deep discussion on project, easy to schedule and professional. the interviewer was kind and patient, had a good experience
Machine Learning Software Engineer Interview Questions
8,249 machine learning software engineer interview questions shared by candidates
In ML fundamentals they asked to design a small LLM that could run on a phone while making sure its polite. In ML system design they asked to design a system to detected copyright violations on youtube
A start-up owner is looking to meet new investors to get some funds for the company. Each investor has a tight schedule that the owner has to respect. Given the schedules of the days investors are available, determine how many meetings the owner can schedule. Note that the owner can only have one meeting per day. The schedules consist of two integer arrays: firstDay, and lastDay, aligned by index. Each element in the array firstDay represents the first day an investor is available, and each element in lastDay represents the last day an investor is available, both inclusive. Example: firstDay = [1,2,3,3,3] lastDay= [2,2,3,4,4] • There are 5 investors [I-0, 1-1, I-2, I-3, 1-4] • The investor I-0 is available from day 1 to day 2 inclusive [1, 2] • The investor I-1 is available in day 2 only [2, 2]. The investor 1-2 is available in day 3 only [3, 3] • The investors I-3 and I-4 are available from day 3 to day 4 only [3, 4] • The owner can only meet 4 investors out of 5: I-0 in day 1, 1-1 in day 2, 1-2 in day 3 and I-3 in day 4. Function Description Complete the function countMeetings in the editor below. countMeetings has the following parameters: int firstDayIn]: firstDaytil is the first day the ith investor is available to meet. int lastDayIn]: lastDayli] is the last day the ith investor is available to meet. Returns: int: the maximum number of meetings possible Constraints • 1 ≤ n ≤ 105 • 1 s firstDayli], lastDayli] ≤ 105 (where 0 ≤ i < n) • firstDayli] ≤ lastDayli] (where 0 ≤ i < n)
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what's the problem, how you design solutions, how you choose solutions, what's the outcome, what's the take away.
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take home machine learning challenge
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