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Machine Learning Engineer Interview Questions

Review this list of 292 Machine Learning Engineer interview questions and answers verified by hiring managers and candidates.
  • Anthropic logoAsked at Anthropic 
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    Machine Learning Engineer
    System Design
    +1 more
  • Adobe logoAsked at Adobe 
    4 answers
    +1

    " 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
  • Anthropic logoAsked at Anthropic 
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    Machine Learning Engineer
    Artificial Intelligence
    +4 more
  • Anthropic logoAsked at Anthropic 
    4 answers
    +1

    "Hallucinations are evaluated by measuring how often generated outputs contain information that is not supported by trusted sources. what hallucination means in context: Intrinsic hallucination: contradicts provided context Extrinsic hallucination: introduces unsupported facts Fabrication: confidently incorrect answers"

    Hardik saurabh G. - "Hallucinations are evaluated by measuring how often generated outputs contain information that is not supported by trusted sources. what hallucination means in context: Intrinsic hallucination: contradicts provided context Extrinsic hallucination: introduces unsupported facts Fabrication: confidently incorrect answers"See full answer

    Machine Learning Engineer
    Artificial Intelligence
    +4 more
  • Amazon logoAsked at Amazon 
    4 answers
    +1

    "Situation - A time I dealt with conflict while on a team was while I was working at Shopify on physical and digital gift card refund point of sale solutions. The situation was that we were dealing with complex technical constraints including not changing particular UI components behavior to act as they should be intended. On the refund screen, the existing design was using a toggle on the same screen to bring up a modal for gift card selection to either select digital or physical options. Thi"

    Ben G. - "Situation - A time I dealt with conflict while on a team was while I was working at Shopify on physical and digital gift card refund point of sale solutions. The situation was that we were dealing with complex technical constraints including not changing particular UI components behavior to act as they should be intended. On the refund screen, the existing design was using a toggle on the same screen to bring up a modal for gift card selection to either select digital or physical options. Thi"See full answer

    Machine Learning Engineer
    Behavioral
    +5 more
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  • Visa logoAsked at Visa 
    3 answers

    "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 
    1 answer

    "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
  • Anthropic logoAsked at Anthropic 
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    Machine Learning Engineer
    Artificial Intelligence
    +5 more
  • Reddit logoAsked at Reddit 
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    Machine Learning Engineer
    Concept
    +1 more
  • Adobe logoAsked at Adobe 
    34 answers
    +28

    "Idea for solution: Reverse the complete char array Reverse the words separated by space. i.e. Find the space characters and the reverse the subarray between two space characters. vector reverseSubarray(vector& arr, int s, int e) { while (s reverseWords(vector& arr ) { int n = arr.size(); reverse(arr, 0, n - 1"

    Rahul M. - "Idea for solution: Reverse the complete char array Reverse the words separated by space. i.e. Find the space characters and the reverse the subarray between two space characters. vector reverseSubarray(vector& arr, int s, int e) { while (s reverseWords(vector& arr ) { int n = arr.size(); reverse(arr, 0, n - 1"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • Sierra AI logoAsked at Sierra AI 
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    Machine Learning Engineer
    Data Structures & Algorithms
    +2 more
  • Amazon logoAsked at Amazon 
    8 answers
    +5

    "t"

    Srikhar S. - "t"See full answer

    Machine Learning Engineer
    Behavioral
    +4 more
  • Amazon logoAsked at Amazon 
    56 answers
    Video answer for 'Merge Intervals'
    +48

    "const mergeIntervals = (intervals) => { const compare = (a, b) => { if(a[0] b[0]) return 1 else if(a[0] === b[0]) { return a[1] - b[1] } } let current = [] const result = [] const sorted = intervals.sort(compare) for(let i = 0; i = b[0]) current[1] = b[1] els"

    Kofi N. - "const mergeIntervals = (intervals) => { const compare = (a, b) => { if(a[0] b[0]) return 1 else if(a[0] === b[0]) { return a[1] - b[1] } } let current = [] const result = [] const sorted = intervals.sort(compare) for(let i = 0; i = b[0]) current[1] = b[1] els"See full answer

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

    "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
    Machine Learning
    +1 more
  • Meta logoAsked at Meta 
    4 answers

    "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
    +5 more
  • OpenAI logoAsked at OpenAI 
    3 answers
    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
  • Google logoAsked at Google 
    7 answers
    +4

    "The company culture is very supportive and collaborative. Googlers are encouraged to be creative and innovative, and there is a lot of freedom to explore new ideas. The work is challenging and rewarding. Googlers have the opportunity to work on cutting-edge projects that have a real impact on the world. The company is committed to diversity and inclusion. Google is a great place to work for people from all backgrounds and with all different perspectives. I am confident that I would b"

    Praful B. - "The company culture is very supportive and collaborative. Googlers are encouraged to be creative and innovative, and there is a lot of freedom to explore new ideas. The work is challenging and rewarding. Googlers have the opportunity to work on cutting-edge projects that have a real impact on the world. The company is committed to diversity and inclusion. Google is a great place to work for people from all backgrounds and with all different perspectives. I am confident that I would b"See full answer

    Machine Learning Engineer
    Behavioral
    +3 more
  • DoorDash logoAsked at DoorDash 
    2 answers

    "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
  • Anthropic logoAsked at Anthropic 
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    Machine Learning Engineer
    Artificial Intelligence
    +5 more
  • Nvidia logoAsked at Nvidia 
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    Machine Learning Engineer
    Concept
    +1 more
Showing 41-60 of 292