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Roblox

Machine Learning Engineer

I went through three interview rounds for a Machine Learning Engineer role at Roblox. It was for a full-time position for a recent graduate. HR Screen The recruiter introduced the team and discussed the interview schedule. Coding and ML/LLM Technical This round involved a coding exercise and technical s, primarily focused on Machine Learning and Large Language Models (LLMs). The coding was about creating a scheduling system: given a list of games, output a list where no two adjacent games are the same. I worked through this with a friendly interviewer and we resolved it within the allotted time. The remaining 20 minutes were dedicated to ML and Transformer s. * ML s: I was asked to discuss classification and regression algorithms, including their pros and cons. * LLM s: I was asked about the Transformer architecture and the attention process, as well as how to optimize attention. Two 1-hour Interviews * First Interview (ML Modeling/LLM Deep Dive): Although the preview mentioned ML modeling, this interview focused entirely on in-depth LLM technical s. We covered: * Differences between GPT and BERT pre-training, with detailed explanations of their structures. * Self-attention structure and optimization techniques. * Distributed training methods, specifics of Tensor Parallelism (TP), its shortcomings, and optimization for Pipeline Parallelism (PP). * Various types of Positional Encoding (PE) and their respective pros and cons. The scope was broad and s were detailed, indicating the interviewer had a strong understanding of LLM underlying technologies. * Second Interview (Work Experience Deep Dive): This involved a deep dive into my previous work. I presented using my meeting slides for an hour, answering s as I went. This session also concluded quickly.

Palo Alto Networks

Security Researcher

The role I interviewed for was Sr. Staff Security Researcher. The overall process was smooth and quick. I applied through an internal referral and also directly via LinkedIn. Initially, I applied for two positions. My timeline was as follows: - D+0: Saw job opening on LinkedIn, used referral + online application. - D+1: Reached out to the overall group manager on LinkedIn. - D+3: Received an interview notification email and scheduled the first interview for Job 1. - D+4: HR call for Job 2. Discussed my background and scheduled subsequent interviews. - D+5: Job 2 preliminary interview with the team lead. This involved a deep dive into my resume and behavioral s focusing on my research approach and mindset. Given my PhD background and the research nature of the role, the focus was primarily on my past projects. - D+8: Job 1 preliminary interview with a Principal Researcher. Similar to the Job 2 interview but with a deeper dive. I was asked a trace analysis at the end. - D+9: HR informed me that Job 1 and Job 2 interview processes would be merged and scheduled the Virtual Onsite (VO) interviews. Virtual Onsite (VO) Interviews: - (VO - Programming & System Security): Interviewed with another Principal Researcher. The role is security-focused, so it wasn't a typical LeetCode problem. I was given two runnable programs and asked how I would ensure system stability while running them. A significant issue arose because while my background is in C++ for network security, the job description listed Java, C++, and Python. I was forced to use Python, a language I had forgotten the syntax for, as my C++ compiler had issues. We eventually shifted to a discussion-based approach where I outlined my analysis steps and wrote test cases for identified edge cases. The interviewer guided me smoothly, and although I eventually answered everything, I'm unsure how this affected the outcome. - (VO - Behavioral & Research Methodology): This interview was with a recent hire (less than a year in the role) who had a similar background to me. It was a relaxed conversation, mainly focusing on my research methodology and mindset. We even chatted about the food in the cafeteria. It felt like a good conversation. Overall, the interview process was fluid and fast-paced. Despite the Python programming challenge, I felt the discussions went well. Today (D+12), I messaged the recruiter for feedback, but haven't heard back. I suspect the VO programming round might have had a significant impact, and I might not have passed.

Pinterest

Machine Learning Engineer

The interview was for an ML Platform role. The interviewer asked a coding . The task was to generate all possible combinations from a given dictionary of parameters. The input format was a dictionary where keys were parameter names and values were lists of possible settings for that parameter. The output should be all unique combinations of these parameters. Example input: ``` parameters = { "learning_rate": [0.1, 0.2, 0.3], "feature": ["A", "B", "C"], "batch": [10, 20, 30], "depth": [100, 200, 300], } ``` Example output: ``` {'learning_rate': 0.1, 'feature': 'A', 'batch': 10, 'depth': 100} {'learning_rate': 0.1, 'feature': 'A', 'batch': 10, 'depth': 200} {'learning_rate': 0.1, 'feature': 'A', 'batch': 10, 'depth': 300} ... {'learning_rate': 0.3, 'feature': 'C', 'batch': 30, 'depth': 300} ``` The was not considered difficult.

Lam Research

Quality Engineer

The interview process for the Quality Engineer role at Lam Research spanned over four months. After receiving an invitation on January 10th, the process concluded with an offer on May 13th. The first interview was a phone screen, and after a significant delay due to travel restrictions, subsequent interviews were conducted in March and May. (Phone Screen): - Discussed my background related to Quality Engineering (QE) and my understanding of the QE role. - Addressed common s such as how I handle conflicts with teammates, my knowledge of Lam Research, and reasons for wanting to join the company. - Inquired about my preferred work style: whether I prefer tasks assigned to me or if I like to work more independently. - The interview lasted approximately one hour. The manager also provided details about the responsibilities of the position. Rounds 2 & 3 (Phone Screens): I felt these interviews were somewhat redundant, possibly due to uncertainty about the lockdown situation. The s were similar to the first round. I made an effort to use different examples than I had in the first interview. (Video Interview): This final round took place in May. Due to everyone wearing masks, it was difficult to read facial expressions, but I perceived a positive atmosphere as the interviewers seemed to be smiling and laughing. - s were similar to the first round, with a greater emphasis on logical reasoning and analytical skills. - The role's potential challenges were reiterated, and my interest in the work was reconfirmed. Following the fourth round on a Friday, I received a positive email from the manager on Saturday and the official offer the following Thursday. Key Takeaways from the Experience: - Prepare multiple examples for behavioral s to avoid repetition. - Research the background of the interviewers; one director had recently transitioned from a competitor, which led to an interesting discussion when asked what s I had for them. - Be conversational and aim to turn the Q&A into a discussion to foster better communication.

Apple

Software Engineer

I interviewed for an SDE Intern position. The process consisted of resume screening, an online test, a technical round, and a techno-managerial round. Online Test The coding was the leetcode Jump Game problem. I used a greedy approach, tracking the farthest reachable index as I iterated. The MCQs covered DBMS, OS, OOP, and DSA. Technical Interview I implemented Merge Sort, walked through the time complexity, and discussed edge cases. I also performed time complexity analysis on given code snippets and wrote one SQL query with joins and GROUP BY, then explained the output. I answered detailed s about my resume projects, including design decisions and trade-offs. I was also asked behavioral s about handling team conflict and my learning approach. Techno-Managerial Interview The manager started with pair coding. The problem was N-Queens. I recognized it, stated the approach, and coded a working backtracking solution in 25 minutes. After coding, I answered behavioral s about handling conflict with teammates, the biggest challenge in my projects, and the impact of my projects. There were also a few unexpected s about my college, seemingly to test my composure.

Design Dropbox

Dropbox is a cloud storage and collaboration platform that lets users store, sync, and share files across devices. It is commonly used by individuals and teams to organize documents, collaborate on projects, and securely access files from anywhere. Over time, Dropbox has expanded beyond file storage into productivity tools like document workflows, e-signatures, and team collaboration features.

Design Google Doc

Google Docs is an online document editor from Google, similar to Microsoft Word but cloud-based. It lets users create, edit, share, and collaborate on documents in real time. Since files are saved in Google Drive, people can access them from different devices and work together through comments, suggestions, and live editing.

Design Tinder

Tinder is a popular dating app where people create profiles with photos, a short bio, and basic personal details. Users swipe right if they are interested in someone and swipe left if they are not; when two people both swipe right, they “match” and can start chatting. It is commonly used for dating, casual conversations, and meeting new people nearby.

Design Google News

Google News is a news aggregation product from Google that collects news articles from many publishers and organizes them by topic, source, location, and user interest. Instead of writing its own news, Google News crawls and indexes articles from news websites, then ranks and groups related stories so users can quickly see what is happening across different sources.

Design Web Crawler

A web crawler is a program that automatically visits web pages, reads their content, and follows links to discover more pages. Search engines like Google use web crawlers to scan the web, collect page data, and build an index so users can search for information later.

A library has arranged books on a shelf using a permutation order of length n, where every integer from 1 to n appears exactly once. For each value x from 1 to n, determine whether all books labeled 1 through x appear within one contiguous segment of the shelf. Return a binary string of length n where the xth character is: '1' if the books {1, 2, ..., x} occupy a continuous block somewhere in the permutation '0' otherwise Implement the function findCoveredPrefixes(List[int] order) which returns the binary string described above. ---

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A storage room is represented as a grid. Each cell may be empty, contain a healthy item, or contain an infected item. Every minute, infection spreads from infected items to healthy items that are directly next to them in one of four directions: up down left right Diagonal cells are not affected. You are given: room: a 2D grid where: 0 means empty space 1 means healthy item 2 means infected item Return the minimum number of minutes needed until no healthy item remains. If some healthy item can never be infected, return -1. If there are no healthy items at the beginning, return 0. --- Implement the Solution class: int minutesToInfectAll(List> room) returns the minimum number of minutes needed for infection to spread to all reachable healthy items. --- Constraints: 1 ≤ room.length ≤ 10 1 ≤ room[i].length ≤ 10 room[i][j] is 0, 1, or 2 ---

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A company records employee shift activity using a list of log entries. Each log is a 3-element string array containing: employeename an action: "start" or "finish" a timestamp Rules: "start" means that employee begins using the shared workstation "finish" means that employee stops using the workstation The workstation can only be used by one employee at a time Shifts may be nested, and when one employee finishes, the workstation resumes the most recently paused employee Your task is to compute the total active workstation time for each employee. Return the result as a list of strings in the format: "employeename: total_time" sorted alphabetically by employee names.

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You are working on a deployment monitoring system that tracks feature rollouts across a sequence of releases. Each release is identified by a version string in the format: The releases are provided in strictly increasing order. At some unknown point, a feature was enabled. From that release onward, all future releases also have the feature enabled. You are given access to an external API that can check whether a given release includes the feature. --- External API Returns true if the feature is enabled in that version Returns false otherwise --- Requirements Implement the DeploymentAnalyzer class. Initializes the analyzer with the provided API. Returns the earliest version where the feature is enabled You must minimize the number of calls to hasFeature --- Constraints 1 ≤ releases.length ≤ 10^5 Versions are sorted in ascending order At least one version has the feature enabled Each version string is valid and contains no leading zeros ---

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Implement a simplified in-memory inventory ledger that stores item profiles. Each profile is identified by a unique string itemId. A profile can contain multiple attribute-value pairs, where both the attribute and value are strings. --- Implement the InventoryLedger class: void putAttribute(String itemId, String attribute, String value) Inserts the attribute-value pair into the profile associated with itemId If the attribute already exists in that profile, overwrite its value If the profile does not exist, create it first String readAttribute(String itemId, String attribute) Returns the value stored for attribute in the profile identified by itemId If the profile or attribute does not exist, return "" boolean removeAttribute(String itemId, String attribute) Removes the attribute from the profile identified by itemId Returns true if the attribute was removed successfully Returns false if the profile or attribute does not exist If removing the attribute leaves the profile empty, remove the entire profile from the system ---

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