Mistral AI·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Mistral AI software engineer interview with a meaty DP problem that took me a while to fully work through. The GPU scheduling setup carries over from a prior part, so you're building on existing state rather than starting fresh, which adds a layer of pressure.

Questions Asked (1)

Given a 2D grid where grid[d][c] represents GPUs available on day d at cluster c, find the maximum total GPUs a workload can consume over all days if it runs on exactly one cluster per day and can switch clusters at most K times total.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The multi-part setup is what gets you.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Model the problem as a dynamic programming over days and clusters, where the state tracks the current cluster and the number of switches used so far. For each day, either stay on the same cluster or switch to another, updating the maximum GPUs consumed. Optimize transitions using prefix/suffix maxima to achieve O(D*C*K) time, or O(D*C) with careful state design.

Pro tip: Clarify edge cases upfront: K=0 (no switches), K >= D-1 (unlimited switches), and negative or zero GPU counts. Also mention that if K is large, the problem reduces to picking the max per day independently, which can be a quick sanity check.

1. Clarify constraints and edge cases

Ask about grid dimensions, GPU value ranges, and whether K can exceed the number of days. Confirm that switching clusters counts as a change from one day to the next, and that you start with 0 switches used.

2. Define DP state and recurrence

Let dp[d][c][k] be the max GPUs up to day d ending at cluster c with k switches. Recurrence: dp[d][c][k] = grid[d][c] + max(dp[d-1][c][k], max_{c' != c} dp[d-1][c'][k-1]).

3. Optimize transitions

For each day and k, precompute the best and second-best values from the previous day to handle the max over c' != c in O(1). This reduces time to O(D*C*K) and space to O(C*K) by rolling the day dimension.

4. Handle base cases and final answer

Initialize day 0: dp[0][c][0] = grid[0][c], and dp[0][c][k>0] = -inf. The answer is max over c and k <= K of dp[D-1][c][k].

5. Discuss trade-offs and alternatives

Mention that if K >= D-1, the answer is sum of max per day. Also note that if C is small, a simpler O(D*C^2*K) DP might suffice, but the optimized version is better for large C.

Key Points to Mention

Dynamic programming with state (day, cluster, switches used)
Transition optimization using best and second-best previous values to avoid O(C^2) per state
Space optimization by rolling the day dimension
Edge cases: K=0, K >= D-1, negative GPU values
Time and space complexity: O(D*C*K) time, O(C*K) space
Sanity check: if K is large, answer is sum of daily max GPUs

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