Whiteboard Confidential

About Whiteboard Confidential

Technical interview replays and deep-dive commentary, with engineers from the world's best companies: Google, Meta, OpenAI, and many more

Publisher

interviewing.io

Category

technology

Language

en

Explicit

No

First episode

7 Aug 2025

Latest episode

5 Dec 2025

Latest episodes

11 of 15 episodes have a transcript.

1. 

   5 Dec 2025TRANSCRIPT

   Longest Consecutive Sequence: Go Interview with a FAANG Engineer

   A candidate works through three algorithmic problems in Go during a mock interview with a senior FAANG engineer. This mock touched on correctness, complexity tradeoffs, implementation clarity, and communication — the core signals real interviewers evaluate.🧩 Problem 1 — Longest Consecutive Sequence (Unsorted Array)Design an O(n) solution using a hash set to identify sequence starts and expand. Covers duplicate handling, boundary detection, and correctness reasoning without sorting.🧩 Problem 2 — Binary Tree Level Order TraversalClassic BFS over tree nodes, using a queue and level-by-level batching. Discussion includes space complexity, queue growth, and common implementation pitfalls.🧩 Problem 3 — Cutting Trees on a Cliff (Grid Stability / Connectivity)After removing one tree, determine which remaining trees are still connected to the bottom row. Involves multi-source BFS/DFS from stable nodes, component tracking, and correctness guarantees for 4-directional connectivity. Key skills demonstrated• Translating verbal reasoning into clean Go implementations• Maintaining communication while coding (a major FAANG evaluation axis)• Applying BFS/DFS patterns across trees and grids• Identifying optimal vs. suboptimal approaches via time/space analysis• Handling interviewer hints and recalibrating solutions• Ensuring correctness through example-driven verification👉 Book coaching or watch more mock interviews: https://www.interviewing.io📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-go-longest-consecutive-sequence🔗 Explore more FAANG interviews: https://www.interviewing.io/mocksDisclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

2. 

   18 Nov 2025TRANSCRIPT

   Binary Tree Views & Closest Pair Problem: Python Mock Interview with a Meta Engineer

   REPLAY EPISODE: A candidate takes on two Meta-style interview problems—one array, one binary tree—and a Senior Meta engineer breaks down their performance step by step.In this Meta-style mock coding interview, a candidate is put through two challenging problems that test practical reasoning, optimal data structures, and clear communication — exactly what real Meta interviewers look for.🧩 Problem 1: Given a sorted array of positive integers and a target k, find the pair whose sum is closest to k.(A classic Meta-style twist on two-pointer optimization problems.)🧩 Problem 2: You’re given the root of a binary tree. If one person stands on the left side and another on the right side, return the nodes visible to both of them — left view (bottom → top) followed by right view (top → bottom).(A unique combination of tree view and traversal-order reasoning.)Watch how the candidate breaks down both problems like in a real Meta onsite:✅ Identifies the optimal O(n) two-pointer strategy✅ Handles tricky edge cases with clean logic✅ Designs a combined traversal strategy for dual tree views✅ Codes efficiently under pressure✅ Gets detailed feedback from a Senior Meta engineer💡 What you’ll learn:• Two-pointer patterns Meta loves to ask• How left/right tree views work and how to combine them• How to structure reasoning during a high-pressure interview• What great communication looks like in a real FAANG interview👉 Sign up to book coaching or watch more mock interviews: https://www.interviewing.io📝 Interviewer feedback & transcript: https://interviewing.io/mocks/facebook-python-closest-pair-sum🔗 Explore more Meta interviews: https://www.interviewing.io/mocks?company=metaDisclaimer: All interviews are shared with explicit permission from the interviewer and the interviewee. All candidates remain anonymous.

3. 

   11 Nov 2025TRANSCRIPT

   Personalized Newsfeed system: ML System Design Interview with a Google Engineer

   REPLAY EPISODE: In this Google machine learning system design interview mock, a candidate tackles a personalized newsfeed recommendation system — the kind of large-scale ML challenge that real Google engineers face. 🧩 Problem: Design an ML system that ranks and recommends posts in a user’s feed by predicting engagement (likes, comments, shares) in real time. Watch how the candidate approaches it like a real interview: ✅ Clarifies goals, scope, and constraints for a production ML system ✅ Defines the ML objective and key features (user, content, interaction) ✅ Chooses and explains a two-tower deep learning architecture with multitask learning ✅ Discusses tradeoffs in retrieval, ranking, latency, and scalability 💡 What you’ll learn: • How to approach ML system design questions in Google interviews • How to connect engagement metrics to ML objectives • What a two-tower recommendation model looks like in production • How top candidates communicate complex ML ideas clearly 👉 Watch more interviews or book ML interview coaching: https://www.interviewing.io 📝 See the full transcript and interviewer feedback:https://interviewing.io/mocks/google-machine-learning-personalized-newsfeed-system 🔗 More Google interviews:https://interviewing.io/mocks?company=googleDisclaimer: All interviews are shared with explicit permission from both the interviewer and the interviewee, and all interviews are anonymous. interviewing.io has the sole right to distribute this content.

4. 

   3 Nov 2025TRANSCRIPT

   Delete Nodes and Return Forest: Python Interview with a Google Engineer

   REPLAY EPISODE: In this Google coding interview mock, a candidate tackles one of the trickiest binary tree problems that keeps showing up in real interviews.🧩 Problem: Given the root of a binary tree and a list of node values to delete, remove those nodes and return the roots of all remaining subtrees. (LeetCode #1110 – Delete Nodes and Return Forest)Watch how the candidate breaks it down from scratch:✅ Clarifies the problem and edge cases like a real Google interview✅ Designs an O(n) BFS solution (with clean logic)✅ Codes in Python and tests multiple tricky examples✅ Gets detailed interviewer feedback on problem-solving, communication, and structure💡 What you’ll learn:• How to think out loud in a Google interview• BFS vs DFS strategies for tree deletion problems• How to handle “delete and return forest”–type graph problems• What great communication looks like under pressure👉 Sign up to book coaching or to watch more interviews in our showcase: https://www.interviewing.io 🔗 Or view other Google interviews: https://interviewing.io/mocks?company=googleDisclaimer: All interviews are shared with explicit permission from the interviewer and the interviewee, and all interviews are anonymous. interviewing.io has the sole right to distribute this content.

5. 

   22 Oct 2025TRANSCRIPT

   From Layoff to OpenAI: Clarifying Questions, Improv, Better Interviews with James Maa

   SPOTLIGHT EPISODE: What happens when a senior engineer stops “faking it” and starts treating interviewers like partners? Eamonn sits down with James—now at OpenAI, with prior stops at Pinterest, Reddit, and Discord—to unpack how he managed imposter syndrome, learned to ask clarifying questions early, and used improv to become a clearer, more confident communicator on the job. Along the way, we pause to analyze James’s mock coding interview on interviewing.io, tracing the exact habits that set him apart: writing while thinking, checking in for buy-in, and iterating toward better solutions under pressure. If you’re preparing for coding interviews, you’ll see how James navigates anagrams with letter-count tuples, weighs time/space tradeoffs, and engages with hints without losing the thread. Watch James's Full Mock Here: https://start.interviewing.io/interview/Gw58xSkxBKqu/replayWatch Another of James's Full Mocks Here: https://www.youtube.com/watch?v=eHF6YxfOXy4 Watch replays & transcripts: https://start.interviewing.io/showcase Book a mock interview: https://interviewing.io Timestamps 00:00 Cold open — why “faking it” fails in interviews 01:00 James’s background and path to OpenAI 04:00 How improv rebuilt confidence and communication skills 07:00 Layoffs at Discord and finding upside in a reset 10:00 Structured prep: LeetCode, mocks, and recruiter advice 13:00 Why peer mocks matter more than grinding alone 16:00 Mock interview replay: the anagram challenge 24:00 Working through space vs. time tradeoffs under pressure 33:00 Staying unstuck: writing, asking, and engaging hints 58:57 Feedback clip: top-tier insight, senior-level signal Disclaimer: All interviews are shared with explicit permission from the interviewer and the interviewee, and all interviews are anonymous. interviewing.io has the sole right to distribute this content.

6. 

   9 Oct 2025TRANSCRIPT

   ​​Amazon L5 vs L6 Expectation: Behavioral Interview with a Staff-Level Amazon Engineer

   REPLAY EPISODE: Our candidate prepares for Amazon’s behavioral leadership principles interview. They practice answers around making high-stakes decisions without full data, handling disagreements with stakeholders, and demonstrating ownership during complex migrations. The interviewer pushes for depth and senior-level framing, highlighting where answers are closer to L5 vs. L6 expectations. The feedback section breaks down how to strengthen STAR responses, as well as how to provide scope and complexity at a senior engineer level Also discussed are strategies for clarifying ambiguous questions in the moment. This episode is vital for candidates targeting Amazon or other FAANG-level behavioral interviews where leadership principles are central to evaluation. Sign up to book coaching or to watch more interviews in our showcase: https://www.interviewing.io Or view other Amazon interviews: https://interviewing.io/mocks?company=amazon Disclaimer: All interviews are shared with explicit permission from the interviewer and the interviewee, and all interviews are anonymous. interviewing.io has the sole right to distribute this content.

7. 

   1 Oct 2025TRANSCRIPT

   Seat Allocation at Scale: Coding Interview with a Google Engineer (Python)

   REPLAY EPISODE: A candidate takes on their very first coding mock interview. The problem: designing a seat allocation algorithm for a cinema that can scale to billions of rows. Given a list of reserved seats, the task is to calculate how many groups of four people can still sit together, factoring in aisle breaks, adjacency rules, and edge cases. The interviewer, a Staff-level engineer with experience at Google and other top-tier Bay Area companies, guides the candidate to optimize their solution by leveraging sparsity, reducing space complexity, and thinking carefully about logical overlap between seating groups. Along the way, they also explore how to make the code cleaner, more modular, and easier to analyze. The feedback at the end breaks down what the candidate did well, what edge cases tripped them up, and how they can sharpen their real-time reasoning for future interviews. Sign up to book coaching or to watch more interviews in our showcase: https://www.interviewing.io (https://www.interviewing.io) See the interviewer’s feedback and transcript here: https://interviewing.io/mocks/google-python-seat-allocation-at-scale (https://interviewing.io/mocks/google-python-seat-allocation-at-scale) Or view other interviews: https://interviewing.io/mocks?company=google (https://interviewing.io/mocks?company=google) Disclaimer: All interviews are shared with explicit permission from the interviewer and the interviewee, and all interviews are anonymous. interviewing.io has the sole right to distribute this content.

8. 

   24 Sept 2025TRANSCRIPT

   Payment Pipeline Design at Netflix Scale: System Design Interview with a Netflix Engineer

   REPLAY EPISODE: A candidate takes on their very first system design mock interview. The problem: designing a pipeline that connects rights management, financial accounting, and payments at Netflix scale. They need to figure out how to combine movie rights data with royalty fees and ensure accurate payouts downstream. The interviewer, a seasoned Netflix engineer who has led dozens of design interviews, pushes the candidate to think through trade-offs like consistency vs. availability, synchronous APIs vs. event-driven systems, and how to handle failures across dependent services. The feedback at the end breaks down what the candidate did well, what could be stronger, and how to level up toward L6 readiness. Sign up to book coaching or to watch more interviews in our showcase: https://www.interviewing.io (https://www.interviewing.io) See the interviewer’s feedback and transcript here: https://interviewing.io/mocks/netflix-system-design-payment-pipeline (https://start.interviewing.io/showcase/8fyTV2y1TAph) Or view other Netflix interviews: https://interviewing.io/mocks?company=netflix (https://interviewing.io/mocks?company=netflix) Disclaimer: All interviews are shared with explicit permission from the interviewer and the interviewee, and all interviews are anonymous. interviewing.io has the sole right to distribute this content.

9. 

   17 Sept 2025TRANSCRIPT

   Design a Calendar for 100 Million Users: Senior/Staff System Design Interview with an Amazon Engineer

   REPLAY EPISODE: In this system design mock interview, the candidate is asked to design the event management component of a large-scale calendar system — including functionality for creating events, sending invitations, and sending notifications before meetings. With 12 years of experience, the candidate targets L5–L6 roles at top tech companies like Amazon and Meta. The interviewer, a seasoned SDE3 at Amazon, pushes the candidate on functional requirements, database design, and operational scalability, providing insightful feedback throughout. This is an excellent watch if you’re preparing for senior level and up system design interviews at FAANG companies. Curious how to handle trade-offs between availability and consistency, how to design relational schemas for user-event systems, or how to architect a cron-driven notification service? You’ll find answers here. Sign up to book coaching or to watch more interviews in our showcase: https://www.interviewing.io See the interviewer’s feedback and transcript here: https://interviewing.io/mocks/amazon-system-design-calendar-system Or view other Amazon interviews: https://interviewing.io/mocks?company=amazon (https://interviewing.io/mocks?company=amazon) Timestamps: 00:00 Intro 00:03:31 Problem begins – Event Management in a Calendar System 00:10:49 Scale calculations & database design 00:37:02 Notifications & system tradeoffs 00:48:09 Feedback and review Disclaimer: All interviews are shared with explicit permission from the interviewer and the interviewee, and all interviews are anonymous. interviewing.io has the sole right to distribute this content.

10. 

    16 Sept 2025TRANSCRIPT

    Whiteboard Confidential Trailer

    Whiteboard Confidential brings you unfiltered interview replays with engineers from top companies like Google, Meta, and OpenAI. Raw problem-solving and candid feedback straight from the hiring seat. Once a month, Spotlight episodes feature engineers who’ve survived the interview gauntlet as they share lessons learned and even react to their own mock interviews. Subscribe now so you never miss an episode!

11. 

    9 Sept 2025TRANSCRIPT

    Beating the Odds: Poker, Persistence, and a Meta Offer with Shawn Strausser (Senior MLE at Meta)

    SPOTLIGHT EPISODE: What happens when a physics PhD and former pro poker player decides to break into big tech? Drew sits down with Shawn Strausser to break down how he went from burnout to hired by Meta as a Senior MLE. You’ll hear the full story, from his initial struggles applying to 500+ jobs with no interviews, to the moment a spam-folder recruiting email from Meta changed everything. Along the way, we pause to reflect on Shawn’s live mock ML system design interview, highlighting key moments, strategies, and insights that helped him land a role as a machine learning engineer. If you’re preparing for system design or ML interviews, this one’s packed with gold. Watch Shawn’s full mock interview here: https://youtu.be/Q-g9nGDBUpY?si=pC896LpbRDRvPLNa Book your own mock interview: https://interviewing.io Connect with Shawn on LinkedIn: https://www.linkedin.com/in/shawn-strausser-93aa8620b/ Sign up to book coaching or to watch more interviews in our showcase: https://www.interviewing.io See the interviewer’s feedback and transcript here: https://start.interviewing.io/showcase/KQwiaFnBEwAL (https://start.interviewing.io/showcase/KQwiaFnBEwAL) Or view other Meta interviews: https://interviewing.io/mocks?company=meta (https://interviewing.io/mocks?company=meta) Disclaimer: All interviews are shared with explicit permission from the interviewer and the interviewee, and all interviews are anonymous. interviewing.io has the sole right to distribute this content. ⸻ Timestamps: 00:00 Introduction 00:24 Interview begins: Shawn’s background and path to Meta 19:39 Commentary: prepping to perform in interviews 23:39 Interview: mock interview begins 26:00 Commentary: feedback on outlining and preparation 26:35 Interview: data features and fraud detection 32:00 Commentary: fusion and feature discussion 32:21 Interview: early vs. late fusion 36:00 Commentary: bias and human labeling 37:00 Interview: modeling architecture and label bias 40:41 Commentary: real-world complexity of label bias 43:43 Interview: clarifying questions and best practices 48:03 Commentary: content fusion and meme example 50:41 Interview: pivotal interview moment 52:07 Commentary: rehearsal strategy 53:45 Interview: mindset, sleep, and sustainability 55:41 Final commentary and outro

12. 

    2 Sept 2025

    Solve Binary Tree Challenges Under Pressure: Coding Interview with a Meta Engineer

    REPLAY EPISODE: Our candidate is preparing for an upcoming Meta phone screen and takes on two tree-based problems under timed conditions. The first question involves finding the minimum depth of a binary tree, which the candidate solves confidently with both DFS and BFS approaches. The second, more challenging follow-up asks for the smallest subtree containing all the deepest nodes (a classic lowest common ancestor problem with a twist). Throughout the interview, the candidate demonstrates clear communication, strong algorithmic thinking, and a willingness to iterate and explore edge cases. The conversation includes valuable coaching moments around recursion, time/space trade-offs, and the importance of clarity under pressure. This is a valuable watch if you’re preparing for technical screens at top-tier companies, especially those that emphasize tree-based problems and recursive reasoning. Sign up to book coaching or to watch more interviews in our showcase: https://www.interviewing.io See the interviewer’s feedback and transcript here: https://start.interviewing.io/showcase/Ytbd1imDj61q Or view other Meta interviews: https://interviewing.io/mocks?company=meta (https://interviewing.io/mocks?company=meta) Disclaimer: All interviews are shared with explicit permission from the interviewer and the interviewee, and all interviews are anonymous. interviewing.io has the sole right to distribute this content. Timestamps: 00:00 Intro 00:01:34 Problem 1 begins 00:14:48 Problem 2 begins 00:57:04 Feedback and review

13. 

    19 Aug 2025

    Build a Photo Sharing App for Up to 1 Billion Users: System Design Interview with a FAANG Engineer

    REPLAY EPISODE: Curious how to approach large-scale design interviews? This is the level of depth, structure, and clarity that top companies like Stripe and Meta are actively looking for. In this mock system design interview, a senior engineer is asked to design a photo-sharing app (something like Instagram, but from scratch and at massive scale). What follows is a masterclass in thoughtful architecture: from exploring nuanced functional and nonfunctional requirements to tackling schema design, RESTful APIs, and infrastructure choices under realistic constraints. The candidate works through challenges like privacy controls, feed generation, follow request flows, and media storage for 1 billion daily active users—all while communicating clearly and methodically. You’ll see them navigate trade-offs around consistency vs. availability, break down how to serve images efficiently with CDNs, and set up queues for asynchronous photo processing. Sign up to book coaching or to watch more interviews in our showcase: https://www.interviewing.io (https://www.interviewing.io/) See the interviewer’s feedback and transcript here: https://start.interviewing.io/showcase/B0pirlqbsofX (https://start.interviewing.io/showcase/B0pirlqbsofX) Or view other FAANG interviews: https://interviewing.io/mocks?company=faang (https://interviewing.io/mocks?company=faang) Disclaimer: All interviews are shared with explicit permission from the interviewer and the interviewee, and all interviews are anonymous. interviewing.io (http://interviewing.io/) has the sole right to distribute this content. Timestamps: 00:00 Introduction 00:58 Interview Begins 02:00 Clarifying assumptions: privacy, authentication, feeds 06:45 Discussing follow requests and celebrity suggestions 09:00 Metadata and interaction features (likes, comments) 12:00 Non-functional requirements and scale (1B DAUs!) 15:00 Schema design begins: Users and Follows 20:00 Designing the Photo entity (metadata, location, blob storage) 27:00 Comment schema + verified users 31:00 Designing upload & metadata API endpoints 35:45 Designing follow request and response APIs 41:00 Designing the user’s feed + pagination 44:00 High-level architecture begins: services, DBs, queues 48:00 Document store vs. relational DB for photo storage 52:00 Serving photos via CDN + Follow service interaction 56:00 Final review and detailed feedback from interviewer 01:00:00 Encouragement on focusing more on requirements depth

14. 

    12 Aug 2025

    Detect Fraud & Scam Content: E6 Interview with a Meta Engineer

    REPLAY EPISODE: In this episode of Whiteboard Confidential, an aspiring ML engineer with no formal industry experience impresses a Meta interviewer by tackling a complex system design question: how would you detect fraudulent or scam content on Facebook? Despite never having held an ML job, the candidate delivers a calm,  clear, and deeply thoughtful design that rivals what you’d expect from  an IC5–IC6 engineer. From feature pipelines and model architecture to  deployment strategy and label bias, this interview is packed with insight. The feedback section is especially valuable, touching on how to ask better clarifying questions, start simpler, and negotiate an offer—even in a tough market. A must-watch for anyone preparing for ML system design interviews at top tech companies. Sign up to book coaching or to watch more interviews in our showcase: https://www.interviewing.io See the interviewer’s feedback and transcript here: https://start.interviewing.io/showcase/KQwiaFnBEwAL Or view other Meta interviews here: https://interviewing.io/mocks?company=meta Disclaimer: All interviews are shared with explicit permission from the interviewer and the interviewee, and all interviews are anonymous. interviewing.io has the sole right to distribute this content. Timestamps: 00:00 Introduction and candidate background 00:30 System design prompt: ML model to detect scam content 13:00 Architecture walkthrough and feature engineering 36:00 Modeling strategy, focal loss, and label bias 48:00 Interviewer feedback and praise 51:00 Advice on simplifying, asking questions, and tradeoffs 1:07:00 Meta-specific offer negotiation tips and job market talk

15. 

    7 Aug 2025

    What Separates E5 from E6 (Design Instagram Reels Interview with a Meta Engineer)

    REPLAY EPISODE: In this episode, we have a real mock interview for a Staff Machine Learning (E6) role at Meta, where the candidate is asked to design the recommendation system behind Instagram Reels. This means choosing which short videos to show to billions of users in real time, based on their behavior and interests. The candidate has a strong grasp of ML fundamentals and proposes modern architecture choices like multitask learning and multi-stage ranking. However, they ultimately do not pass the interview—mainly due to time management and not addressing key practical concerns like feedback loops, feature freshness, and production-readiness. The interviewer offers detailed, actionable feedback that gives you a clear picture of what sets apart a good answer from one that meets the E6 bar. If you’re preparing for ML system design interviews at Meta, Google, or other top-tier tech companies, this interview is full of insights to help you sharpen your strategy, improve your pacing, and avoid common pitfalls. Sign up to book coaching or to watch more interviews in our showcase: https://www.interviewing.io (https://www.interviewing.io/) See the interviewer’s feedback and transcript here: https://start.interviewing.io/showcase/Mek40HIliiP0 Or view other Meta interviews: https://interviewing.io/mocks?languag=&company=meta Disclaimer: All interviews are shared with explicit permission from the interviewer and the interviewee, and all interviews are anonymous. interviewing.io (http://interviewing.io/) has the sole right to distribute this content. ⸻ Timestamps: 00:00 Introduction and interview setup 00:53 Problem presented: Instagram Reels recommendation system 17:00 Candidate defines ML framing and objective 38:00 Discussion of candidate generation and ranking model 45:00 Interview ends and candidate self-assessment 46:30 Feedback begins: time management, pacing issues 50:00 Why this would not pass the E6 bar 56:00 What the candidate did well and what was missing 1:03:00 Final takeaways from the interviewer

Inside a recent episode

Longest Consecutive Sequence: Go Interview with a FAANG Engineer

Published 5 Dec 2025 · Transcript excerpt

[…] Sometimes people do ask that. Okay. Okay, okay, okay, okay. Okay, here is Okay. All right. So interview prompt. So this is cut trees on a cliff. It does not exist anywhere. I made this question myself. And usually just for a sake of time, I put in the prompt. What I would do is I'll give you like a very, very big prompt. And then I tell the interviewee is to make the question for me to even make it more complicated. But just for a second time, I provide the question. So you are a landscape designer working on a vertical cliff covered with trees. The cliff is a 2D grid. So let's just say there's a matrix you're given. […]

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