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Robinhood·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

Robinhood SWE interview with a graph problem that looks straightforward until you actually think about how shared dependencies should be handled. Worth knowing your DAG traversal cold before going in.

Questions Asked (1)

Q1

You have a directed acyclic graph of services. Each service has a base load, and its total 'load factor' is that base load plus the load factors of everything it directly depends on. Given the graph and base loads, compute the load factor for every service.

Algorithms & Data StructuresSystem Design
Author's notes

I jumped straight to DFS and memoization which was the right call, but I fumbled when the interviewer asked about shared dependencies.

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AI HintsAI Generated

Suggested Approach

Model the services as nodes in a DAG and compute each node's load factor using topological order or DFS with memoization. Emphasize that the load factor is the sum of the base load and the load factors of all direct dependencies, which are computed recursively.

Pro tip: Clarify whether 'directly depends on' means immediate successors or all reachable nodes; typically it's immediate dependencies, but confirming avoids a critical misunderstanding. Also, mention that the graph is a DAG, so no cycles, which simplifies computation.

1. Clarify the problem

Confirm the definition of load factor: base load plus the sum of load factors of direct dependencies. Verify that the graph is a DAG and that dependencies are directed edges from a service to its dependencies.

2. Choose an algorithm

Select topological sort (Kahn's algorithm) or DFS with memoization to process nodes in dependency order. Both ensure each node's dependencies are computed before the node itself.

3. Implement computation

For each node in topological order, compute its load factor by summing its base load and the load factors of its direct dependencies. Use a hash map or array to store results.

4. Analyze complexity

State that the time complexity is O(V + E) and space complexity is O(V + E) for storing the graph and results, which is optimal for this problem.

5. Discuss edge cases and extensions

Mention handling of disconnected graphs, multiple roots, and potential for parallel computation. Also, consider if the graph is large and distributed, how you might compute load factors in a system design context.

Key Points to Mention

  • Topological sorting (Kahn's algorithm or DFS) to process nodes in dependency order.
  • Memoization to avoid recomputing load factors for shared dependencies.
  • Time and space complexity: O(V + E) for both.
  • Handling of disconnected components and multiple roots.
  • Potential for parallel or distributed computation if the graph is large.
  • Clarification of the problem statement to ensure correct interpretation of 'directly depends on'.

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