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numeric·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Got a coding round at Numeric for a software engineer role. One problem, graph-based, took up the whole session. Not the hardest thing I've ever seen but the details trip you up if you're not careful.

Questions Asked (1)

Q1

You're given a list of translators, each defined as a directed edge from one language to another, and an API call for each edge that performs the actual translation. Given a source language and a target language, find a translation path if one exists and apply each translation in sequence to convert the input text.

Algorithms & Data StructuresAPI & IntegrationsSystem Design
Author's notes

My first instinct was to reach for Dijkstra but there's no weights here so BFS is cleaner and finds the shortest chain.

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AI HintsAI Generated

Suggested Approach

Model the translators as a directed graph where languages are nodes and translators are edges. Use BFS to find the shortest translation path from source to target, then sequentially apply each translator's API to the input text. If no path exists, return an error or the original text.

Pro tip: Mention that BFS guarantees the fewest API calls, which reduces latency and cost. Also, discuss handling API failures with retries or fallback paths to make the solution robust.

1. Model as a graph

Represent languages as nodes and translators as directed edges. Build an adjacency list for efficient traversal.

2. Find translation path

Use BFS from the source language to find the shortest path to the target. Track parent pointers to reconstruct the path.

3. Apply translations sequentially

Iterate through the path, calling each translator's API in order, passing the output of one as input to the next.

4. Handle edge cases and errors

If no path exists, return an error. Handle API failures with retries or fallback paths, and consider cycles or duplicate edges.

Key Points to Mention

  • Graph representation: adjacency list for O(V+E) traversal.
  • BFS for shortest path in unweighted graph, ensuring minimal API calls.
  • Path reconstruction using parent pointers.
  • Sequential API calls with error handling (retries, timeouts).
  • Time and space complexity: O(V+E) for path finding, plus API call latency.
  • Edge cases: no path, cycles, multiple translators between same languages.

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