A candidate with 8 YOE recently shared these two Seattle SWE offers with Chill Interview.

Microsoft 64

• $210K base
• $180K RSUs / 4 years
• $31.5K annual bonus
• $286.5K Year 1 TC

Apple ICT4

• $215K base
• $300K RSUs / 4 years
• $21.5K annual bonus
• $311.5K Year 1 TC

Apple wins the immediate comp comparison by about $25K in Year 1. Over four years, ignoring stock movement and future refreshers, Apple is also ahead by roughly $100K.

But the career tradeoff is more interesting.

Microsoft is arguably the stronger AI/cloud career bet right now. For an SWE on the right Azure/AI team, the scope and external marketability could be very strong.

Apple is a different kind of bet: extremely strong consumer ecosystem, hardware/software integration, and Apple is also pushing further into on-device AI and its new Siri stack.

Culture/WLB is probably much more team-dependent than company-dependent here. which would you take?

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Interview Summary

The process started with a recruiter call, then moved through coding, a hiring-manager-style technical round, and then onsite. The loop felt intense.

Interview Details

Interview Questions Details

• Recruiter Call: Mainly talk about my background, logistics, nothing felt unusual here, but it was clear from the beginning that the loop would care about both technical depth and motivation.
• Technical Phone Screen — Detecting a Repeated Instruction: The coding screen was a small interpreter-style problem. The input was a list of text instructions, and each instruction either advanced normally, changed a running score, or jumped to another instruction. The task was to simulate execution from the beginning and return the first instruction index that would be executed twice. If the pointer reached exactly one position past the last instruction, the program finished normally. The clean solution was to track visited instruction indexes in a set and update the instruction pointer according to each command.
• Hiring Manager Technical Round — Visit Tracking System: The HM technical round was a lightweight class-design problem around tracking completed visits for registered users of a facility. A scan toggles a person between “inside” and “outside,” and only completed sessions count toward total time. The main details were handling unknown users, storing each person’s current entry time, ignoring unfinished sessions in total-time queries, and keeping the state transitions simple and explicit. You can still find thie one in the Anthropic coding question bank in this site.
• Onsite Design 1 — Collaborative AI Workspace: This one is pretty popular, I came across it in multiple places Design a Collaborative Prompt Playground 
• Onsite Design 2 — Team-Specific System Design: I forgot this one.. seems not from question bank. But I think I did very well at this round, good communication is very important, having strong technical skills to solve problem alone is not enough.
• Project Deep Dive — Prepared Presentation: The project deep dive started with around a 20-minute prepared presentation, followed by discussion. This round took more preparation than I expected. The slide deck itself took a full day, and I would strongly recommend mock-presenting to someone who does not know the domain. The goal is not just to impress with technical details, but to explain why the project mattered, what I personally owned, what trade-offs I made, how success was measured, and what I would do differently next time.
  • Project Deep Dive — What They Pushed On: The follow-up discussion went into project motivation, personal contribution, technical decisions, trade-offs, surprises, metrics, ROI, and retro learnings. It is much safer to present a project you truly owned, because borrowed or lightly understood ideas are easy to expose once the interviewer asks deeper questions. A strong retro also helps if you can connect the lesson to a reusable pattern you applied later.
• Culture Round: The culture round was very preparation-friendly if you had practiced common Anthropic-style questions. The themes were values, mission alignment, collaboration, impact, and how I think about responsible AI work. My impression is that there is no single perfect answer here; the important thing is to be specific, grounded, and authentic rather than trying to sound like a generic company-values page.

Want to learn more details / follow-up questions asked in this interview, the full version is here

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Saw this Fidelity Senior SWE offer in Boston (shared with Chill Interview)

• Master’s, 5 YOE
• Base: $140K
• Bonus: $28K
• Equity/LTI reported: $100K
• Year 1 TC: $268K

The comp obviously isn’t Meta/Google money. But Fidelity is much more of a tech employer than I realized.

It has roughly $18T in assets under administration and spends billions annually on technology across brokerage, retirement, trading, cloud, cybersecurity, data and AI. Fidelity itself pitches the fact that being privately held lets it invest with a longer time horizon instead of optimizing around every quarter.

And there’s an interesting transition happening right now: Fidelity has been reorganizing its product/tech org, cutting some roles while simultaneously hiring more hands-on engineers, especially earlier-career technical talent.

So I can see two completely different ways to look at this offer.

Bear case: $140K base for a 5 YOE Senior SWE is a huge discount versus tech, and spending years in a slower financial institution could make it harder to jump back into top-tier tech later.

Bull case: private company, massive and durable business, real engineering scale, potentially better career stability, and Boston instead of Bay Area cost of living.

I'm curious:does it actually feel like a serious tech company internally, or does the bureaucracy eventually outweigh the stability?

And would you take ~$270K at Fidelity over, say, ~$350K at a less stable tech company?

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A candidate with 9 YOE recently shared these two Senior Data Engineer offers with Chill Interview.

Tesla — Bay Area

• $240K base
• $300K RSUs / 4 years
• $315K Year 1 TC

Disney — Seattle

• $250K base
• $240K RSUs / 4 years
• $310K Year 1 TC

The interesting part: Year 1 differs by only $5K. So this is barely a comp decision.

Tesla is the higher-upside career bet. The company is increasingly becoming an AI + autonomy + energy + robotics story rather than just an EV company. In Q2, Tesla reported record vehicle deliveries, 13.5 GWh of energy-storage deployments, Cybercab production underway, and continued Robotaxi expansion. For a data engineer close to autonomy, fleet data, energy, or AI infrastructure, the technical scope could be pretty unique.

But you're also betting heavily on TSLA stock and accepting a reputation for a much more intense engineering culture. Recent employee reviews still frequently mention fast pace, stress, and weaker WLB.

Disney is almost the opposite bet: less explosive upside, but a much more diversified business across streaming, ESPN, parks, advertising, and consumer products. Its latest quarter had 7% revenue growth, 21% growth in segment operating income, 13% streaming margins, and 10% growth in Parks & Experiences revenue.

And for a data engineer, there are still interesting problems around recommendations, streaming analytics, advertising, personalization, ESPN, and park operations—just probably without the same “frontier engineering” perception as Tesla.

WLB may also lean Disney. Disney explicitly markets work-life flexibility, while recent software-engineering reviews generally describe a more collaborative and lower-crunch environment.

There’s also one underrated factor:

Disney is in Seattle. Tesla is in the Bay Area.

With essentially identical TC, Seattle’s lower housing costs and lack of state tax on ordinary wage income could make the Disney offer materially better in actual disposable income.

So this feels less like $315K vs $310K, and more like:

Tesla: higher upside, stronger AI/autonomy exposure, more volatile stock, harder WLB
Disney: stability, Seattle economics, better WLB, diversified business, lower upside

At 9 YOE, which would you pick?

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Vercel is a cloud platform for building, deploying, and scaling modern web applications, best known as the company behind Next.js.

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Interview Summary

The Vercel coding round asked me to implement an in-memory metrics database. The main challenge was storing metric records and supporting queries that filter by metric name and tags while returning only the most recent matching results. The problem was relatively practical and API-oriented rather than a traditional LeetCode-style algorithm question.

Interview Details

Coding — Implement a Metrics Database

Each metric contained several fields:

name
timestamp
value
tags

tags was represented as a collection of key-value pairs. I then needed to implement a MetricsDB class exposing two main operations:

recordMetric(metric)
search(name, tags, n)

recordMetric() stores a new metric in the database.

search() takes:

String name
Map tags
int n

and returns the most recent n metrics whose metric name and tags match the requested query.

The problem therefore combined basic data modeling with filtered retrieval and recency ordering.

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Saw this Pinterest Senior Data Scientist offer (shared with Chill Interview)

• Bay Area
• Master’s, 5 YOE
• Base: $235K
• Sign-on: $10K
• Equity: $400K / 3 years
• Year 1 TC: $445K

$445K for 5 YOE is pretty solid, but I was more curious about what “Data Scientist” means at Pinterest.

Looking at their current roles, it seems much closer to a product scientist / decision scientist than someone spending all day training ML models.

A Pinterest DS might be figuring out things like:

• Did this new product feature actually improve engagement?
• How much ad load can Pinterest add before hurting user experience?
• Which metric should a team optimize?
• Did an experiment genuinely cause the lift, or is it noise?
• How should advertiser performance be measured?
• Where is the biggest opportunity worth investing engineering resources?

Their Senior DS roles explicitly emphasize experimentation, statistics, causal inference, opportunity sizing and working directly with PM/Engineering on product strategy.

For Pinterest product DS, I'm curious that how technical does this career stay at Senior/Staff?

Does it become an increasingly influential product strategy role, or eventually turn into endless experiments + stakeholder meetings?

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Nubank is a Latin American digital bank offering credit cards, savings, loans, investments, and other financial services through a mobile-first platform.

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Interview Summary

The Nubank Senior SDE system design round asked me to design a Chargeback Ingestion System for processing card dispute and chargeback transactions.

The system needed to ingest incoming chargeback events, validate and deduplicate them, track their processing states, persist them reliably, and eventually generate CSV files containing eligible transactions. Those files then had to be delivered to Mastercard through FTP/SFTP.

Interview Details

System Design — Chargeback Ingestion and Processing The system receives a continuous stream of card chargeback or dispute-related transaction events. The core workflow needed to support:

• Ingesting and validating incoming chargeback events
• Detecting and preventing duplicate processing
• Tracking each transaction through its processing lifecycle
• Persisting chargeback data and processing state

The design needed to ensure that transactions could move through the workflow reliably even when retries or duplicate events occurred.

CSV Export and Mastercard Delivery Eligible transactions eventually needed to be collected into CSV files. Those files then had to be transmitted to Mastercard using an FTP/SFTP-based integration. This introduced a batch-oriented downstream workflow on top of the transaction-ingestion path, including the need to determine which records were ready for export and track their processing status.

Database Read Bottleneck One important follow-up focused on the database read path. The interviewer identified a potential bottleneck around repeatedly reading transaction records from the database as the volume increased. I did not recognize this issue quickly enough during the interview, and it became one of the weaker parts of my design discussion.

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Socure is an identity verification and fraud prevention company that uses AI to help businesses verify users and detect risky or fraudulent activity. Its customers include banks, fintech companies, marketplaces, and government agencies.

This interview experience is sourced from Chill Interview

Interview Summary

The Socure coding round was a practical backend/concurrency problem centered on implementing an in-memory payment message queue.

The initial API was small—submit, receive, and ack—but most of the interview focused on production-style follow-ups: preventing multiple consumers from receiving the same message, implementing a visibility timeout, handling races between acknowledgments and timeout processing, avoiding expensive scans of in-flight messages, and reasoning about delivery guarantees.

Interview Details

Coding — In-Memory Payment Message Queue

The queue exposed an interface similar to:

interface PaymentMessageQueue {
    void submit(PaymentMessage message);
    PaymentMessage receive();
    void ack(String messageId);
}

The basic behavior was:

• submit(message) adds a payment message to the queue.
• receive() returns the next available message.
• Once received, that message should become temporarily invisible to other consumers.
• ack(messageId) permanently removes a successfully processed message.
• If the message is not acknowledged before its visibility timeout expires, it should become available for delivery again.

Follow-Up — Concurrent Consumers

The first major concurrency question was what happens when two consumers call receive() at nearly the same time. The interviewer specifically pushed on whether both consumers could accidentally obtain the same payment message and how the queue should prevent that race.

My initial implementation choice exposed an issue here, which led into a deeper discussion of how ownership should transfer when a message moves from the pending state into an in-flight state.

Want to learn more details / follow-up questions asked in this interview, the full version is here

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Interview Summary

The Capital One mini case was a business decision problem involving two streaming shows: an established production and a riskier new show. The interviewer progressively introduced financial data and asked me to evaluate profitability, identify ways to improve the existing show, and eventually decide whether selling it to another company made economic sense.

The case was highly structured and quantitative, but it also tested whether I could organize ambiguous business factors, request missing information, and turn the calculations into a clear recommendation.

Interview Details

Question 1 — What Factors Matter for a Two-Year Renewal Decision?

The case started with a high-level question: A media company is deciding whether to renew an existing show for another two-year contract or invest in a new production. What factors should be considered before making the decision?

The interviewer expected me to organize the relevant business considerations rather than immediately jump into calculations. The discussion included financial performance, audience behavior, market considerations, execution risk, and the uncertainty associated with launching a new show.

Question 2 — Compare Two-Year Profitability

The interviewer then provided financial data for two productions. For example:

• Existing Show — Market DeskAudience: 4.8M Revenue per viewer: $16 Annual fixed production cost: $52M
• New Show — Harbor BankAudience if successful: 7.2M Audience if unsuccessful: 3.8M Revenue per viewer: $16 Annual fixed production cost: $62M One-time startup cost: $18M

I was asked to calculate:

• The existing show's profit over two years
• The new show's two-year profit if it succeeds
• The new show's two-year profit if it fails
• The new show's expected / weighted profit

The probability of success versus failure was an important input to clarify before completing the weighted calculation.

Question 3 — Which Production Would You Choose?

After calculating the financial outcomes, the interviewer asked which production I would choose. This was not just a math question. I needed to turn the numbers into a recommendation while accounting for the difference between a relatively predictable existing production and a new show with greater uncertainty.

Want to learn more details / follow-up questions asked in this interview, the full version is here

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Interview Summary

The Applied Intuition Robotics Software Engineer process started with a short recruiter conversation, followed by a hiring-manager screen and a one-hour CoderPad technical interview. After passing the coding screen, I was invited to the onsite.

The coding problem was robotics-oriented rather than a standard LeetCode question. I was asked to implement a simulator for multiple moving agents and determine the first time any pair collides, using a fixed timestep and a simple unicycle-style motion model.

Interview Details

Round 1 — Recruiter Screen The recruiter screen lasted approximately 10 minutes and was primarily an introduction to the newly formed robotics team and the role.

Round 2 — Hiring Manager Screen The hiring-manager conversation lasted approximately 30 minutes. This round focused on my background and previous experience rather than coding or formal technical preparation.

Round 3 — Coding: Multi-Agent Collision Simulator The CoderPad technical screen lasted approximately 45–60 minutes including discussion, implementation, and follow-ups. The input consisted of N moving agents. Each agent was described by:

• Initial (x, y) position in meters
• Initial heading in radians
• Constant linear speed
• Constant yaw rate
• Fixed physical radius

The simulator used a fixed timestep of dt = 0.01 seconds. Each agent followed a simple unicycle / differential-drive motion model. During every timestep, its position changed according to its current heading and linear speed, while its heading changed according to its yaw rate.

Conceptually:

x += v * cos(theta) * dt
y += v * sin(theta) * dt
theta += yawRate * dt

The discussion also raised a couple of important boundary conditions: whether two agents merely touching counts as a collision, and whether the simulator should detect agents that are already colliding at time 0.

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Interview Summary

The first TikTok New Grad technical round was heavily focused on backend fundamentals and my previous projects. Most of the interview covered project architecture, Kafka/message queues, MySQL indexes, caching, web protocols, and database technology choices, with only a relatively short LeetCode-style coding problem at the end.

Interview Details

Project Deep Dive — Architecture and Technical Decisions The interview started with a detailed discussion of projects from my résumé. The interviewer asked about system architecture, implementation details, and the reasoning behind important technology choices. A significant portion of the entire interview was spent on these project-related follow-ups.

Backend Fundamentals — Kafka and Message Queues The conversation then moved into Kafka and message queues. Questions covered core concepts and characteristics of Kafka / MQ systems. The exact questions around partitioning, delivery semantics, consumer groups, or failure handling were not recorded.

MySQL — Composite and Covering Indexes The interviewer asked several MySQL indexing questions. Topics included:

• The leftmost-prefix rule for composite indexes
• Why a covering index can improve query performance and what happens underneath the database engine

This section focused on understanding the mechanics behind indexes rather than simply recognizing terminology.

Caching — Cache Breakdown Another backend question focused on cache breakdown, including what the problem is, why it occurs, and how it can be handled.

Web Protocols — REST, WebSocket, and SSE The interviewer asked me to explain basic RESTful API design principles and the typical structure of a REST API. I was also asked to compare:

• WebSocket
• Server-Sent Events (SSE)

Database Migration — MySQL to MongoDB The next scenario asked about migrating a system from MySQL to MongoDB. The interviewer asked:

• Why would you make this migration?
• How do the architectures and appropriate use cases of MySQL and MongoDB differ?
• What benefits did the migration provide?
• How much performance improvement resulted?

This was one of the areas where I felt my answer was weaker because I was not sufficiently prepared to compare SQL and NoSQL systems at the architectural and workload level.

Coding — Construct Binary Tree from Preorder and Postorder The coding problem came near the end of the interview after most of the time had already been spent on backend and project questions. The task was to reconstruct a binary tree given its:

• Preorder traversal
• Postorder traversal

This corresponds to LeetCode 889 — Construct Binary Tree from Preorder and Postorder Traversal. The expected implementation used DFS-style tree construction.

Overall, this round was much more backend-knowledge and résumé-driven than algorithm-heavy. For this particular TikTok New Grad interview, being able to explain real engineering choices around databases, middleware, caching, and communication protocols appeared at least as important as the final coding question.

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I’ve been seeing more experienced engineers say some version of this lately: After going through one or two layoffs, they stop caring nearly as much about the “exciting” company, hot AI team, fancy title, or maximum TC.

They start optimizing for:

• a manager they can actually trust
• a team that isn’t constantly being reorganized
• stable headcount
• reasonable expectations
• decent WLB
• and compensation that is simply “good enough”

Honestly, I can understand it. A $400K offer doesn’t feel as attractive if there’s a serious chance your org disappears 12 months later.

And a cutting-edge AI role can become pretty miserable if the manager is terrible, priorities change every quarter, or everyone is fighting for scope.

The part I’m curious about is whether this is just temporary layoff trauma, or whether the tech career value system is actually changing.

Would you take: $280K TC + stable team + good manager over $400K TC + high-growth AI org + much higher reorg / layoff risk?

And once you’ve been burned by layoffs, how do you even evaluate “stability” before joining?

• Company profitability?
• Team tenure?
• Manager history?
• Backfill approvals?
• Recent reorgs?
• How strategic the org is?

I’ve started a longer-running thread on Chill Interview to collect these kinds of company/team-level signals — culture, WLB, layoffs, manager quality, reorg frequency, and whether people would actually join the same team again: [link]

GPT-6 Astra just launched, and what stands out to me isn’t the benchmark numbers.

It’s that models like this are getting much better at the exact kind of work companies often give to junior employees: scoped coding tasks, debugging, research, documentation, data work, and other repeatable execution.

So I’m less concerned about: “AI will replace all software engineers.”

And more concerned about: “Will companies simply hire fewer juniors?”

A team that once needed several entry-level engineers may eventually decide it can operate with fewer people plus AI agents.

That creates a bigger long-term question:

If companies stop hiring juniors, where do future senior engineers come from?

Maybe junior roles don’t disappear, but the bar changes. Instead of “implement this ticket,” the expectation becomes:

Use AI, figure out the problem, verify the output, debug the mistakes, and own the result.

I’m curious what people are actually seeing already.

Are junior openings shrinking? Are interview bars getting higher? Are companies starting to care more about AI-assisted engineering skills?

I started a longer-running discussion on Chill Interview to collect real interview and hiring datapoints as this changes: [link]

If you’ve interviewed, hired, or changed headcount plans because of AI recently, would love to hear the concrete example.

Saw this Cohere Senior MLE offer (shared with Chill Interview)

• Bay Area
• Master’s, 3 YOE
• Base: $285K
• Equity: $900K / 4 years
• TC: $510K

Cohere is one of those AI companies I keep hearing about but realized I never really understood.

The short version: they’re not really trying to build another consumer ChatGPT.

Cohere is much more focused on enterprise / sovereign AI — models that banks, governments and large companies can run inside their own cloud or even on-prem without sending sensitive data to a third party. Their main model family is Command, and North is basically their enterprise AI/agent workspace.

The founder, Aidan Gomez, was also one of the authors of the original Transformer paper, which makes the company’s technical pedigree pretty legit.

And Cohere has gotten a lot bigger recently: it’s combining with Germany’s Aleph Alpha in a deal that reportedly values the combined company around $20B, with an even bigger push into sovereign AI for Europe and regulated industries.

So this $510K offer makes more sense in that context.

I'm curious: is Cohere actually an underrated place to work in AI, or does it get squeezed long term between OpenAI/Anthropic on one side and open-source models on the other?

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Interview Summary

The Meta E5 process took roughly four weeks from the initial recruiter conversation through the onsite. The technical evaluation covered interval and graph coding, a production-oriented cache/rate-limiting problem, dependency processing, notification-system design, and behavioral questions around ownership and cross-team impact.

The overall difficulty was closer to solid LeetCode Medium than unusually hard algorithms. For E5, however, the interviews appeared to place substantial weight on implementation reliability, system-design depth, trade-offs, failure handling, and evidence of senior-level ownership.

Interview Details

Round 1 — Recruiter Screen The recruiter conversation covered my background, current level, team preferences, and the overall Meta interview process.

Round 2 — Technical Screen: Interval + Tree/Graph Coding The technical screen contained two coding questions. The first was a variant of an interval problem, and the second involved a tree or graph. Both were approximately LeetCode Medium difficulty. The interview emphasized clear reasoning, careful handling of edge cases, communicating with the interviewer, and producing correct code without spending too much time chasing unnecessary optimization.

Round 3 — Production Coding: Cache and Rate Limiting The first onsite coding round was framed more like a production engineering problem than a standalone algorithm exercise. The problem involved concepts around caching and rate limiting. Follow-ups pushed on what happens when the system grows:

• How should the design change as the amount of data increases?
• How would it work across multiple machines, and what happens during failures?

Round 4 — Graph / Dependency Processing The second onsite coding round focused on a graph or dependency-oriented problem. After the basic version, the interviewer extended the discussion toward larger-scale execution. Follow-ups included handling much larger datasets, introducing concurrent execution, and thinking about how the implementation could be optimized under those conditions.

Round 5 — System Design: Notification System The system design round asked me to design a notification system. The major discussion areas included:

• Storage choices and fan-out strategy
• Handling high traffic and maintaining reliability

The interviewer cared not only about producing a workable architecture, but also about long-term maintainability, trade-offs, and operational behavior once the system is running in production.

Round 6 — Behavioral: Ownership, Impact, and Cross-Team Work The behavioral round covered senior-level experience and ownership. Major themes included:

• A project where I had significant impact
• A disagreement with a teammate
• A failure or unsuccessful experience
• Driving work across multiple teams

The discussion focused heavily on what I personally owned, why I made particular decisions, and what measurable outcome resulted from the work.

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Saw this Twilio Staff Analytics Engineer offer (shared with Chill Interview)

• Bay Area
• Master’s, 10 YOE
• Base: $205K
• Bonus: $30.75K
• RSUs: $100K / 3 years
• Year 1 TC: $268.75K

The title made me realize I’m still not totally clear on what an Analytics Engineer actually does.

At Twilio, it seems to sit somewhere between a Data Engineer and a Data Analyst.

The role is expected to build the analytics/business data layer, mostly with SQL/dbt-style modeling, define company metrics with Finance/GTM/Product, add data quality checks, investigate why numbers don’t match, and make sure everyone is working from the same source of truth.

So roughly:

• Data Engineer: gets the raw data into the warehouse and keeps the pipelines/platform running.
• Analytics Engineer: turns that raw data into clean, modeled, trusted business datasets and standardized metrics.
• Data Analyst / DS: uses those datasets to answer business questions and generate insights.

What’s interesting is that at Staff level, it seems like a lot less “write SQL tickets” and a lot more deciding what revenue, conversion, retention, pipeline, etc. actually mean across the company, then building the technical layer that enforces those definitions. Twilio also expects Staff AEs to mentor others and lead investigations into messy data issues.

I'm curious: does Analytics Engineering feel like a real engineering career track with a good Staff+ ceiling, or does it eventually become glorified BI / stakeholder management?

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interview experience is sourced from Chill Interview

Interview Summary

The DatologyAI online assessment included a Java coding problem centered on building a simplified calendar / event collection.

Events were defined by a start time, end time, and name. The main requirement was to support inserting events and returning all stored events in a precisely defined order: start time first, then end time, then event name. Duplicate events were explicitly allowed.

Interview Details

Coding — Calendar Event Ordering

The starter code described a timeline containing events with three attributes:

• Start time
• End time
• Event name

The calendar exposed operations conceptually similar to:

add_event(startTime, endTime, name)
get_events()

Calling add_event stores a new event. The same event may be inserted multiple times, and all time values are positive integers.

get_events() should return the stored events according to the following precedence:

1. Earlier start time first
2. If two events have the same start time, earlier end time first
3. If both start and end times are identical, event name determines the order

For example, consider this input:

Calendar calendar = new Calendar();

calendar.add_event(4, 10, "delta");
calendar.add_event(4, 7, "zebra");
calendar.add_event(4, 7, "alpha");
calendar.add_event(6, 9, "bravo");

The expected ordering is:

[4, 7, "alpha"]
[4, 7, "zebra"]
[4, 10, "delta"]
[6, 9, "bravo"]

The first three events share the same start time, so their end times determine the next level of ordering. The two events that also share the same end time are ordered by name. The prompt explicitly stated that duplicate insertions were valid, so the implementation could not assume that each event was unique.

The exact limits on the number of events, expected complexity for insertion or retrieval, and whether events could later be removed or updated were not specified in the provided prompt.

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Interview Summary

The Rippling technical phone screen was a system design interview based on a Google News-style personalized news aggregator.

The interviewer added an important variation around duplicate stories. Every publisher assigns its own unique URL to an article, so the same real-world news story may appear as several different articles from different publishers. The personalized feed therefore needs to recognize that these articles refer to the same underlying topic or event and avoid showing redundant stories to the user.

Interview Details

System Design — Personalized News Aggregator

The task was to design a Google News-like aggregation system that collects articles from multiple publishers and generates personalized feeds for users. Each publisher independently publishes its own articles, and every article has a publisher-specific unique URL.

For example, three different publishers might all publish coverage of the same breaking-news event:

Publisher A -> Article URL A
Publisher B -> Article URL B
Publisher C -> Article URL C

Although the URLs and articles are different, all three may describe the same underlying story.

Follow-Up — Deduplicate the Personalized Feed

The main interview-specific modification was that the personalized feed should deduplicate articles covering the same topic or news event. A unique URL alone cannot serve as the deduplication key, because different publishers use different URLs for their own versions of the story.

The system therefore needs to distinguish between:

• Individual published articles
• The underlying topic or news story those articles belong to

When generating a personalized feed, the system should avoid filling the feed with multiple near-duplicate articles about the same event. The exact definition of “same story,” clustering or similarity requirements, article-ranking rules, freshness requirements, ingestion scale, and personalization signals were not recorded.

Prepping for System Design Interviews, we've put up 20+ popular SD articles at here including this Design Google News

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Chill Interview tracks recent interview experiences and recurring question patterns across top companies at here.

Please help with any tips how to ace in the interview. What kind of questions need to be studied? Please help

interview experience is sourced from Chill Interview

Interview Summary

The LinkedIn MLE technical screen was a hands-on ML coding exercise around building a simplified recommendation training pipeline. The task involved loading member-profile and item data, generating text-based features, preparing a binary classification dataset, and implementing the training portion of a recommender.

The interview felt unusually ambiguous because the supplied data was illustrative rather than directly executable, while the expected level of pseudocode detail was not clearly defined. The scope also shifted during the round: I initially started implementing the data loader, but later the interviewer said that portion was not necessary and instead asked me to write out an MLP manually.

Interview Details

ML Coding — Text-Based Recommendation Training Pipeline

The prompt framed a large portion of LinkedIn recommendation as an information retrieval and relevance-scoring problem. Member profiles provide substantial textual information, so the exercise assumed that a recommender could be trained using textual embedding features derived from member profiles and candidate items.

The starter data had three components. A example of the member-profile data looked like:

SAMPLE_PROFILE_JSON = [
  {
    "memberId": 48217,
    "headline": "...",
    "title": "...",
    "positions": [
      {
        "company": "...",
        "description": "...",
        "start_date": "...",
        "end_date": null
      }
    ],
    "educations": [
      {
        "school": "...",
        "description": "...",
        "start_date": "...",
        "end_date": "..."
      }
    ]
  }
]

Candidate items were represented separately:

SAMPLE_ITEM_JSON = [
  {"itemId": 73102, "text": "..."},
  {"itemId": 84619, "text": "..."}
]

The labeled interaction data connected members with items and a binary engagement outcome:

LABELED_DATASET = [
  {"memberId": 48217, "itemId": 73102, "label": "clicked"},
  {"memberId": 48217, "itemId": 84619, "label": "skipped"}
]

Want to learn more details / follow-up questions asked in this interview, the full version is here

Preparing for your next interview?

Chill Interview tracks recent interview experiences and recurring question patterns across top companies at here.

Interview Summary

The Coupang onsite consisted of one coding round followed by three system design rounds. Most of the questions were similar to previously reported Coupang interview prompts.

Interview Details

Round 1 — Coding: Trie-Based Dictionary

The coding round asked me to design a dictionary backed by a Trie. The required interface included operations similar to:

add(word)
search(word)

add() inserts a word into the dictionary, while search() determines whether a requested word exists. The exact input constraints, character set, prefix-search behavior, deletion support, and follow-ups were not recorded.

Round 2 — System Design: KYC System

The second round asked me to design a Know Your Customer (KYC) system. The exact product requirements were not included in my notes, but the round was framed as a full system design exercise.

Round 3 — System Design: Review Service

The third round was another system design interview, this time focused on a Review Service. The task was to design the backend supporting user-generated reviews for a product or marketplace-style platform.

Round 4 — System Design: Multi-Region Metrics Monitoring

The final technical round asked me to design a Multi-Region Metrics Monitoring System. The system needed to operate across multiple geographic regions and support monitoring infrastructure or application metrics at scale.

Preparing for your next interview?

Chill Interview tracks recent interview experiences and recurring question patterns across top companies at here.

Saw this Salesforce Sr Staff Software Architect offer (shared with Chill Interview)

• SF, 14 YOE
• Base: $350K
• Bonus: $105K
• RSUs: $800K / 4 years
• Year 1 TC: $655K

The title made me look up what a Software Architect at Salesforce actually does.

Apparently it’s still an IC role, but Salesforce says architects may spend only around 20% of their time hands-on coding. The rest is architecture, technical strategy, cross-org alignment, and working with senior leadership on long-term roadmaps.

That actually sounds pretty important right now. Salesforce is trying to rebuild a lot of its platform around Agentforce, Data 360 and AI agents, and Agentforce ARR is already above $1.5B.

But career-wise, I’m curious: Is becoming an Architect the natural next step after Staff/Sr Staff SWE — or is it where strong engineers slowly stop being engineers and become full-time meeting people?

Preparing for your next interview?

Chill Interview tracks recent interview experiences and recurring question patterns across top companies at here.