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Google·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026

Summary

Two coding problems for a Google MLE round, both pretty meaty. The first was a weighted interval scheduling problem and the second was the classic robot room cleaning thing where you have to build your own coordinate system blind. Felt like a solid session but I definitely had shaky moments on both.

Questions Asked (2)

Q1

You have a list of meeting requests, each with a start time, end time, and a priority value. Only one meeting can use the room at a time. Write a function that selects a non-overlapping subset of meetings to maximize the total priority sum.

Algorithms & Data Structures
Author's notes

I recognized this as weighted interval scheduling pretty quickly, which helped.

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AI HintsAI Generated

Suggested Approach

Recognize this as a weighted interval scheduling problem and solve it using dynamic programming. Sort meetings by end time, then for each meeting compute the maximum priority sum achievable by either including it (plus the best non-overlapping prior meetings) or excluding it. Use binary search to efficiently find the latest non-overlapping meeting.

Pro tip: Mention that this is a classic DP problem and that the greedy approach fails because priorities are not uniform. Also, discuss how to handle edge cases like empty input or meetings with zero priority.

1. Clarify and Sort

Clarify assumptions (e.g., meetings are half-open intervals, priorities are positive) and sort meetings by end time to enable DP.

2. Define DP State

Let dp[i] be the maximum total priority using a subset of the first i meetings (sorted by end time).

3. Recurrence and Binary Search

For each meeting i, find the latest meeting j that ends before meeting i starts (using binary search). Then dp[i] = max(dp[i-1], priority[i] + dp[j]).

4. Compute and Return

Iterate through meetings to fill dp array, then return dp[n] as the maximum total priority.

5. Analyze Complexity

Sorting takes O(n log n), binary search per meeting O(log n), overall O(n log n) time and O(n) space.

Key Points to Mention

  • Weighted interval scheduling is a dynamic programming problem, not solvable by greedy due to varying priorities.
  • Sorting by end time is crucial for the DP recurrence to work correctly.
  • Binary search efficiently finds the latest non-overlapping meeting, reducing time complexity.
  • DP state definition: dp[i] = max priority using first i meetings.
  • Recurrence: dp[i] = max(dp[i-1], priority[i] + dp[p(i)]) where p(i) is the latest non-overlapping meeting index.
  • Time and space complexity: O(n log n) time, O(n) space.

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

Q2

You control a robot vacuum in an unknown grid room. You can only call move(), turnLeft(), turnRight(), and clean() on it. Implement a function that guarantees every reachable empty cell gets cleaned, without any direct access to the room layout.

Algorithms & Data StructuresSystem Design
Author's notes

This one stressed me out more than I expected.

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AI HintsAI Generated

Suggested Approach

Use a systematic exploration algorithm like spiral search or wall-following to cover the entire reachable area. Maintain an internal representation of the grid and track visited cells to avoid redundant cleaning. Implement backtracking to return to unexplored frontiers when dead ends are encountered.

Pro tip: Discuss how you would handle dynamic obstacles or changes in the environment, and mention the importance of efficient path planning to minimize battery usage and time.

1. Understand the problem and constraints

Clarify that the robot has no prior knowledge of the room layout and can only move, turn, and clean. The goal is to guarantee coverage of all reachable empty cells.

2. Choose a coverage strategy

Select an algorithm such as spiral search, wall-following, or depth-first search with backtracking. Consider the trade-offs between simplicity and efficiency.

3. Implement exploration and mapping

Use the robot's sensors (implied by move() success/failure) to build an internal map. Track visited cells and mark obstacles. Use a stack or queue to manage unexplored frontiers.

4. Handle backtracking and termination

When no unexplored frontiers are adjacent, backtrack to the nearest unexplored cell. Terminate when all reachable cells have been cleaned.

5. Optimize and discuss extensions

Consider optimizations like path smoothing, battery constraints, or dynamic environments. Discuss how the solution scales with room size.

Key Points to Mention

  • Depth-first search (DFS) with backtracking for systematic coverage
  • Maintaining a visited set and internal map to avoid redundant work
  • Using the robot's movement feedback to detect obstacles and boundaries
  • Ensuring termination by tracking all reachable cells
  • Time and space complexity analysis of the chosen algorithm
  • Potential optimizations for real-world constraints like battery life

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