Anthropic·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026

Summary

Coding interview at Anthropic for a software engineering role. The problem started straightforward enough but then got layered with two extensions back to back, which is where things got interesting.

Questions Asked (1)

Implement the core algorithmic solution to a programming problem, then extend it to handle a constraint involving consecutive N elements, and further optimize it using only suffix-based information.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The base problem was fine, got through it without much trouble.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and walking through a brute-force solution to establish correctness, then iteratively refine it to handle the consecutive N elements constraint using a sliding window or similar technique. Finally, optimize by leveraging suffix-based information (e.g., suffix sums, suffix arrays, or dynamic programming from the end) to reduce time/space complexity, explaining the trade-offs at each step.

Pro tip: Always verbalize your thought process and the trade-offs between approaches; interviewers value clear reasoning and the ability to iterate over a working solution rather than jumping to the optimal one.

1. Understand and Clarify

Restate the problem in your own words, ask clarifying questions about input constraints, edge cases, and expected output. Confirm the definition of 'consecutive N elements' and what 'suffix-based information' means in this context.

2. Brute-Force Baseline

Describe a straightforward solution (e.g., nested loops) that solves the core problem without the consecutive constraint. Analyze its time and space complexity to set a baseline.

3. Handle Consecutive N Elements

Extend the baseline to enforce the consecutive N constraint. Use a sliding window, prefix sums, or a deque to efficiently compute the required metric over each window of N elements.

4. Optimize with Suffix Information

Identify how suffix-based data (e.g., suffix sums, suffix minima/maxima, or DP from the end) can replace redundant computations. Explain how this reduces complexity, possibly from O(n*N) to O(n) or O(n log n).

5. Analyze and Test

Compare the optimized solution with previous ones, discuss trade-offs (time vs. space, readability vs. performance), and walk through edge cases and test scenarios to validate correctness.

Key Points to Mention

Time and space complexity analysis for each approach (brute-force, sliding window, suffix-based).
Correct handling of edge cases: N=1, N=array length, negative numbers, empty input, etc.
The role of suffix information in avoiding recomputation (e.g., suffix sums for range queries, suffix arrays for pattern matching).
Trade-offs between different data structures (e.g., deque vs. heap for sliding window).
How to maintain code clarity while optimizing, and when to stop optimizing based on constraints.
Potential follow-up questions or extensions (e.g., handling multiple queries, dynamic updates).

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