Shopify·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Shopify ML Engineer interview with a grid simulation problem. Pretty straightforward coding round but there were enough edge cases to trip you up if you weren't careful about the boundary conditions.

Questions Asked (1)

A robot navigates a 2D grid using a sequence of instructions (turn left, turn right, move forward, move backward). Given the grid dimensions, starting position, and starting direction, simulate the robot's movement and return its final position and orientation. Instructions that would move the robot off the grid are ignored.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The core logic isn't hard but I spent way too long second-guessing the direction rotation math.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a simulation approach that tracks the robot's position and direction while validating each move against grid boundaries. Discuss trade-offs between direct simulation and alternative representations, and consider how to handle invalid moves efficiently.

Pro tip: Demonstrate awareness of real-world ML engineering by discussing how this simulation could be vectorized or batched for multiple robots, and mention the importance of writing clean, testable code with clear separation of concerns.

1. Clarify requirements and constraints

Ask about grid size limits, instruction set, starting position/direction format, and whether the robot can move backward. Confirm that invalid moves are ignored and that the robot's orientation changes even if the move is invalid.

2. Define state representation

Choose a representation for position (e.g., (x, y)) and direction (e.g., 0=North, 1=East, 2=South, 3=West). Explain how turns update direction using modular arithmetic.

3. Simulate instructions

Iterate through each instruction, updating direction for turns and computing the new position for moves. Check if the new position is within grid bounds; if not, ignore the move but still update direction if it was a turn.

4. Handle edge cases and validate

Consider edge cases: empty instructions, robot starting at boundary, moves that would go off-grid, and backward moves. Validate with small examples and discuss time/space complexity.

5. Discuss trade-offs and optimizations

Mention alternative approaches (e.g., precomputing direction vectors, using complex numbers for direction) and trade-offs between clarity and performance. Discuss how to extend to multiple robots or larger grids.

Key Points to Mention

State representation: position as (x, y) and direction as an integer or enum, with modular arithmetic for turns.
Boundary checking: ensure moves stay within grid dimensions; invalid moves are ignored but turns are always applied.
Time complexity: O(n) where n is number of instructions; space complexity O(1).
Edge cases: empty instruction list, robot at boundary, backward moves, and starting direction.
Trade-offs: direct simulation vs. vectorized operations for multiple robots; clarity vs. performance.
Testing: unit tests for each instruction type and boundary conditions.

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