

Write pseudocode or actual code, ensuring division by zero is skipped and floating-point comparisons use a tolerance. For the follow-up, track expressions as strings alongside values.

4. Analyze Complexity and Optimizations

Discuss time complexity (exponential in number of numbers) and space complexity. Mention memoization or pruning to improve performance.

5. Discuss Extensions and Trade-offs

Explain how to generalize to N numbers (same recursion) and additional operators (e.g., exponentiation). Discuss trade-offs like using rational arithmetic vs. floating-point.

Key Points to Mention

Recursive backtracking with pairwise combination and operator application
Division by zero handling: skip invalid operations
Floating-point tolerance: use epsilon (e.g., 1e-6) for equality checks
Expression string tracking for follow-up: store expression alongside value
Time complexity: O((n! * 4^(n-1)) * n) or similar, space complexity: O(n) recursion depth
Extensions: N numbers, additional operators, and potential optimizations like memoization

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