Intuit·Machine Learning Engineer·Technical Phone Screen·Intermediate

IntermediateRejected

Jun 2026Remote

Summary

Phone screen for an MLE role at Intuit. One coding problem, timestamp manipulation, nothing crazy on paper but I fumbled the execution pretty badly.

Questions Asked (1)

Given a CSV with employee ID, meeting ID, join time, and leave time, compute each employee's longest continuous working duration in a day, excluding time spent in meetings.

Algorithms & Data StructuresData Modeling

Author's notes

I kept flip-flopping between pandas and a plain dict approach and that was my downfall.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem: define 'working duration' as time between first join and last leave, and 'meetings' as intervals to exclude. Then, for each employee, merge overlapping meeting intervals, subtract them from the working window, and find the longest gap between consecutive meetings (including boundaries).

Pro tip: Mention that you would handle edge cases like meetings spanning midnight or employees with no meetings, and discuss time complexity (O(n log n) due to sorting) to show algorithmic maturity.

1. Clarify assumptions and edge cases

Confirm definitions: working day boundaries, whether meetings can overlap, and if multiple meetings can occur simultaneously. Discuss handling of missing data or invalid times.

2. Group and sort data

Group records by employee ID and date. For each employee-day, sort meetings by join time.

3. Merge overlapping meetings

Iterate through sorted meetings and merge any that overlap or are adjacent, producing a list of non-overlapping busy intervals.

4. Compute free intervals

Given the employee's first join and last leave as the working window, subtract merged meetings to get free intervals. Include gaps before the first meeting and after the last meeting.

5. Find longest continuous free duration

Calculate the length of each free interval and return the maximum. If no meetings, the entire working window is free.

Key Points to Mention

Time complexity: O(n log n) due to sorting meetings per employee-day.
Space complexity: O(n) for storing merged intervals.
Handling overlapping meetings by merging intervals.
Edge cases: no meetings, meetings covering entire day, multiple employees, timezone considerations.
Data preprocessing: parsing CSV, converting time strings to comparable formats (e.g., minutes since midnight).
Scalability: if data is large, consider distributed processing (e.g., MapReduce) or streaming algorithms.

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