Anduril Industries·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Anduril Industries coding round for a software engineer role. One problem, optimization-flavored, took me longer than it should have to nail down the edge cases.

Questions Asked (1)

You have a list of rod lengths. You can cut any rod into pieces of a single chosen integer length. Each cut has a fixed cost, and each resulting piece earns revenue proportional to its length and a sale price. If a rod divides evenly into k pieces, you only need k-1 cuts; otherwise k cuts. You can also skip a rod entirely if it would hurt your profit. Find the integer cut length that maximizes total profit across all rods.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I got the basic structure pretty fast: iterate over candidate sale lengths, simulate the cuts, track profit.

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AI HintsAI Generated

Suggested Approach

Start by restating the problem to ensure you understand the constraints and objectives. Then, propose a brute-force approach that iterates over all possible cut lengths, computes profit for each rod, and selects the maximum. Finally, discuss potential optimizations and trade-offs, such as using prefix sums or early termination.

Pro tip: Demonstrate awareness of edge cases like rods shorter than the cut length or negative profit, and mention that you would validate the solution with test cases. Also, emphasize the importance of clear variable naming and modular code for maintainability.

1. Clarify the problem

Ask clarifying questions about constraints, input size, and profit calculation details. Confirm that cut length must be an integer and that rods can be skipped.

2. Define profit calculation

For a given cut length L, compute the number of pieces k = floor(rod_length / L). If rod_length % L == 0, cuts = k-1; else cuts = k. Profit per rod = k * L * sale_price - cuts * cut_cost. Only include if positive.

3. Brute-force approach

Iterate over all possible integer cut lengths from 1 to max(rod_lengths). For each, compute total profit across all rods and track the maximum.

4. Analyze complexity and optimize

Time complexity is O(max_length * n). Discuss potential optimizations like precomputing rod lengths or using mathematical insights to reduce search space.

5. Handle edge cases and validate

Consider cases where no cut yields positive profit, rods shorter than L, and large inputs. Suggest testing with small examples and boundary values.

Key Points to Mention

Profit formula: revenue = k * L * sale_price, cost = cuts * cut_cost, where cuts = k-1 if divisible else k.
Skip rods if profit is negative; only sum positive profits.
Brute-force over L from 1 to max(rod_lengths) is feasible for moderate input sizes.
Time complexity O(max_length * n) and space complexity O(1) or O(n) if storing rods.
Edge cases: L larger than some rods, L=1, no profitable cuts.
Potential optimization: early termination if profit becomes negative, or using binary search if profit function is unimodal (though not guaranteed).

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