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Robinhood·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
Jul 2026

Summary

Robinhood software engineer coding round with two problems, both heavier on implementation than I expected. The calendar layout one especially had more edge cases than it looked like at first glance.

Questions Asked (2)

Q1

Given a list of events for a single day (each with a start and end time in minutes since midnight), write a function that computes a visual layout for a calendar day view. Overlapping events must go in different columns, and you should return each event's column index plus the total columns needed for its overlap group, using the minimum number of columns possible.

Algorithms & Data Structures
Author's notes

This one looked manageable until I started thinking about what 'group' actually means when you have chains of overlapping events.

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AI HintsAI Generated

Suggested Approach

Model this as a graph coloring / interval scheduling problem: sort events by start time, then use a greedy column-assignment pass to place each event in the earliest available column that doesn't conflict with an already-placed overlapping event. After assigning columns, do a second pass to compute the total columns needed per overlap group by identifying connected components of mutually overlapping events.

Pro tip: Mention that this is essentially the same algorithm used in real calendar UIs (e.g., Google Calendar), and proactively discuss edge cases like zero-duration events, back-to-back events that share an endpoint (do they overlap?), and events that span the entire day — this signals production-level thinking beyond the happy path.

1. Clarify Assumptions & Define Overlap

Ask whether events whose end time equals another's start time are considered overlapping (typically they are not). Confirm the output format — column index (0-based) and total columns for the group — and whether events are guaranteed to be within a single day.

2. Sort Events by Start Time

Sort all events by their start time (break ties by end time) to enable a single left-to-right sweep. This O(n log n) step is the foundation for the greedy assignment.

3. Greedy Column Assignment

Maintain a list of columns, each tracking the latest end time of the event placed there. For each event, assign it to the first column whose latest end time is ≤ the event's start time; if none exists, open a new column. Record each event's assigned column index.

4. Identify Overlap Groups & Compute Total Columns

Group events into clusters of mutually overlapping intervals (connected components): sweep through sorted events and merge an event into the current cluster if it starts before the cluster's running max end time, otherwise start a new cluster. The total columns for every event in a cluster equals the maximum column index used within that cluster plus one.

5. Return Results & Analyze Complexity

Construct and return the result array with each event's column index and total columns. State the overall time complexity as O(n log n) due to sorting, and O(n) space for the column tracking structures.

Key Points to Mention

  • Interval overlap detection: two events [s1,e1) and [s2,e2) overlap iff s1 < e2 && s2 < e1 — be explicit about open vs. closed endpoints
  • Greedy column assignment using a min-heap or linear scan on active column end times to achieve O(n log n) or O(n·k) respectively
  • Connected-component / cluster grouping to correctly scope 'total columns' to only the events that mutually interact, not the entire day
  • Edge cases: zero-duration events, back-to-back events sharing an endpoint, all events overlapping (one big group), no events at all
  • The analogy to graph coloring (chromatic number of an interval graph equals the maximum clique size, i.e., maximum simultaneous overlap depth)
  • Trade-off discussion: a priority-queue approach is cleaner and more scalable for large n compared to a naive O(n²) pairwise comparison

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

Q2

You have a list of strings and exactly 3 rows, each with a fixed max width. Process strings in order. Part A: place each string into any row with enough remaining capacity, or report failure. Part B: among valid rows, always pick the one with the most remaining space before placement, breaking ties by smallest row index. Return the placement and remaining space per row.

Algorithms & Data Structures
Author's notes

Part A was fine, pretty mechanical.

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AI HintsAI Generated

Suggested Approach

Break the problem into two clearly defined parts: Part A establishes the feasibility check (greedy placement into any valid row), while Part B adds an optimization layer using a priority queue or sorted selection to always pick the row with maximum remaining capacity. Implement Part A first to validate correctness, then extend it with the tie-breaking selection logic for Part B.

Pro tip: Explicitly call out the difference between the two parts as a greedy feasibility problem versus a greedy optimization problem — interviewers at Robinhood appreciate candidates who recognize algorithmic nuance and can articulate why a max-heap or sorted structure is the right tool for Part B's selection criterion.

1. Clarify Constraints and Examples

Confirm that there are always exactly 3 rows, each with a fixed but potentially different max width, and ask for an example input/output to align on edge cases like strings that exceed all row capacities or empty string lists.

2. Model the State

Represent each row as a tuple or object tracking its index, max width, and remaining capacity. Initialize remaining capacity equal to max width for all three rows before processing begins.

3. Implement Part A — First-Fit Feasibility

Iterate through strings in order; for each string, scan the three rows and place it into the first row with sufficient remaining capacity, updating that row's remaining space. If no row can fit the string, report failure immediately.

4. Implement Part B — Best-Fit with Tie-Breaking

Extend Part A by selecting the row with the maximum remaining capacity before placement; use a max-heap keyed on remaining space (negated for Python's min-heap) with row index as the tie-breaker to efficiently find the optimal row in O(log 3) = O(1) time.

5. Return Results and Validate

After processing all strings, return the placement mapping (string → row index) and the final remaining capacity per row. Walk through your example to verify correctness, and discuss time complexity: O(n) for Part A and O(n log 3) ≈ O(n) for Part B.

Key Points to Mention

  • Greedy algorithm design: Part A is a first-fit greedy feasibility check, while Part B is a best-fit greedy optimization requiring a different selection strategy.
  • Max-heap / priority queue usage for Part B to efficiently select the row with the most remaining space, with row index as a deterministic tie-breaker.
  • Failure condition handling: a string whose length exceeds all rows' remaining capacities must trigger an immediate failure report rather than silent skipping.
  • Immutability of processing order: strings must be placed in the given sequence, ruling out reordering optimizations.
  • Edge cases: strings longer than a row's max width (impossible to place anywhere), all strings fitting in one row, or rows with equal remaining capacity triggering the tie-breaking rule.
  • Space and time complexity analysis: O(n) time for both parts given the fixed constant of 3 rows, and O(n) space for storing placements.

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