Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026Remote

Summary

OpenAI software engineer interview that was basically a grid simulation problem broken into three escalating parts. BFS-heavy, and the follow-up variant was where things got interesting.

Questions Asked (1)

Given a 2D grid where each cell is either healthy, infected, or immune, simulate how infection spreads to 4-directional neighbors. First, implement basic spread until no more cells can be infected. Then compute the minimum time to fully infect the grid, returning -1 if it's impossible due to immune cells or disconnected regions. Finally, handle a follow-up variant with added constraints like multiple infection sources, immune blockers, or per-cell infection delays.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The first part was fine, standard BFS from all infected cells simultaneously.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Model the grid as a graph and use multi-source BFS to simulate infection spread, tracking time steps. For the minimum time, run BFS from all initially infected cells and check if any healthy cell remains unreachable; return -1 if so. For follow-up variants, adapt the BFS by incorporating multiple sources, immune blockers, or per-cell delays using a priority queue (Dijkstra) or layered BFS.

Pro tip: Clarify the problem constraints and edge cases upfront (e.g., empty grid, all immune, no initial infection) and discuss time/space complexity trade-offs; this shows systematic thinking and prevents misunderstandings.

1. Clarify problem and constraints

Ask about grid size, infection rules (4-directional, simultaneous spread), definition of 'fully infected', and follow-up specifics. Confirm input/output format and edge cases.

2. Design basic simulation

Use BFS with a queue of infected cells, processing level by level to simulate time steps. Mark newly infected cells and continue until no more can be infected.

3. Compute minimum time or detect impossibility

After BFS, check if any healthy cells remain. If yes, return -1; otherwise, return the number of levels processed (time steps).

4. Handle follow-up variants

For multiple sources, initialize queue with all. For immune blockers, treat as obstacles. For per-cell delays, use Dijkstra with a priority queue where edge weights are infection times.

5. Analyze complexity and trade-offs

Discuss O(N) time and space for BFS (N = grid cells), and O(N log N) for Dijkstra. Mention alternative approaches like union-find for connectivity checks.

Key Points to Mention

Multi-source BFS for simultaneous spread from multiple initial infections
Level-order traversal to track time steps and ensure simultaneous updates
Handling immune cells as obstacles and detecting disconnected regions
Using Dijkstra's algorithm or priority queue for per-cell infection delays
Time and space complexity analysis: O(N) for BFS, O(N log N) for Dijkstra
Edge cases: empty grid, no initial infection, all cells immune, unreachable healthy cells

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