Machine Learning Engineer Interview Questions

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

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

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

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

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

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

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

Kanishvaran P. - "class Solution { public boolean isValid(String s) { // Time Complexity and Space complexity will be O(n) Stack stack=new Stack(); for(char c:s.toCharArray()){ if(c=='('){ stack.push(')'); } else if(c=='{'){ stack.push('}'); } else if(c=='['){ stack.push(']'); } else if(stack.pop()!=c){ return false; } } return stack.isEmpty(); } }"See full answer

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

Tiago R. - "function isValid(s) { const stack = []; for (let i=0; i < s.length; i++) { const char = s.charAt(i); if (['(', '{', '['].includes(char)) { stack.push(char); } else { const top = stack.pop(); if ((char === ')' && top !== '(') || (char === '}' && top !== '{') || (char === ']' && top !== '[')) { return false; } } } return stack.length === 0"See full answer

Louie Z. - "DNNs can learn hierarchical features, with each layer learning progressively more abstract features, and generalizes better. SNNs are better for simplier problems involving smaller datasets and if low latency is required."See full answer

Avon T. - "Initialize left pointer: Set a left pointer left to 0. Iterate through the array: Iterate through the array from left to right. If the current element is not 0, swap it with the element at the left pointer and increment left. Time complexity: O(n). The loop iterates through the entire array once, making it linear time. Space complexity: O(1). The algorithm operates in-place, modifying the input array directly without using additional data structures. "See full answer

Srini K. - "performance issues and sudden spikes on input requests by scaling techniques and optimization."See full answer

Gabriele G. - "DFS with check of an already seen node in the graph would work from collections import deque, defaultdict from typing import List def iscourseloopdfs(idcourse: int, graph: defaultdict[list]) -> bool: stack = deque([(id_course)]) seen_courses = set() while stack: print(stack) curr_course = stack.pop() if currcourse in seencourses: return True seencourses.add(currcourse) for dependency in graph[curr_course]: "See full answer

Scott S. - " The Situation A few months ago, our trading platform started experiencing significant latency issues during peak trading hours. This latency was affecting our ability to process real-time market data and execute trades efficiently, potentially leading to substantial financial losses and missed opportunities. Identifying the Problem The first step was to identify the root cause of the latency. I organized a team meeting with our data engineers, DevOps, and network specialists to gather"See full answer