Machine Learning Engineer Interview Questions

Review this list of 237 machine learning engineer interview questions and answers verified by hiring managers and candidates.
  • Meta (Facebook) logoAsked at Meta (Facebook) 
    +2

    "C : Okay. So I would want to start with knowing what is the product for which we have to build a recommendation system. I : This is a photo sharing product. C : Okay. So is this something on the lines of Instagram? I : Yes C : Okay. And are we a new product co or we have some current product built already? I : You can assume yourself. C : Okay. Is there any demography or country we are targeting? I : No, this is a global product C : Okay. So, the biggest goal of any product recommendation system"

    Kartikeya N. - "C : Okay. So I would want to start with knowing what is the product for which we have to build a recommendation system. I : This is a photo sharing product. C : Okay. So is this something on the lines of Instagram? I : Yes C : Okay. And are we a new product co or we have some current product built already? I : You can assume yourself. C : Okay. Is there any demography or country we are targeting? I : No, this is a global product C : Okay. So, the biggest goal of any product recommendation system"See full answer

    Machine Learning Engineer
    System Design
    +1 more
  • Google logoAsked at Google 
    Machine Learning Engineer
    System Design
    +1 more
  • " Compare alternate houses i.e for each house starting from the third, calculate the maximum money that can be stolen up to that house by choosing between: Skipping the current house and taking the maximum money stolen up to the previous house. Robbing the current house and adding its value to the maximum money stolen up to the house two steps back. package main import ( "fmt" ) // rob function calculates the maximum money a robber can steal func maxRob(nums []int) int { ln"

    VContaineers - " Compare alternate houses i.e for each house starting from the third, calculate the maximum money that can be stolen up to that house by choosing between: Skipping the current house and taking the maximum money stolen up to the previous house. Robbing the current house and adding its value to the maximum money stolen up to the house two steps back. package main import ( "fmt" ) // rob function calculates the maximum money a robber can steal func maxRob(nums []int) int { ln"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • Machine Learning Engineer
    Technical
    +3 more
  • Machine Learning Engineer
    Technical
    +4 more
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  • DoorDash logoAsked at DoorDash 

    "Prompt: We work for an online shopping website. Our team wants to consider offering discounts (e.g. 10% off your next purchase) to customers to incentivize them to make purchases. How would you design a system that decides how to offer these incentives? Answer Goals: Increase customer engagement while controlling costs. Specifically, we want the increase in revenue per customer per week of customers that receive the discount to be greater than the cost of the discount. Metrics: Revenue per cu"

    Michael F. - "Prompt: We work for an online shopping website. Our team wants to consider offering discounts (e.g. 10% off your next purchase) to customers to incentivize them to make purchases. How would you design a system that decides how to offer these incentives? Answer Goals: Increase customer engagement while controlling costs. Specifically, we want the increase in revenue per customer per week of customers that receive the discount to be greater than the cost of the discount. Metrics: Revenue per cu"See full answer

    Machine Learning Engineer
    System Design
  • Visa logoAsked at Visa 

    "I generally struggle with stakeholders and partners who doesn't communicate enough. Now it could be either they don't invest sufficient time and energy in doing so or at times they lack the skill sets to do so. In both the cases, the entire responsibility fell on the other person to dig deep into why someone is doing the way they are doing, reading into patterns and behaviour of their personality and adapting to those communication styles"

    Lati K. - "I generally struggle with stakeholders and partners who doesn't communicate enough. Now it could be either they don't invest sufficient time and energy in doing so or at times they lack the skill sets to do so. In both the cases, the entire responsibility fell on the other person to dig deep into why someone is doing the way they are doing, reading into patterns and behaviour of their personality and adapting to those communication styles"See full answer

    Machine Learning Engineer
    Behavioral
    +2 more
  • Adobe logoAsked at Adobe 
    +17

    "We can use dictionary to store cache items so that our read / write operations will be O(1). Each time we read or update an existing record, we have to ensure the item is moved to the back of the cache. This will allow us to evict the first item in the cache whenever the cache is full and we need to add new records also making our eviction O(1) Instead of normal dictionary, we will use ordered dictionary to store cache items. This will allow us to efficiently move items to back of the cache a"

    Alfred O. - "We can use dictionary to store cache items so that our read / write operations will be O(1). Each time we read or update an existing record, we have to ensure the item is moved to the back of the cache. This will allow us to evict the first item in the cache whenever the cache is full and we need to add new records also making our eviction O(1) Instead of normal dictionary, we will use ordered dictionary to store cache items. This will allow us to efficiently move items to back of the cache a"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +6 more
  • Asana logoAsked at Asana 
    +1

    "A good answer is describing an experience where you either proposed something that was selected after discussion or an alternate approach was taken, or you disagreed with a decision and argued for something else, either successfully or not. A good answer would be you had cogent arguments, the decision went another way for some reason, and you then fully backed the decision (agree to disagree is another way of stating it). You did not take it personally and you fully committed to the deci"

    Mrinalini R. - "A good answer is describing an experience where you either proposed something that was selected after discussion or an alternate approach was taken, or you disagreed with a decision and argued for something else, either successfully or not. A good answer would be you had cogent arguments, the decision went another way for some reason, and you then fully backed the decision (agree to disagree is another way of stating it). You did not take it personally and you fully committed to the deci"See full answer

    Machine Learning Engineer
    Behavioral
    +2 more
  • Atlassian logoAsked at Atlassian 

    "The interviewer hinted that a two-tower recommender system might be a suitable approach, using user history to embed users and pages separately and train on view or interaction data. Instead, I proposed a different approach that I felt was more aligned with how knowledge is structured in Confluence: I designed a system using a graph database to model the relationships between Confluence pages. Each page is a node, and edges represent content-based references. For example, when one article"

    Clayton P. - "The interviewer hinted that a two-tower recommender system might be a suitable approach, using user history to embed users and pages separately and train on view or interaction data. Instead, I proposed a different approach that I felt was more aligned with how knowledge is structured in Confluence: I designed a system using a graph database to model the relationships between Confluence pages. Each page is a node, and edges represent content-based references. For example, when one article"See full answer

    Machine Learning Engineer
    System Design
    +2 more
  • Amazon logoAsked at Amazon 
    +6

    "As a PM i received a feedback from my program manager on my style of verbal communication. It is about me speaking faster when i wanted to get away with a topic that i wasn't confident (may be not backed up with data, or still in process of getting detailed insight of a problem etc.). Whereas when I'm confident I tend to speak slowly or more assertively that made people to follow easily. I welcomed that feedback so from then on when I'm not confident in a topic I became more assertive to let pe"

    Rajesh V. - "As a PM i received a feedback from my program manager on my style of verbal communication. It is about me speaking faster when i wanted to get away with a topic that i wasn't confident (may be not backed up with data, or still in process of getting detailed insight of a problem etc.). Whereas when I'm confident I tend to speak slowly or more assertively that made people to follow easily. I welcomed that feedback so from then on when I'm not confident in a topic I became more assertive to let pe"See full answer

    Machine Learning Engineer
    Behavioral
    +7 more
  • Google logoAsked at Google 

    "App I Don’t Like: Electrify America What Electrify America Is Supposed to Do Electrify America is an app designed to help electric vehicle (EV) owners locate, access, and pay for fast-charging stations across the U.S. The app provides real-time station availability, allows users to initiate and monitor charging sessions, and offers membership plans for discounted rates. Ideally, it should enable a seamless charging experience, especially for long-distance travelers relying on i"

    fuzzyicecream14 - "App I Don’t Like: Electrify America What Electrify America Is Supposed to Do Electrify America is an app designed to help electric vehicle (EV) owners locate, access, and pay for fast-charging stations across the U.S. The app provides real-time station availability, allows users to initiate and monitor charging sessions, and offers membership plans for discounted rates. Ideally, it should enable a seamless charging experience, especially for long-distance travelers relying on i"See full answer

    Machine Learning Engineer
    Product Design
    +1 more
  • +10

    "Would be better to adjust resolution in the video player directly."

    Anonymous Prawn - "Would be better to adjust resolution in the video player directly."See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • Meta (Facebook) logoAsked at Meta (Facebook) 

    "At a high level, the core challenge here revolves around building an effective recommendation algorithm for news. News is an inherently diverse category, spanning various topics and catering to a wide array of user types and personas, such as adults, business professionals, general readers, or specific cohorts with unique interests. Consequently, developing a single, one-size-fits-all recommendation algorithm is not feasible. To enhance the personalization of the news recommendation algorithm,"

    Sai vuppalapati M. - "At a high level, the core challenge here revolves around building an effective recommendation algorithm for news. News is an inherently diverse category, spanning various topics and catering to a wide array of user types and personas, such as adults, business professionals, general readers, or specific cohorts with unique interests. Consequently, developing a single, one-size-fits-all recommendation algorithm is not feasible. To enhance the personalization of the news recommendation algorithm,"See full answer

    Machine Learning Engineer
    System Design
    +1 more
  • +2

    "public class Solution { public string MinRemoveToMakeValid(string s) { var dict = new Dictionary(); dict.Add('(', 0); dict.Add(')', 0); StringBuilder result = new StringBuilder(); for (int i = 0; i = dict[')']+1) { result.Append(c); dict[c]++; } } else result.Append(c); } StringBuilder answer = new StringBuilder(); dict.Clear(); dict.Add('(', 0); dict.Add(')', 0); for ("

    Murat S. - "public class Solution { public string MinRemoveToMakeValid(string s) { var dict = new Dictionary(); dict.Add('(', 0); dict.Add(')', 0); StringBuilder result = new StringBuilder(); for (int i = 0; i = dict[')']+1) { result.Append(c); dict[c]++; } } else result.Append(c); } StringBuilder answer = new StringBuilder(); dict.Clear(); dict.Add('(', 0); dict.Add(')', 0); for ("See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +2 more
  • OpenAI logoAsked at OpenAI 
    Video answer for 'How is gradient descent and model optimization used in linear regression?'

    "Gradient Descent is an optimisation strategy used in several supervised learning models. It is the technique for finding the optimum solution of an objective function. Typically, for a linear regression use case, it is used to find the weights and bias that produce the lowest loss. It involves computing the partial derivative of the objective function with respect to the weight and bias vectors. To find the optima of the function, the derivative is equated to 0, and iteratively the weight and b"

    Megha V. - "Gradient Descent is an optimisation strategy used in several supervised learning models. It is the technique for finding the optimum solution of an objective function. Typically, for a linear regression use case, it is used to find the weights and bias that produce the lowest loss. It involves computing the partial derivative of the objective function with respect to the weight and bias vectors. To find the optima of the function, the derivative is equated to 0, and iteratively the weight and b"See full answer

    Machine Learning Engineer
    Concept
    +1 more
  • Adobe logoAsked at Adobe 

    "Use a representative of each, e.g. sort the string and add it to the value of a hashmap> where we put all the words that belong to the same anagram together."

    Gaston B. - "Use a representative of each, e.g. sort the string and add it to the value of a hashmap> where we put all the words that belong to the same anagram together."See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • Apple logoAsked at Apple 
    +16

    "we can use two pointer + set like maintain i,j and also insert jth character to set like while set size is equal to our window j-i+1 then maximize our answer and increase jth pointer till last index"

    Kishor J. - "we can use two pointer + set like maintain i,j and also insert jth character to set like while set size is equal to our window j-i+1 then maximize our answer and increase jth pointer till last index"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • Machine Learning Engineer
    Technical
    +2 more
  • Meta (Facebook) logoAsked at Meta (Facebook) 

    "I want to work at Meta because of its reputation as a company that consistently pushes the boundaries of technology, particularly in areas like AI, machine learning, and immersive technologies such as AR and VR. I admire Meta's mission to bring people closer together and create meaningful connections, as well as its focus on long-term innovation, such as the development of the metaverse. As an AI engineer, I'm excited about the opportunity to work on cutting-edge projects that have a global impa"

    Alan T. - "I want to work at Meta because of its reputation as a company that consistently pushes the boundaries of technology, particularly in areas like AI, machine learning, and immersive technologies such as AR and VR. I admire Meta's mission to bring people closer together and create meaningful connections, as well as its focus on long-term innovation, such as the development of the metaverse. As an AI engineer, I'm excited about the opportunity to work on cutting-edge projects that have a global impa"See full answer

    Machine Learning Engineer
    Behavioral
    +1 more
Showing 21-40 of 237