Machine Learning Engineer sollicitatievragen
Bedrijven vertrouwen op machine learning engineers voor het ontwerpen en verbeteren van systemen waarmee hun software zichzelf kan verbeteren, in plaats van dat deze specifiek wordt geprogrammeerd. Tijdens een sollicitatiegesprek kunt u flink worden getest op uw kennis van computerwetenschappen en gegevenswetenschappen, met de nadruk op het herkennen van patronen en trends. Een bachelor in computerwetenschappen of een gerelateerd vakgebied is vereist.
Meest gestelde sollicitatievragen voor een machine learning engineer (M/V/X) en hoe te antwoorden
Tips om deze drie veelgestelde sollicitatievragen voor een machine learning engineer te beantwoorden:
Vraag 1: Wat zijn de belangrijkste algoritmen, programmeertermen en theorieën die u moet begrijpen als machine learning engineer?
Zo antwoordt u: Bereid u voor op het praten over zaken als Type I- en Type II-fouten, supervised en unsupervised machine learning, ROC-curves en andere belangrijke onderdelen van machine learning. Werkgevers willen weten of u veel kennis hebt van de technische aspecten van de functie.
Vraag 2: Hoe zou u machine learning uitleggen aan iemand die het niet begrijpt?
Zo antwoordt u: Soms moeten machine learning engineers werken met mensen die niet bekend zijn met de technische aspecten van de functie. Gebruik deze sollicitatievraag als een kans om uw grote kennis van de functie en uw communicatieve vaardigheden te laten zien.
Vraag 3: Hoe blijft u op de hoogte van het laatste nieuws en trends in machine learning?
Zo antwoordt u: Door te vertellen over hoe u op de hoogte blijft van het laatste nieuws en trends in machine learning laat u een werkgever zien dat u betrokken bent bij de branche, een vaardig onderzoeker bent en over zelfmotivatie beschikt.
Meest gestelde sollicitatievragen
Suppose you have a matrix of numbers. How can you easily compute the sum of any rectangle (i.e. a range [row_start, row_end, col_start, col_end]) of those numbers? How would you code this?
7 antwoorden↳
Compute the sum of the rectangles, for all i,j, bounded by (i,j), (i,m), (n,j), (n,m), where (n,m) is the size of the matrix M. Call that sum s(i,j). You can calculate s(i,j) by dynamic programming: s(i,j) = M(i,j) + s(i+1,j) + s(i,j+1) - s(i+1,j+1). And the sum of any rectangle can be computed from s(i,j). Minder
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Awesome!!
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The answer is already popular in computer vision fields!! It is called integral imaging. See this page http://en.wikipedia.org/wiki/Haar-like_features Minder
Have you ever had your code formally verified?
6 antwoorden↳
What were the online coding questions like? Could you elaborate?
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Object detection. Is that what yours was?
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it is same as mine. Could you give me more details about the online coding? what algorithm did they test on object detection part? Minder
The three data structure questions are: 1. the difference between linked list and array; 2. the difference between stack and queue; 3. describe hash table.
4 antwoorden↳
Arrays are more efficient for accessing elements , while linked list are better for inserting or deleting elements, the choice between the two data structure depends on the specific requirements of the problem being solved. Minder
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Stack and queues have different order of processing, operations for adding and removing elements, and usage scenarios.The choice between the two data structure depends on the specific requirements of the problem being solved Minder
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A hash table is a data structure that allows for efficient insertion, deletion, and lookup of key-value pairs. It is based on the idea of hashing, which involves mapping each key to a specific index in an array using a hash function. The hash function takes a key as input and returns a unique index in the array. In order to handle collisions (when two or more keys map to the same index), some form of collision resolution mechanism is used, such as separate chaining or open addressing. In separate chaining, each index in the array is a linked list, and each key-value pair is stored in a node in the corresponding linked list. When a collision occurs, the new key-value pair is added to the end of the linked list at the corresponding index. In open addressing, when a collision occurs, a different index in the array is searched for to store the new key-value pair. There are several techniques for open addressing, such as linear probing, quadratic probing, and double hashing. Hash tables have an average case time complexity of O(1) for insertion, deletion, and lookup operations, making them a highly efficient data structure for many applications, such as database indexing, caching, and compiler symbol tables. However, their worst-case time complexity can be as bad as O(n) in rare cases, such as when there are many collisions and the hash table needs to be resized. Minder
What are some of the projects that you have done?
4 antwoorden↳
Do you mind to share what are the hard leetcode questions they asked during the interview? Minder
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I dont think it's fair to share which question they asked. But the exact same question is on leetcode and the difficulty level is hard. Minder
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What topic you are being ask from in leetcode? also did they ask you system design and CS fundamentals. Minder
Give an image, when we take 2 sub images from it, calculate the ratio similar to AnB/AuB.
4 antwoorden↳
Coded in python but wasn't able to finish it
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Can you elaborate on the question
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Given a matrix and coordinates of 2 rectangles calculate the weighted IoU in linear/constant time. Minder
If the gradient descent doesn't converge, what might be the problem?
3 antwoorden↳
Step size
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If learning rate is too high gradient descent will never converge
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Low learning rate with early stopping High learning rate diverges
how to sort in O(Logn) time
3 antwoorden↳
I don't think you can sort in O(logn) because you will need to go through the whole data at least once, making it O(n). Indeed, you can do it in O(logn) if the data is guarantee with some specific constrain or relationship. I think the best you can sort a completely random data is O(nlogn). Minder
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I didn't come up with the answer. it is not difficult, just not prepared
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what is the question
Design round: Design an api rate limiter Coding round: simple manipulation of arrays and maps Craft round: Design an ML Labelling system
3 antwoorden↳
There will be many documents in a document database. The labelling system must use machine learning to label into different categories. Eg help desk, system document, technical. There will a small train dataset available but not entirely reliable. Minder
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The correct answer would be to use a combination of weak learning methods and gradually incorporate feedback and make it stronger Minder
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APi rate limiter was really simple, just look at uber/ratelimit on git and thats it. Rest was farily easy Minder
1 question I had was next greatest element in an array - searching only to the right. I had a solution with O(n^2), but they said don't even bother, that's rejected
3 antwoorden↳
If you do it backwards, you actually just need to compare the last greatest value against the next element, so should be o(n) Minder
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Just use monotonic stack , it will help to get the next greatest element for every element of the array on O(n) with a space of o(n) Minder
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O(n^2) solution rejected, then tried reverse search, but ran out of time