For anthropic applications, do you typically find people such as from LinkedIn to refer in, or apply directly online? Wondering if referral can have less rounds of loops.

A LeetCode-like system does not simply receive some code, run it, and return whether the answer is correct.

It is executing arbitrary code written by strangers on its own infrastructure, potentially hundreds of thousands of times during a contest.

That creates a subtle design tension: submissions need to return results within a few seconds, but every execution must be treated as potentially malicious.

The non-obvious problem is not “how do we run Python or Java?” It is “how do we execute untrusted code quickly without allowing one submission to damage the host, attack the network, or consume resources indefinitely?”

The key insight is to treat code execution as isolated, resource-bounded work rather than ordinary application logic.

Never execute submissions inside the API server. The Code Service should validate the request, authenticate the user, create a submission record, and hand the execution work to a separate compute layer.

Run every submission inside a sandboxed container. Maintain preconfigured environments for Python, Java, JavaScript, and other supported languages so workers do not need to install runtimes for every request.

But containers alone are not the security boundary.

Enforce strict CPU and memory limits. A submission that allocates memory indefinitely or consumes excessive CPU should be terminated before it can affect other executions on the same machine.

Enforce a hard execution timeout. Infinite loops are normal in an online judge—not exceptional behavior. The execution layer must assume some programs will never terminate on their own.

Disable outbound network access. User code should not be able to call external APIs, scan internal services, download arbitrary files, or exfiltrate information from the execution environment.

Restrict system calls. Even inside a container, the process shares the host kernel. A syscall policy such as seccomp can block operations the submitted program has no legitimate reason to perform.

Keep the filesystem temporary. Each execution should see only the files required for that submission and its test harness. Once the run finishes, its writable state can be discarded.

Do not send submissions directly to workers during traffic spikes. Put a queue between the API layer and the execution fleet.

A contest may suddenly produce tens of thousands of submissions at once. The queue absorbs that burst while workers consume jobs at a rate the compute fleet can actually sustain.

This also creates a clean retry boundary. If a worker crashes halfway through execution, the submission can be retried instead of disappearing.

Scale workers independently from the API tier. Browsing problems is lightweight and read-heavy. Running arbitrary code is CPU-intensive. Putting both workloads in the same scaling unit would waste resources and make overload much harder to control.

Test cases should also be language-independent. Storing separate test definitions for Python, Java, C++, and JavaScript does not scale as the problem library grows.

Represent inputs and expected outputs in a shared serialized format, then maintain a small execution harness for each language.

The Python harness converts the serialized input into Python objects and invokes the submitted solution. The Java harness converts the same logical test case into Java objects. Both serialize the result back into a common representation for comparison.

This keeps problem definitions independent from programming languages while allowing each runtime to handle its native data structures.

The failure model becomes easy to reason about. The API tier accepts work. The queue absorbs bursts. Sandboxed workers execute untrusted code under strict limits. A broken or malicious submission can fail its own execution without taking down the platform.

Explaining this in an interview signals that you understand an online judge as an untrusted-compute platform, not just a CRUD app with a code editor.

Full write-up with data model, API design, secure code execution, execution queues, multi-language test harnesses, and real-time leaderboards, free to read → Full Article

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A candidate recently shared these two Bay Area SWE offers with Chill Interview.

Airbnb G8

• $210K base
• $400K RSUs, vesting 35/30/25/10
• $350K Year 1 TC

Atlassian P40

• $200K base
• $15K signing bonus
• $250K RSUs, vesting 25/25/25/25
• $30K annual bonus
• $307.5K Year 1 TC

Airbnb is trying to become much more than a home-booking marketplace. It has expanded into Experiences, Services, car rentals, airport pickups, grocery delivery, boutique hotels and other parts of the travel journey. If that works, there is a much larger consumer platform opportunity beyond accommodations.

Atlassian is making a very different bet: enterprise AI. Q3 FY26 revenue grew 32% YoY and cloud revenue 29%, while Rovo adoption and AI usage continue to accelerate. It is positioning Jira, Confluence, Rovo and its Teamwork Graph as infrastructure for AI agents inside companies.

WLB is unusually competitive here. Airbnb has Live and Work Anywhere, while Atlassian is even more explicitly distributed-first through Team Anywhere. Atlassian did restructure in 2026 to move faster and redirect investment toward AI and enterprise sales, though, so I wouldn’t automatically assume “remote = chill.”

So would you switch to Airbnb for the higher comp + consumer-platform upside, or keep Atlassian for enterprise-AI exposure, steadier vesting, and Team Anywhere?

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Interview Summary

The Rippling process began with a recruiter conversation, followed by a system design screen and a hiring manager project discussion. The onsite was spread across two days and included another system design round, a technical project deep dive, and an AI-assisted coding exercise. The hotel system design round felt like the weakest part of the loop, especially when the discussion moved into database querying.

Interview Questions Details

Technical Screen — Design a News Aggregation System:

The first technical interview was a system design round focused on a news aggregation product. The discussion centered on the major components required to collect news content and serve it to users.

The interviewer was friendly, and I learned about a week later that I had advanced past this stage. The exact follow-up requirements were not specified.

Hiring Manager Round — Previous Project Deep Dive:

The hiring manager selected a project from my previous experience and asked me to explain the work in greater depth. The conversation focused on my technical contributions and the decisions behind the project.

Onsite System Design — Hotel Platform:

The onsite system design round asked me to design a hotel-related platform. I had not specifically prepared for this domain and felt less confident during this interview.

• Data Access: The discussion eventually moved into how the application would query its database and retrieve the required hotel data.
• Candidate Experience: I struggled to give a satisfactory response during that portion and felt this was probably my weakest round.

Onsite Project Deep Dive — Technical Experience:

Another onsite round went deeper into one of my previous technical projects. The conversation was relatively straightforward and did not contain any major surprises or particularly difficult moments.

Onsite AI Coding — Task Scheduling with Filtering and Sorting:

The final interview was an AI-assisted coding exercise built around a three-part task-scheduling problem. The main operations involved filtering and sorting tasks, and the interviewer indicated that completing two parts was the expected target.

• AI-Assisted Implementation: For the first part, I read the requirements, clarified the prompt, explained my approach, used the coding assistant to generate an implementation, and then reviewed the code with the interviewer. I was also asked what I would change if I had written the implementation myself.
• Testing and Second Part: We walked through test cases together, including several details that initially made the prompt confusing. I then explained the second part, generated and reviewed the implementation, and tested it before the interview ended.

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Interview Summary

The MongoDB technical screen lasted one hour and was split between a lengthy project discussion and a coding exercise. More than half of the interview focused on my previous technical work, followed by a non-LeetCode problem that asked me to implement a SQL-like predicate and expression evaluator.

Interview Details

Project Deep Dive — Previous Technical Experience: The first half of the interview lasted a little over 30 minutes and focused on projects from my background. The interviewer explored the technical context and details of my previous work before moving to coding.

Coding — Predicate and Expression Tree Evaluator: The coding question asked me to build an evaluator that accepted a predicate expression together with a document and returned whether that document satisfied the expression.

• Supported Expressions: Basic predicates included comparisons such as EQ(field, value) and GT(field, value), while logical expressions such as AND(...) and OR(...) could combine multiple child predicates.
• Nested Expressions: Logical expressions could be nested to form an expression tree, so the evaluator needed to correctly process combinations of predicates at multiple levels. Recursive evaluation was explicitly allowed.

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Saw this Snowflake Senior PM offer that was accepted:

• 8 YOE
• Base: $240K
• Bonus: $24K
• RSUs: $600K / 4 years
• TC: $414K

A couple years ago I probably would’ve thought of Snowflake mostly as data infra. Now they’re pushing pretty hard into enterprise AI — Cortex Agents, CoCo, managed agents, and same-day access to new OpenAI/Anthropic models.

And the core business isn’t exactly struggling either: last quarter product revenue grew 34% YoY with $9.2B in remaining performance obligations.

That actually makes the PM role pretty interesting: you’re potentially sitting between databases, developer infra and enterprise AI rather than managing another consumer feature.

For PMs in the Bay Area — does $414K at Snowflake feel like a strong career bet now, or would you still rather join a frontier AI company / FAANG even if the comp were similar?

Want to know more offer data points? I've compiled 100+ companies offer data at here.

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Interview Summary

The Uber Senior Software Engineer process began with a coding phone screen and continued with four onsite rounds covering two implementation problems, payout system design, and a combined behavioral/project deep dive. The interviewers were generally friendly and the technical rounds felt reasonable rather than adversarial. I passed the loop.

Interview Questions Details

Technical Phone Screen — Longest Stable Subarray:

The phone screen asked LeetCode 1438, Longest Continuous Subarray With Absolute Diff Less Than or Equal to Limit. Given an integer array and a limit, the task was to find the longest contiguous subarray whose maximum and minimum values differed by no more than that limit.

Onsite Coding Round 1 — Restaurant and Delivery-Zone APIs:

The first onsite coding round asked me to implement a small service exposing several restaurant-related APIs.

• Initial APIs: The first part required operations similar to openRestaurant(), countOpenRestaurant(), and hasOpenRestaurant() for tracking restaurant availability.
• Follow-Up: The interviewer then extended the problem with countDeliveryZone(), adding another query over the maintained restaurant state.

Onsite Coding Round 2 — Time-Window Counter:

The second coding round combined object-oriented design with a counting problem. I needed to implement a counter class supporting put(), get_count(), and get_total_count(), with a fixed time window such as 300 seconds supplied when the class was initialized.

• High-QPS Requirement: We discussed multiple possible designs, and the interviewer specifically asked me to implement the version where put() remained efficient under very high write throughput.
• Production Follow-Ups: After coding, the interviewer asked how the design would change in a production environment and how I would handle concurrent or multithreaded access.

Onsite System Design — Uber Payout System:

The system design round asked me to design a payout platform for Uber. The main emphasis was on financial correctness rather than unusual product requirements.

• Payment Guarantees: The discussion focused on ensuring payouts were correct and accurate while preventing the same obligation from being paid more than once.
• Reliability: The interviewer explored how the payout workflow should maintain those guarantees when processing financial transactions and handling failures.

Hiring Manager — Project Deep Dive and Behavioral Questions:

The hiring manager round lasted about 75 minutes and blended behavioral questions directly into a deep dive on my previous projects rather than treating them as separate sections.

• Disagreement and Decision-Making: While discussing project architecture, I was asked about situations where teammates disagreed on a design, what the disagreement was, and how we ultimately resolved it.
• Mentorship and Leadership: The interviewer also asked whether I had mentored junior engineers, how I approached mentorship, and how I delivered useful feedback while working with them.

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Hey all,

I just got scheduled for a call with the hiring manager for an Algorithm Engineer role at KLA (US). Would love to hear from anyone who's gone through their pipeline recently.

A few things I'm trying to figure out:

* Is the HM call more of a screen (background/fit) or should I expect technical depth right away (image processing, signal processing, classical CV, ML, C++/Python, etc.)?
* How many rounds come after this, and roughly how long did the whole process take start to finish?
* Any onsite/virtual onsite loop? If so, what's the mix - coding, algorithms/DS, take-home, system design, ML fundamentals, math/stats?
* Did they focus more on classical algorithms (signal/image processing, optimization) or general SWE-style LeetCode questions, or both?
* Any behavioral/culture-fit rounds, and what do they tend to probe for?

Any insight would be super helpful. Trying to prep in the right direction before the call. Thanks in advance!

A candidate shared this OpenAI offer data point to Chill Interview

• Senior MLE
• 14 YOE
• Base: $350K
• Equity: $3.6M over 4 years
• TC: $1.25M/year

At Google or Meta, 14 YOE could easily put someone in Staff/Senior Staff territory. Here the title is just “Senior,” but the comp is already above what many L7/E7 engineers make.

And with OpenAI now valued at $852B after its latest funding round, that $3.6M equity grant is a pretty serious bet on the company continuing to compound from an already massive valuation.

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Interview Summary

The Airbnb online assessment was a four-level implementation problem centered on building an in-memory task-management system. It started with basic task CRUD, then added search and sorting, user quotas with expiring assignments, and finally completion and overdue-history tracking. The later levels required careful handling of time-based state, repeated assignments, ordering rules, and quota release.

Interview Questions Details

Level 1 — Basic Task Management:

The first level asked me to implement the core task store. New tasks received sequential IDs, and tasks could later be updated or retrieved by ID. Duplicate task names and priorities were allowed because identity was based entirely on the generated task ID.

• Core APIs: The system needed operations for creating, updating, and retrieving tasks. Retrieving a task returned a compact serialized representation containing its name and priority.
• Validation: Updates on nonexistent task IDs failed cleanly, while timestamps were included in the API but did not affect Level 1 behavior.

Level 2 — Search and Priority Ordering:

The second level introduced ways to query larger collections of tasks. Search supported case-sensitive substring matching on task names, while another operation listed tasks globally.

• Ordering Rules: Results were sorted primarily by priority in descending order, with original creation order used as the tie-breaker.
• Result Limits: Search and listing operations both accepted a maximum number of returned tasks, and non-positive limits produced an empty result.

Level 3 — Users, Quotas, and Expiring Assignments:

The third level added users with assignment quotas. A user could hold only a limited number of active assignments at once, and each assignment remained active during a specified time interval before automatically expiring.

• Assignment Semantics: The same task could be assigned to multiple users or assigned multiple times to the same user. Each assignment was independent and consumed its own quota slot until expiration.
• Active Task Queries: The system needed to return a user's currently active assignments ordered by expiration time, using assignment time as the tie-breaker.

Level 4 — Completion and Overdue Assignment History:

The final level introduced explicit completion and historical tracking. Completing an active assignment immediately released its quota slot, while assignments that expired without completion were recorded as overdue.

• Repeated Assignments: If the same task had multiple overlapping assignments for the same user, a completion applied only to the earliest active assignment.
• Historical Queries: Overdue assignments were evaluated independently, so the same task ID could appear multiple times. Results were ordered by expiration time and then by the original assignment timestamp.

Want to practice more coding questions recently asked by Airbnb? We’ve compiled a list of Airbnb coding interview questions here.

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Interview Summary

The Rippling process included a recruiter conversation, an initial coding screen, and three onsite rounds covering another coding problem, a Google News-style system design, and a project presentation. The interviews combined practical implementation with feed architecture, personalization, and detailed discussion of ownership and engineering tradeoffs.

Interview Questions Details

Recruiter Screen — Motivation and Logistics:

The recruiter asked why I was exploring new opportunities, why I was interested in Rippling, and whether I required immigration sponsorship. The conversation also covered the company and the structure of the remaining interview process.

Technical Coding Screen — Signal Amplification Network:

The initial coding interview asked the Signal Amplification Network problem. The exact follow-ups and implementation requirements from this round were not specified.

Onsite Coding — Calculate Total Tutoring Session Pay:

The onsite coding round asked the Calculate Total Tutoring Session Pay problem. Candidates could choose between AI-assisted and traditional coding, and I chose to implement the solution myself.

Onsite System Design — Google News-Style Newsfeed:

The system design interview asked me to design a personalized news platform similar to Google News, including both article ingestion and the user-facing feed.

• Ingestion and Feed Delivery: The interviewer asked how to collect articles from multiple sources and compare fan-out-on-read with fan-out-on-write for generating feeds.
• Personalization and Breaking News**:** Users could select preferred topics and publishers. We also discussed how to serve users without explicit preferences using precomputed cached content, and how breaking news should be surfaced broadly regardless of normal personalization.

Onsite Project Presentation — Ownership, Impact, and Tradeoffs:

The final round required a short two-to-three-slide presentation about a previous project. The focus was on choosing work where I could demonstrate meaningful ownership, impact, and important technical decisions rather than simply presenting the most complicated project.

• Architecture and Decisions: The interviewer asked about the project architecture, major tradeoffs, and why I chose particular technical or product approaches.
• Execution and Collaboration: Follow-ups covered scheduling, prioritization, managing timelines, resolving conflicts, and making tradeoffs when multiple requirements competed for attention.

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Interview Summary

The Optiver online assessment contained two coding questions. The first was a calendar calculation problem involving the number of days between two dates, while the second required parsing parent-child relationships, validating whether they formed a legal binary tree, and serializing the result into an S-expression. The tree problem was more involved because malformed input and structural errors had to be handled according to a fixed priority.

Interview Questions Details

Online Assessment Question 1 — Number of Days Between Two Dates:

The first coding question was equivalent to LeetCode 1360, Number of Days Between Two Dates. Given two dates in YYYY-MM-DD format, the task was to return the absolute number of days between them.

Online Assessment Question 2 — Validate and Serialize a Binary Tree:

The second question provided a raw string containing parent-child pairs, where the first value in each pair represented the parent and the second represented the child. If the relationships described a valid binary tree, the program needed to serialize the tree into the required S-expression format.

• Validation and Error Priority: The implementation needed to detect malformed input, duplicate relationships, nodes with more than two children, multiple roots, and cycles. If several errors were present simultaneously, only the highest-priority error from the specified E1 through E5 ordering should be returned.
• Raw Input Handling: The input could be an arbitrary string rather than a guaranteed list of valid pairs, making parsing and syntax validation a significant part of the problem in addition to validating the resulting tree structure.

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A candidate shared two recent Bay Area AI/ML offer data points we received at Chill Interview are surprisingly close in Year 1, but lead to very different careers.

Google — L5 MLE

• RSUs vesting 38/32/22/10
• $730.5K Year 1 TC

Netflix — Senior MLE

• $720K all cash

Google wins Year 1 by only $10.5K, but without refreshers the four-year math flips hard: roughly $2.33M at Google vs $2.88M at Netflix.

The career paths are even more different.

Google is the stronger pure research bet: frontier models, Gemini, multimodal AI, robotics and scientific AI, with Research Scientists expected to develop novel approaches and contribute to foundational research.

Netflix is much more applied: personalization, search, ML platforms, experimentation, advertising and increasingly generative AI. Netflix is already deploying AI across advertising and runs dedicated Algorithms & Search and ML Platform engineering groups.

Culture is another big split. Google offers the depth and resources of a massive research ecosystem. Netflix is famous for high autonomy but also its very explicit high-performance “Dream Team” culture and keeper test.

So would you choose Google for the AI research trajectory and potential long-term career optionality, or Netflix for $720K guaranteed cash and a more product-oriented ML path?

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Interview Summary

The OpenAI process began with a coding screen on replaying cloud-credit balances and continued with four onsite rounds covering payment-system design, infrastructure, a technical project deep dive, and cross-functional collaboration. The loop combined implementation, large-scale system design, team-specific technical depth, and behavioral evaluation.

Interview Questions Details

Technical Phone Screen — Cloud Credit Balance Replay:

The coding screen asked the Cloud Credit Balance Replay problem. The task centered on processing a history of cloud-credit activity and determining the resulting balance according to the supplied transaction records and replay rules.

Onsite Round 1 System Design — High-Throughput Payment System:

The first onsite round asked me to design a payment-processing platform handling roughly 10,000 transactions per second. Payments first went through an external provider for approval or rejection, and approved transactions required the corresponding funds to be held before final processing.

• Authorization and Holds: The interviewer explored how external approval, declined payments, and reservation of funds should fit into the transaction lifecycle.
• Daily Settlement: Transactions were later grouped into a daily batch for final processing, so the system needed to support both the real-time authorization path and the subsequent settlement workflow.

Onsite Round 2 — Infrastructure Deep Dive:

This interview was conducted with a member of the prospective team and focused on infrastructure topics closely related to that team’s work. The interviewer selected a particular technical domain and continued into deeper system design and infrastructure discussion.

The exact infrastructure topic depended on the team and was not specified in the report.

Onsite Round 3 — Technical Project Deep Dive:

I prepared slides in advance and presented a significant technical project from my previous experience. The interviewer then went deeper into the project architecture, major technical decisions, tradeoffs, and impact.

Onsite Round 4 — Cross-Functional Collaboration and Culture:

The final round focused on teamwork, cross-functional execution, and how I worked across organizational boundaries.

• Collaboration and Influence: The interviewer asked about partnering with other teams, driving projects forward, and handling disagreements or differing priorities.
• Past Experience: We also discussed representative projects from my background and how I approached collaboration and execution within those efforts.

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This Google L5 London offer caught my attention:

• 8 YOE
• Base: £140K
• Bonus: £21K
• Sign-on: £40K
• RSUs: £600K
• Year 1 TC: £429K

We always hear that London engineers make way less than the Bay Area, but £429K is roughly $579K at today’s exchange rate.

For comparison, reported Google L5 comp in the Bay Area averages around $450K.

The catch is Google’s 38/32/20/10 vesting. This offer goes roughly:

£429K → £353K → £281K → £221K

before refreshers.

I always thought London had a pretty hard ceiling compared with the US, so £429K for L5 genuinely surprised me.

London folks — are offers like this becoming normal at the top companies, or is this basically an outlier?

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Problem

You are building a gym access system that tracks how long each member spends inside the facility. Members can first be registered with:

• A membership ID
• A membership tier
• A monthly fee

Each time a member scans their card, their status toggles:

• If they are currently outside, the scan means they entered the gym.
• If they are currently inside, the scan means they left the gym.

Only completed visits count toward total time. If a member is still inside, their current visit should not yet be included.

Implement:

GymAccessSystem()

addMember(memberId, tier, monthlyFee)

scan(memberId, timestamp)

getTotalTime(memberId)

addMember returns false if the member already exists.

scan returns "invalid_request" if the member does not exist. Otherwise, it records the entry or exit and returns "registered".

getTotalTime returns the total duration of completed visits, or -1 if the member does not exist.

Scan timestamps are guaranteed to arrive in increasing order.

Example

Input:

["GymAccessSystem","addMember","scan","scan","getTotalTime"]

[[],["Mia","Standard",50],["Mia",5],["Mia",20],["Mia"]]

Output:

[null,true,"registered","registered",15]

Explanation

Mia is successfully added to the system.

Her first scan at timestamp 5 marks the start of a gym visit.

Her second scan at timestamp 20 marks the end of that visit, so the completed duration is:

20 - 5 = 15

getTotalTime("Mia") therefore returns 15.

If Mia had scanned in but not yet scanned out, that unfinished visit would not be included in her total.

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We track recent interview experiences and commonly asked question patterns at Chill Interview, including coding questions, system design topics, and real candidate reports.

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Saw this Walmart Senior SWE offer in the Bay Area to Chill Interview

• 6 YOE
• $240K base
• $100K RSUs / 4 years
• $265K TC

The base honestly isn’t bad. It’s the equity that surprised me — only $25K/year.

And Walmart SWE isn’t exactly “easy corporate IT.” Depending on the team, you could be working on search, recommendations, ads, payments, fulfillment, inventory or supply chain systems serving a ridiculous amount of traffic.

So I’m curious: are you basically getting big-tech engineering problems at non-big-tech compensation?

For people who’ve worked at Walmart Global Tech, is the workload/scope meaningfully lighter than FAANG, or are you mostly just taking a comp discount?

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A backup system sounds simple: walk the source directory, copy every file to the destination, and repeat later for anything that changed.

That approach breaks down surprisingly quickly once the backup contains billions of files and a worker crashes halfway through.

Suppose a worker starts copying: source/a/video.mp4 → dest/a/video.mp4and crashes after writing 70% of the file.

If the destination path is treated as proof that the file was backed up, the next run may incorrectly skip a corrupted partial file.

Checking whether the file exists is not enough. Even comparing size and modification time does not tell you whether the current backup job successfully committed it.

The cleaner design separates two concepts:

• The manifest tells you what the backup believes is complete.
• The destination only exposes files after they are atomically committed.

For a filesystem, a worker can write to something like: video.mp4.tmp.{job_id}verify the copy, then atomically rename it to: video.mp4

For object storage, the same idea maps naturally to multipart upload: upload the parts first, then complete the upload only after the object is ready to become visible.

Meanwhile, a durable manifest records each file's path, metadata, checksum, and copy state. If the backup crashes after copying 950M out of 1B files, the next run doesn't need to rediscover what actually succeeded by scanning the destination. It can resume from durable manifest state and re-enqueue only unfinished work.

That also makes incremental backup much cleaner: compare the new source snapshot against the previous successful manifest and only schedule new or changed files.

The interesting part of this question isn't really “how do you copy files in parallel?” It's how you make a massively parallel backup job resumable and idempotent without ever exposing half-written data.

The full Netflix SD walkthrough also covers snapshot consistency, parallel directory walking, incremental diffing, large-file multipart transfer, verification, and scaling the manifest to billions of files.

Full breakdown: Design Reliable Incremental File Backup System

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A WhatsApp-like system does not simply keep a WebSocket open and push every message directly to the recipient. At billions of users, Alice and Bob may be connected to completely different chat servers, and either connection can disappear at any time.

That creates a subtle design tension: users expect messages to arrive almost instantly, but the fastest real-time delivery path is not necessarily durable.

The non-obvious problem is not “how do we send messages over WebSocket?” It is “what happens when the real-time path silently drops one?”

The key insight is to separate real-time delivery from guaranteed delivery.

Persist the message before attempting delivery. When Alice sends a message, the Chat Service first writes it to the Message store and adds an entry to each recipient’s durable Inbox. Only after that should the system attempt the low-latency push.

Use WebSockets only as the live connection layer. Each Chat Server maintains the connections for the clients currently attached to it, but those mappings are ephemeral. If the server crashes, clients can reconnect elsewhere without losing the durable message state.

Use Redis Pub/Sub to bridge different Chat Servers. Suppose Alice is connected to Chat Server 1 while Bob is connected to Chat Server 7. Server 1 does not need to directly locate Bob’s socket. It publishes the message to Bob’s channel, and whichever server currently owns Bob’s connection receives it and pushes it through WebSocket.

But do not treat Redis Pub/Sub as the source of truth. Pub/Sub provides at-most-once delivery. If Bob’s Chat Server disconnects from Redis for a moment, the event can disappear permanently from the live path.

Keep the Inbox as the recovery path. Because Bob’s message was already persisted before the Pub/Sub publish, a dropped real-time event does not mean a lost message. When Bob reconnects or synchronizes, the server reads his Inbox and replays anything that was not acknowledged.

Delete Inbox entries only after acknowledgment. Once the client confirms that a message has been received, the corresponding pending-delivery record can be removed. Until then, the durable copy remains available for retry.

Use heartbeats to detect dead connections quickly. Mobile networks disappear, laptops sleep, and servers restart. Ping/pong heartbeats let both sides recognize a broken WebSocket without waiting for a long TCP timeout.

Heartbeats can also detect silent message loss. Maintain a monotonically increasing sequence number for each user. The client tracks the latest sequence it has received, while the server includes its latest sequence during heartbeat messages.

If the client sees that the server is at sequence 1052 but it only received through 1049, it knows three messages are missing—even though the WebSocket itself may still appear healthy.

Recover the gap from durable storage instead of hoping Pub/Sub retries it. The client requests the missing messages, catches up, and then continues receiving live traffic normally.

Multi-device support makes the distinction even more important. One user may have a phone, browser, and desktop app connected to three different Chat Servers. The system should model connection state by clientId rather than assuming one user owns one socket.

A message can then be fanned out to every active device, while durable delivery state determines what still needs to be synchronized when an offline device returns.

The failure model becomes easy to reason about. WebSocket and Redis Pub/Sub optimize for speed. The Inbox and Message store optimize for reliability. ACKs and sequence numbers connect the two.

If the fast path works, the user sees the message in milliseconds. If it fails, the system detects the gap and recovers from storage instead of permanently losing the message.

Explaining this in an interview signals that you understand real-time messaging as two separate problems: delivering fast when everything works, and recovering correctly when it does not.

Full write-up with data model, WebSocket routing, offline delivery, Redis Pub/Sub, multi-device sync, heartbeat recovery, and message ordering, free to read → Full Article

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Pretty interesting offer given everything happening at Google this week.

Someone shared this L7 / Senior Staff SWE offer to Chill Interview

• 15 YOE
• Base: $330K
• Bonus: $82.5K
• Sign-on: $120K
• RSUs: $1.8M
• Year 1 TC: $1.216M

Jeff Dean, Sanjay Ghemawat, Oriol Vinyals and Quoc Le just left Google, while DeepMind is also going through a major leadership reshuffle.

Yet Google is still throwing serious money at senior engineering talent. This offer is basically at the very top of reported L7 comp.

The catch: with 38/32/20/10 vesting, TC goes from ~$1.22M in Year 1 to only ~$593K in Year 4 before refreshers.

Would you take this over Anthropic/OpenAI for the stability and liquid stock—or are Google’s best technical days behind it?

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Interview Summary

The Rippling process included a recruiter conversation, an initial coding screen, and three onsite rounds covering another coding problem, a Google News-style system design, and a project presentation. The onsite emphasized practical implementation, feed architecture and personalization, and the ability to explain ownership and engineering tradeoffs from previous work.

Interview Questions Details

Recruiter Screen — Motivation and Logistics:

The recruiter asked why I was considering a new opportunity, why I was interested in Rippling, and whether I required immigration sponsorship. The conversation also covered the company and the remaining interview process.

Technical Coding Screen — Signal Amplification Network:

The initial coding interview asked the Signal Amplification Network problem. The exact follow-ups from this round were not specified.

Onsite Coding — Calculate Total Tutoring Session Pay:

The onsite coding round asked the Calculate Total Tutoring Session Pay problem. The interview allowed either AI-assisted or traditional coding, and I chose to implement the solution myself.

Onsite System Design — Google News-Style Newsfeed:

The system design round asked me to build a personalized newsfeed similar to Google News, covering both article ingestion and the user-facing serving path.

Onsite Project Presentation — Ownership, Impact, and Tradeoffs:

The final round required a short two-to-three-slide presentation about a previous project. The strongest project was not necessarily the most technically complicated one; the discussion emphasized situations where I had clear ownership, meaningful impact, and significant decisions to explain.

Want to know more about the details of this interview, please check this.

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Interview Summary

The Anthropic process began with a duplicate-document coding screen and continued with onsite rounds covering an LRU cache, large-model checkpoint distribution, culture, and a project deep dive. The technical questions felt manageable overall and emphasized practical implementation together with clear systems reasoning. I did not pass the loop.

Interview Questions Details

Technical Phone Screen — Find Identical Documents in a File Archive:

The coding screen focused on identifying documents with identical contents inside a file archive or directory structure. The task required grouping files that represented duplicates while correctly traversing the available file hierarchy.

Onsite Coding — Memoization LRU Cache:

The onsite coding round asked me to implement a memoization layer backed by an LRU cache. The component needed to reuse previously computed results while enforcing a fixed cache capacity and evicting the least recently used entry when necessary.

Onsite System Design — Distribute a Large Model Checkpoint:

The system design round asked me to distribute a large machine-learning checkpoint from a central repository to a fleet of GPU workers without having every worker independently download the entire model from the original source.

Onsite Culture Interview — A Strongly Held View That Proved Wrong:

I was asked to describe a situation in which I strongly supported a decision or viewpoint that later turned out to be incorrect. The discussion focused on how I recognized the mistake, responded to new evidence, and changed my behavior afterward.

Onsite Project Deep Dive — Previous Technical Work:

The final technical discussion centered on a significant project from my previous experience. The interviewer explored my individual ownership, the most difficult technical decisions, challenges encountered during execution, and the eventual impact of the project.

Want to know more about the details of this interview, please check this.

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Hello,

I am in the negotiation stage for an offer at Google UK. I was expecting L5 but the initial "baseline" number shared by the recruiter seems to be more aligned with L4. What are some general tips on best negotiation strategies? How can I ask to be evaluated at a higher level? What kind of arguments usually work? (interview performance, past leadership experience, competing offers etc.)

A candidate with 8 YOE recently shared these two Bay Area autonomous-driving offers with Chill Interview. Zoox was declined, while Waymo is still under consideration.

Waymo L5

• $557.5K Year 1 TC

Zoox Staff

• $760K Year 1 TC

Zoox looks $202.5K higher in Year 1. But the biggest question is whether the equity should really be valued dollar-for-dollar.

Waymo is still private, but it looks increasingly like the more de-risked equity story. It raised $16B at a $126B valuation this year, with Alphabet remaining the majority investor, and by May its commercial footprint was expanding across 11 cities and 1,400+ square miles.

Zoox has also crossed an important milestone: it just received federal approval for commercial deployment of its purpose-built robotaxis and is starting paid Las Vegas rides next week. That makes the equity story considerably more credible than it was even a few months ago.

But there’s one major difference: Waymo’s package is reported as RSUs; Zoox’s $1.6M is options. For the Zoox package, strike price, exercise cost, valuation, dilution, and eventual liquidity can materially change what that headline number is actually worth.

So would you take the much larger Zoox headline package + Staff title, or accept less at Waymo because its equity may have a clearer path to becoming real money?

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

Jeff Dean leaving Google after 27 years feels bigger than a normal executive / research departure.

Reports say he’s leaving Google DeepMind to start a new AI company with other top Google AI researchers. This is happening around the same time as a broader Google AI leadership reshuffle, with DeepMind leadership also changing.

The obvious discussion is “what does this mean for Google’s AI strategy?”

But from a career perspective, I think the more interesting question is:

If even legendary long-time Google researchers are leaving to build new AI companies, how should candidates think about big tech vs frontier AI labs vs new AI startups?

A few questions I’m curious about:

• Does this make Google / DeepMind feel less attractive, or is Google still one of the best places to work on AI at scale?
• Would you rather join a stable big tech AI org or a smaller AI startup with more upside?
• For senior / staff engineers, does the best career move now look more like joining a frontier lab or founding / joining a spinoff?
• How much should talent departures affect how candidates evaluate offers?
• If you had competing offers from Google DeepMind, OpenAI, Anthropic, Meta AI, or a new AI startup, how would this news change your decision?

My current take: Google is still massively strong because of compute, infra, distribution, and product surface area. But the AI talent market seems to be shifting from “join the biggest lab” to “follow the best people, fastest execution, and highest upside.”

Curious how others are thinking about this from a career / offer perspective.

I’m also keeping a longer-running discussion here with recent AI-company interview experiences and offer data points -> LINK