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.
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.
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.
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
See the interviewer’s feedback and transcript here: https://interviewing.io/mocks/google-python-seat-allocation-at-scale
Or view other interviews: 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.
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
See the interviewer’s feedback and transcript here: https://interviewing.io/mocks/netflix-system-design-payment-pipeline
Or view other Netflix interviews: 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.
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
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.
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!
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://interviewing.io/mocks/meta-python-minimum-depth-of-binary-tree
Or view other Meta interviews: 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.
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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
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
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
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
See the interviewer’s feedback and transcript here: https://start.interviewing.io/showcase/B0pirlqbsofX
Or view other FAANG interviews: 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 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
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
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
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 has the sole right to distribute this content.
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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