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I was asked about the relatively short times I had spent with previous employers. Even though the length of my previous employers had been 3, 5, or 7 years in length (and what I considered pretty common these days), one of the interviewers saw this as a potential negative. Concur is interested in building a long term company and they want people on board for the long haul.
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Corporate Learning Program Manager

Interviewed at SAP Concur

3.7★
Aug 12, 2013

I was asked about the relatively short times I had spent with previous employers. Even though the length of my previous employers had been 3, 5, or 7 years in length (and what I considered pretty common these days), one of the interviewers saw this as a potential negative. Concur is interested in building a long term company and they want people on board for the long haul.

https://leetcode.com/problems/merge-intervals/ https://leetcode.com/problems/validate-binary-search-tree/ I summarized my experience in the referred post above. Scheduled full loop 2months ahead to have ample time for prep (but couldn't really cover much). First codding Interview: Two questions a. Find the maximum sub-tree sum (I couldn't find related question on leetcode). Essentially consider every node in the tree as the root, them calculate the sum of the nodes, then return the maximum such sum b. Variation of https://leetcode.com/problems/word-break/. Return the output as a concatenation with the words from the dictionary separated by single space. E.g string = "catsanddogs", wordDict = ["cat", "sand","cats","dog","and"], an output = "cat sand dogs" or "cats and dogs" Second coding Interview: Two questions a. Return top k integers with highest frequencies in an array: https://leetcode.com/problems/top-k-frequent-elements/ b. Course schedule: https://leetcode.com/problems/course-schedule-ii/ Behavioural interview + one coding question a. Typical workplace behaviour questions b. Find the length of the longest sub-array whose sum is target System design: Design a Machine Learning app that makes recommendation to users for places to visit along their trip. Focus is no the ML pipeline: Data, Features, Evaluation, Model-building, Feedback, Online testing, Offline testing Personal Assessment: Didn't give good account of myself (first time system design). ML system design: Design E2E classification pipeline for Facebook marketplace. Users post visual (photos) + textual descriptions. Lots of focus on the Model training practice + Feature Engineering + Feedback loop, as well as emphasis on theory Personal Assessment: Way better than 4. Calibration (Behaviour + Coding): a. Behaviour similar to 3 b. Given a linked list L, a value val, and position pos, insert val at the posth position in L (Not replace).
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Machine Learning Engineer

Interviewed at Meta

3.5★
Aug 1, 2024

https://leetcode.com/problems/merge-intervals/ https://leetcode.com/problems/validate-binary-search-tree/ I summarized my experience in the referred post above. Scheduled full loop 2months ahead to have ample time for prep (but couldn't really cover much). First codding Interview: Two questions a. Find the maximum sub-tree sum (I couldn't find related question on leetcode). Essentially consider every node in the tree as the root, them calculate the sum of the nodes, then return the maximum such sum b. Variation of https://leetcode.com/problems/word-break/. Return the output as a concatenation with the words from the dictionary separated by single space. E.g string = "catsanddogs", wordDict = ["cat", "sand","cats","dog","and"], an output = "cat sand dogs" or "cats and dogs" Second coding Interview: Two questions a. Return top k integers with highest frequencies in an array: https://leetcode.com/problems/top-k-frequent-elements/ b. Course schedule: https://leetcode.com/problems/course-schedule-ii/ Behavioural interview + one coding question a. Typical workplace behaviour questions b. Find the length of the longest sub-array whose sum is target System design: Design a Machine Learning app that makes recommendation to users for places to visit along their trip. Focus is no the ML pipeline: Data, Features, Evaluation, Model-building, Feedback, Online testing, Offline testing Personal Assessment: Didn't give good account of myself (first time system design). ML system design: Design E2E classification pipeline for Facebook marketplace. Users post visual (photos) + textual descriptions. Lots of focus on the Model training practice + Feature Engineering + Feedback loop, as well as emphasis on theory Personal Assessment: Way better than 4. Calibration (Behaviour + Coding): a. Behaviour similar to 3 b. Given a linked list L, a value val, and position pos, insert val at the posth position in L (Not replace).

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