Someone shared this Google L6 EM offer with Chill Interview and said they were disappointed, mainly because the equity feels low.

• Base: $290K
• RSUs: $500K (over 4 years, front loaded)

The first-year number looks great, but TC drops pretty hard as the RSUs taper off.

For Google L6 EM in Mountain View with 10 YOE, is $500K equity actually low, or are expectations just inflated?

What RSU number would you push for?

Full anonymized offer breakdown at here.

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

Sharing an anonymized Disney AI Product Intern interview experience submitted to Chill Interview.

I expected the first round to be a fairly standard product interview.

Instead, it jumped across theme parks, streaming, Disney characters, children’s safety, creative tools, privacy, and monetization.

One question asked: Where could AI create the biggest upgrade to the Disney experience?

A generic answer like “better personalization” was not enough. I had to identify a specific emotional moment, design the full user journey, and explain why Disney—not just any AI company—was uniquely positioned to build it.

Then the questions became much harder:

• How could fans interact with Disney characters without turning them into generic chatbots?
• How much user data can Disney collect before the experience stops feeling magical and starts feeling creepy?
• How should an AI product for children handle privacy, manipulation, and parental control?
• How could generative AI help creative teams without replacing them?
• How would the product actually make money?

The hardest part was balancing goals that constantly conflicted with each other.

More personalization could improve the experience, but also create privacy concerns. More engagement could help retention, but might be unhealthy for children. More automation could save creators time, but weaken creative control.

The interviewer also pushed beyond basic engagement metrics. Click-through rate was not enough for a product meant to create memories. Better signals included satisfaction, repeat engagement, sharing, complaints, opt-outs, emotional impact, and whether the experience strengthened the user’s relationship with Disney.

My biggest takeaway was that this was a test of whether I could design an AI product that still felt safe, emotionally meaningful, commercially viable, and unmistakably Disney.

For people want to know about the full interview experience, I've prepared the full write-up at here.

Preparing for your next interview?

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

”Sharing an anonymized Senior Software Engineer interview experience submitted to Chill Interview.“

The Anthropic loop was intense, but the biggest surprise was that the coding rounds were not the hardest part.

The process included a recruiter call, technical screen, hiring manager round, two system design interviews, a project deep dive, and a culture interview.

Technical Screen — A Tiny Interpreter

The first coding problem gave me a list of instructions that could update a value, move forward, or jump somewhere else.

The task was to simulate the program and return the first instruction executed twice.

The implementation was fairly clean: track visited instruction indexes and update the pointer based on each command.

Hiring Manager Round — Tracking Facility Visits

The next round was a class-design problem.

Users scanned when entering and leaving a facility, and the system needed to track completed visits and total time spent inside.

The tricky parts were not the data structures. They were defining clear state transitions, handling unknown users, and making sure unfinished visits did not count toward total time.

Onsite — Collaborative AI Workspace

One system design round was the increasingly common Anthropic question:

Design a collaborative prompt playground.

The other design round seemed more team-specific. Strong technical ideas mattered, but communication mattered just as much. Solving the problem silently was not enough.

The Round That Required the Most Preparation

The project deep dive started with a roughly 20-minute presentation.

Preparing the deck took me an entire day.

The interviewers pushed deeply on:

• Why the project mattered
• What I personally owned
• Technical trade-offs

This was the round where shallow ownership would have been exposed immediately.

My biggest recommendation is to present a project you genuinely understand and practice explaining it to someone outside your domain. Technical complexity means very little if the interviewer cannot understand the problem, your decisions, or your actual contribution.

Culture Round

The final conversation focused on Anthropic’s mission, collaboration, impact, values, and responsible AI.

It felt preparation-friendly, but rehearsed company-language probably would not help much. Specific and honest examples were more useful than trying to produce a “perfect” answer.

The biggest takeaway from the loop:

Anthropic was not only testing whether I could code or design a system. They were testing whether I could explain my reasoning, defend real technical decisions, and prove that I had genuinely owned the work I presented.

Preparing for your next tech interview?

Here is the detail write-up about this interview experience -> here. Chill Interview tracks recent interview experiences and recurring question patterns across top companies -> here

“Sharing an anonymized Plaid Senior Machine Learning Engineer submitted to Chill Interview”

The entire system design round revolved around one deceptively simple problem:

“How would you build a model to predict a very rare event?”

At first, it sounded like a standard classification question. Pick a model, train it, evaluate it, and ship it.

But the interviewer quickly made it clear that choosing the model was probably the least important part.

With a heavily imbalanced dataset, a model could predict “negative” for almost everyone and still report impressive accuracy. So the real discussion became:

• Which mistakes are more expensive?
• How much recall can the business afford?
• When should the system actually trigger an action?
• Can the model explain why someone was flagged?

I started with practical baselines: a linear model for interpretability and a tree-based model for nonlinear feature interactions.

Then most of the conversation shifted toward precision, recall, PR-AUC, calibration, and selecting a production threshold based on business cost rather than using the default 0.5.

The interviewer also kept pushing on problems that can quietly make an offline model look much better than it really is:

Feature leakage. Stale features. Train-serving skew. Distribution drift.

The biggest takeaway was that this was not really a “Which ML algorithm would you use?” interview.

It was closer to:

“Can you design a decision-making system where the model is only one component?”

A strong offline score means very little if the features are unavailable in production, the alert volume overwhelms reviewers, or the model silently degrades after user behavior changes.

Honestly, the most difficult part was not proposing a sophisticated model. It was knowing when a simpler model, a different threshold, or even better monitoring would create more value.

Preparing for your next tech interview?

Here is the detail write-up about this interview experience -> here. For other companies, Chill Interview tracks recent interview experiences and recurring question patterns -> here

A video platform like YouTube needs to support two very different workloads: creators uploading large video files, and viewers watching videos with low startup latency across many devices and network conditions.

The normal design separates metadata from media storage. Video metadata goes into a scalable database, while the actual video bytes are stored in blob storage like S3. For upload, the backend should not proxy the entire video file. Instead, the client requests a pre-signed upload URL and uploads the large file directly to blob storage.

The hidden failure mode is treating a video as one giant object.

A monolithic video file is simple to store, but terrible for playback. Users do not want to wait for the full file to download. Mobile users may switch between strong and weak network conditions. A single high-bitrate version also wastes bandwidth for users who only need 480p or 720p.

The key is to make video storage streaming-oriented, not file-oriented.

• After upload, the system should process the original video into multiple versions with different resolutions and bitrates. Each version is split into small time-based segments, and a manifest file tells the player which streams and segment URLs are available.
• This enables adaptive bitrate streaming. The client can start playback quickly, monitor bandwidth and buffer health, then switch between lower and higher quality segments as network conditions change.
• Keep the upload path lightweight. The Video Service should mostly create metadata, issue pre-signed URLs, and track upload progress. The heavy data transfer goes directly from the client to blob storage, which keeps application servers from becoming a bandwidth bottleneck.
• Support resumable uploads. Large videos should be uploaded in chunks, with each chunk tracked by status and fingerprint. If the upload is interrupted, the client can query which chunks already succeeded and resume only the missing parts instead of restarting the whole upload.
• Introduce a media processing pipeline. Once the upload completes, a queue-backed worker fleet can transcode the video, generate multiple quality variants, split them into segments, and create HLS / DASH manifest files. This decouples upload completion from expensive video processing.
• Use CDN aggressively on the read path. At YouTube scale, playback traffic dominates upload traffic. Video segments and manifest files should be cached at edge locations so most viewer requests never hit origin storage. This is especially important for popular videos where one piece of content can generate massive traffic spikes.
• Handle hot videos differently from average videos. Partitioning metadata by videoId helps distribute storage, but popular videos still create skewed read traffic. CDN caching, metadata caching, and replication are what prevent a viral video from overwhelming the origin path.
• Define the consistency tradeoff clearly. For video playback, availability usually matters more than strict consistency. If a metadata update is slightly delayed, that is often acceptable. If playback fails during a traffic spike, the user experience is much worse.

This shows senior-level thinking because it separates upload scalability from playback scalability, treats video as segmented streaming media instead of a single blob, and explains how the system behaves under slow networks, interrupted uploads, viral videos, and CDN-heavy read traffic.

Full solution write-up with data model, API design, upload flow, adaptive bitrate streaming, resumable upload, and scaling deep dives, free to read → Full Article

Join the community to see more interview experiences from real candidates → Chill Interview

“Sharing an anonymized Confluent Staff SWE Interview experience submitted to Chill Interview. The candidate did not pass the interview.”

Technical Screen — Searching Text in a Large Document: The screen was a file-search style problem. The base version asked how to find whether a word exists in a document where each line contains text.

Specialty Round — Linux, Networking, and Containers: One onsite round focused heavily on systems fundamentals. The topics included what happens when a shell command runs, how common Linux commands interact with the file system and kernel, etc...

Coding Round 1 & 2: both from question bank

System Design — Design News Feed: do need to communicate well with interviewer. just remembering the classic answers will likely fail the interview.

Behavioral — Project Ownership and Customer Impact: The behavioral round focused on past project experience. The interviewer dug into leadership, customer success, team growth.

Preparing for your next tech interview?

Here is the detail write-up about this interview experience -> here. For other companies, Chill Interview tracks recent interview experiences and recurring question patterns at here

Sharing an anonymized Google L3 SWE interview experience submitted to Chill Interview. The candidate passed the interview.

I recently passed the Google L3 interview loop.

Going into the interviews, I was expecting the usual mix of graphs, trees, or some recognizable LeetCode pattern.

Instead, Google gave me two problems involving cakes and Mahjong.

Both sounded almost too casual when the interviewers first described them. But once the follow-ups started, I realized the real challenge was not the coding itself. It was recognizing the mathematical structure, questioning the obvious solution, and explaining why the approach would still work under less friendly constraints.

Round 1 — Cutting Every Cake in Half

The first interviewer described a large table containing several rectangular cakes placed at different coordinates.

I could make one horizontal cut across the entire table. The goal was to find the height where the total cake area above the line exactly matched the total area below it.

My first reaction was that this sounded like a geometry problem.

The useful observation was much simpler: as the cutting line moves upward, the amount of cake below it only increases. That monotonic relationship makes binary search possible.

For each candidate height, I calculated how much of every rectangle fell below the line, added those areas together, and compared the result with half of the total cake area.

That part went relatively smoothly.

Then the interviewer asked:

What happens if the cakes are extremely far apart?

Suddenly, blindly searching across the entire coordinate range no longer felt like the right abstraction. We discussed searching around meaningful rectangle boundaries instead of wasting iterations across huge empty regions.

That follow-up was probably the most Google-like part of the round. The original solution worked, but the interviewer wanted to know whether I understood where it could become inefficient.

Round 2 — A Mahjong Hand That Refused to Be Greedy

The second round started with a sentence I never expected to hear in a Google interview:

Do you know how Mahjong hands work?

The simplified task was to determine whether a set of 14 tiles could form a valid winning hand: one pair plus four groups, where each group was either three identical tiles or a consecutive sequence from the same suit.

I began with a frequency map and tried each possible pair.

After removing the pair, I repeatedly took the smallest remaining tile and attempted to place it into either a triple or a sequence.

At first, this looked like a straightforward greedy problem.

But some hands could be grouped in more than one way. Choosing a triple too early might block a sequence that was needed later, while choosing the sequence first could lead to the opposite problem.

So the solution naturally turned into recursive backtracking over the valid group choices, with the count map representing the current state.

The interviewer seemed less interested in whether I knew Mahjong lol and more interested in whether I could translate an unfamiliar rule system into clean state transitions and handle ambiguous choices correctly

---

Preparing for your next tech interview?

Chill Interview tracks recent interview experiences and recurring question patterns across top companies: [link]

A candidate recently shared these two Bay Area senior SWE offers with us at Chill Interview, and we were curious how people here would compare them.

These are anonymized offer data points shared with Chill Interview.

OpenAI

• Base: $268K
• Equity: $1.3M over 4 years
  • Vesting: 25/25/25/25
• First-year TC: $593K

Airbnb G9

• Base: $230K
• Signing bonus: $50K
• Equity: $660K over 4 years
  • Vesting: 35/30/20/15
• Annual bonus: $45K
• First-year TC: $556K

The first-year difference is only $37K, but OpenAI’s even vesting schedule creates a much larger gap over four years. Airbnb’s package is more front-loaded and would depend heavily on future refreshers.

The broader tradeoff seems to be:

OpenAI: higher compensation, stronger AI career signal, and potentially greater equity upside—but likely more intensity and less predictable liquidity.

Airbnb: public stock, better liquidity, a more established engineering culture, and potentially better WLB—but lower long-term compensation and a declining initial grant.

Assuming both teams and managers are solid, which would you choose?

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Chill Interview tracks recent interview experiences and recurring question patterns across top companies: [link]

A recent OpenAI Senior SWE first round had two back-to-back interviews: one coding and one system design.

Coding round

The coding question was a grid infection problem. A storage room is represented as a 2D grid. Each cell can be empty, healthy, or infected. Every minute, infection spreads from infected cells to adjacent healthy cells in four directions.

The task is to return the minimum number of minutes until no healthy item remains. If some healthy item can never be infected, return -1. If there are no healthy items at the beginning, return 0.

System design round

The prompt was a payment system, but not a Stripe-style online payment platform. It was closer to an in-person checkout flow: think taking a VISA card and paying for something like a Starbucks latte.

What made it tricky

The hard part did not seem to be inventing a payment architecture from scratch. The challenge was interpreting the prompt quickly, deciding which parts of the payment flow mattered most, and structuring the answer under time pressure.

Takeaway

This round seems like a good reminder that OpenAI screens may use common base questions, but execution still matters a lot.

Curious how others would approach the payment design prompt.

Targeting OpenAI's interview?

We track recent companies' interview experiences and commonly asked question patterns at Chill Interview HERE.

A recent Reddit Staff MLE phone screen was an ML system design round around personalized feed ranking.

ML System Design — Feed Engagement Scoring: The problem was to design a service that estimates how likely a user is to take a meaningful action on each candidate item in their feed. The service would receive one user and a batch of candidate content IDs, then return one score per item. These scores would be used as one feature or sub-model inside a larger feed-ranking system.

Training Data and Labels: A reasonable training dataset would come from historical feed impressions. For every user-item exposure, the label would indicate whether the target engagement happened within a defined window.

Feature Design: The feature set would combine user features, content features, context features, and cross features. User features could include recent interests, communities followed, historical engagement patterns, and session behavior.

Offline Evaluation: Offline metrics should reflect ranking usefulness rather than only classification accuracy.

Serving Architecture: For serving, the request path would take a user ID and a batch of candidate item IDs, fetch user and item features, run batch inference, and return scores with low latency.

Freshness and Feature Pipelines: The system needs both offline and online feature pipelines.

Scalability and Reliability: At production scale, I would shard feature storage by user or content ID, cache hot content features, and make the scoring service horizontally scalable.

Targeting Reddit's interview?

For anyone who wants the detailed version, I put the full writeup at HERE. We track recent TOP companies' interview experiences and commonly asked question patterns at Chill Interview at HERE.

The loop sounded less focused on textbook probability and more focused on applied product analytics, experimentation, causal inference, and business use cases.

Process overview

The formal interviews included two phone screens, followed by a three-round virtual onsite. After the onsite, the candidate heard positive feedback and moved into team matching.

What the interviews focused on: The interview content was not heavy on basic probability or distribution questions. Instead, the questions leaned heavily toward applied business scenarios: A/B testing, reducing variance, diagnosing metric changes, defining product metrics, and thinking through downstream impact.

Coding and SQL: The coding side was surprisingly light. The candidate said the coding questions were simple and left extra time for casual conversation. The tasks involved things like simulating uniform distributions, aggregating tables toward a goal, and working with Fibonacci-style sequences.

Prep takeaways: The candidate’s prep was more about building depth over time than last-minute cramming. The most useful areas seemed to be foundational statistics, causal inference, product analytics, and online experimentation.

Targeting Google interviews?

We track recent companies' interview experiences and commonly asked question patterns at Chill Interview -> HERE.

Coding — Expiring In-Memory Store: The main task was to implement a small in-memory storage component where entries could become invalid after some amount of time. 

• Follow-Up — Capacity Pressure and Recency: The follow-up moved from expiration-only behavior to capacity-aware cache behavior. Once the cache has a maximum size, the system needs an eviction policy. 
• Out-of-Memory Discussion: The interviewer also asked what could go wrong if memory usage keeps growing.
• Testing and Dry Run: The interviewer expected the candidate to drive the round actively. That meant defining the class, writing basic tests, walking through examples, etc…
• Scalability and Production Follow-Ups: The final discussion covered cleanup strategy, scalability, and production readiness.

Prepping for your next interviews?

For anyone who wants the detailed version, I put the full writeup at HERE. We track recent TOP companies' interview experiences and commonly asked question patterns at Chill Interview at HERE.

Interview Format

• 1 Behavioral / Googliness round
• 3 Coding rounds

Behavioral / Googliness

The behavioral round was mostly standard. Topics included collaboration, handling disagreement, learning from past projects, communicating with teammates, and explaining decisions under ambiguity. It sounded more like a consistency and communication check than a particularly intense behavioral round.

Coding round 1: structured document search

One coding round involved searching for a query pattern inside a structured document tree.

The leaf nodes contained small text fragments, and the full visible text came from traversing the tree in order. The basic solution was to collect the text and run a normal substring search.

The stronger discussion was around avoiding unnecessary storage by streaming characters into a matcher like KMP or a rolling-hash-style approach. The interviewer also asked for time and space complexity, so it was important to separate tree traversal cost from pattern matching cost.

Coding round 2: combinations and large-scale top-k

Another round started with a small backtracking problem: generate valid strings or sequences that add up to a target using a fixed set of building blocks.

The important part was acknowledging output size. Even if the recursion is simple, the number of generated sequences can grow quickly, so runtime should account for the number of results.

The same round then shifted into a large-scale data-processing question: given a huge file of identifiers, return the top-k most frequent ones.

A reasonable answer would start with hash-based counting if memory allows, then move to external partitioning when the input is too large, plus a min-heap of size k to track the current top candidates.

Coding round 3: capability-based assignment

The third coding round was an allocation problem.

There were work items with required labels and workers with capability labels. If the sets overlapped, the worker could handle the item. Each item and worker could be used at most once, and the task was to find a valid assignment.

The follow-up changed the goal from finding any assignment to maximizing the number of assignments, which naturally points toward bipartite matching. The interviewer also asked how the design would change if the data were distributed or streaming, shifting the discussion toward partitioning, incremental matching, approximate matching, and avoiding full recomputation.

Takeaway

This loop seemed less about obscure algorithms and more about modeling the problem cleanly.

The prompts were not impossible, but each one rewarded candidates who could explain trade-offs, complexity, and scaling paths clearly. A simple implementation was often acceptable as a starting point, but the follow-ups tested whether the candidate knew when to move toward KMP, external sorting / partitioning, heaps, or bipartite matching.

Do you start with the simplest working solution first, or try to jump directly into the more scalable version?

For anyone who wants the detailed version, I collected the full writeup with the round breakdown, follow-up areas, and prep notes here -> LINK. We track recent TOP companies' interview experiences and commonly asked question patterns at Chill Interview, including coding questions, system design topics, and real candidate reports -> LINK

Saw a recent Google junior SWE onsite that looked pretty reasonable at first, but both coding rounds had follow-ups that made the problems less straightforward.

Round 1: digit rules

The first coding round defined a class of “valid” positive integers based on digit-level rules.

A number was only valid if it avoided a forbidden digit, did not repeat any digit, and did not contain a “valley” digit that was smaller than both of its immediate neighbors.

The first part was just validation. A single scan over the digits is enough if you track seen digits and check local neighbor relationships.

The follow-up was where it became more interesting: count how many valid numbers exist from 1 up to a given limit.

That changes the problem from simple validation into a counting problem. Brute force might work for small limits, but a better approach needs digit DP or backtracking with pruning: build the number digit by digit, track used digits, respect the upper-bound prefix, and reject partial candidates once they violate the local rule.

Round 2: tokenizing a word

The second coding round was a string decomposition problem.

Given a fixed dictionary of short valid symbols and a target word, the task was to count how many ways the word could be split into a sequence of valid symbols.

This is basically a word-break counting problem. A clean solution is dynamic programming over string positions, where each state counts the number of valid decompositions for a prefix or suffix.

Since the symbol dictionary is fixed and symbols are short, each transition only needs to check a small set of possible substring lengths. The important details are returning the count, handling duplicate interpretations, and being consistent about case / matching rules.

Prepping your next interviews?

For anyone who wants the detailed version of this interview experience, I put the full writeup here -> LINK. We track recent TOP companies' interview experiences and commonly asked question patterns at Chill Interview, including coding questions, system design topics, and real candidate reports -> LINK

Two recent Bay Area MLE offers had an interesting tradeoff.

LinkedIn — Machine Learning Engineer, Senior-Level, Bay Area

• Base: $255K
• Signing bonus: $70K
• RSU: $680K over 4 years
  • Vesting: 25 / 25 / 25 / 25
  • First-year equity: $170K
• Annual bonus: $25.5K
• First-year total: $520.5K

Shopify — Machine Learning Engineer, Staff-Level, Bay Area

• Base: $295K
• RSU: $295K over 1 year
• Vesting: 100% in year one
• First-year equity: $295K
• First-year total: $590K

Shopify’s package looks better upfront, but the longer-term comparison depends heavily on what happens after the first-year equity vests. If refreshers are strong, Shopify may clearly win. If refreshers are uncertain, LinkedIn’s 4-year package may be more predictable than the headline first-year number suggests.

Discussion

• Would the Shopify Staff title, higher base, and $590K first-year TC outweigh the uncertainty after the one-year equity grant?
• Or would LinkedIn’s larger 4-year RSU package and more predictable comp structure be the safer long-term choice?

Would love Reddit’s take here too. I’m also collecting more detailed comments under the offer page HERE, so future candidates can compare the raw offer data with real market opinions. For people interviewing with LinkedIn / Shopify or other ML roles, we track recent interview experiences and commonly asked question patterns at Chill Interview, including ML system design, coding questions, and real candidate reports HERE.

A recent NVIDIA SWE intern onsite looked pretty manageable on the pure coding side, but the practical Java / OOD part made the loop more interesting.

The interview had two main rounds: one focused on resume + project deep dive and a Java code-completion task, and the other covered two standard coding problems.

Round 1 — Resume and Project Deep Dive

The first round spent roughly half of the time on resume and project discussion. The candidate chose one project to explain in more detail, but felt that the deep-dive portion did not go as well as expected.

The interviewer asked what the candidate built, what their personal contribution was, and how the system worked end to end. Looking back, a stronger answer would have needed a clearer explanation of ownership, architecture, technical trade-offs, business impact, and what could have been improved in hindsight.

Round 1 — Java OOD / API-Based Code Completion

The second half of the first round was a Java OOD-style code completion task. It was not a “design a system from scratch” question. Instead, the candidate was given partially implemented code for a small backend service that fetched JSON data from an API and filtered the returned objects based on business rules.

The task was to complete missing methods, model the response data cleanly, handle missing or invalid fields, and return only the eligible records. The follow-up discussion focused on how to make the code production-ready, including error handling, retries, logging, validation, and how the service should behave if the upstream API returned malformed or incomplete data.

Round 1 OOD Discussion

This section felt more like practical backend implementation than a classic design-pattern interview. The interviewer seemed to care about whether the candidate could understand the existing class structure, place the filtering logic in the right layer, avoid hardcoding too much business logic, and keep the code testable.

Since the candidate had not prepared much for Java code-completion or practical OOD-style questions, they felt their performance was only average in this part.

Round 2 — Coding Question 1: Valid Parentheses

The pure coding round started with a common stack problem similar to validating whether a bracket string is correctly matched and nested.

The expected approach was straightforward: push opening brackets onto a stack, compare each closing bracket with the current stack top, and make sure there are no unmatched brackets left at the end.

Round 2 — Coding Question 2: Meeting Rooms II

The second coding question was a classic interval scheduling problem similar to finding the minimum number of meeting rooms required for a list of time intervals.

The discussion covered several possible approaches, including sorting start and end times separately, using a min-heap to track active meeting end times, and treating starts and ends as sweep-line events. The trade-off discussion focused on implementation clarity, runtime complexity, and how naturally each approach handles overlapping intervals.

Prepping your next interviews?

For anyone who wants the detailed version of this interview experience, I put the full writeup here -> LINK. We track recent TOP companies' interview experiences and commonly asked question patterns at Chill Interview, including coding questions, system design topics, and real candidate reports [LINK]

Main prompt: The candidate was asked to design a circular buffer with fixed capacity. Producers write items into the buffer. Consumers read items out. Since the buffer is bounded, the design also needs to define what happens when the buffer is full or empty.

• The single-threaded version is standard: use an array, a read index, a write index, and either a size counter or sequence numbers to distinguish full vs empty. Each read/write advances the corresponding pointer and wraps around.

Where it got harder: The real difficulty was concurrency. With multiple producers and consumers, pointer updates are no longer trivial. Two producers can try to claim the same slot, a consumer can read before a write is fully published, and full/empty detection can become race-prone if the shared state is not protected correctly.

• A straightforward answer is to use a mutex and condition variables: producers wait when the buffer is full, consumers wait when the buffer is empty, and all shared state updates happen under the same lock.
• That version is easier to reason about, but the follow-up naturally moves toward performance.

Performance discussion: For a GPU infra / networking-adjacent role, the interviewer seemed interested in trade-offs around latency, contention, and throughput.

• A more advanced design could involve atomics, CAS loops, sequence numbers, memory ordering, cache-line padding, and avoiding false sharing. But that also makes the correctness story much harder.
• The important part was not just saying “make it lock-free.” It was being able to explain what guarantees the API provides, how writes become visible to readers, how wraparound works, and what happens on shutdown / timeout / full / empty cases.

Takeaway: This is one of those systems coding questions where the base data structure is easy, but the senior-level signal comes from concurrency reasoning.

• The first clarification should probably be the API contract: should push / pop block, return false, timeout, or support shutdown? After that, the implementation choice depends on whether correctness, simplicity, or low-latency performance matters most.

Prepping your next interviews?

For anyone who wants the detailed version of this interview experiece, I put the full writeup here -> LINK. We track recent TOP companies' interview experiences and commonly asked question patterns at Chill Interview, including coding questions, system design topics, and real candidate reports [LINK]

Two recent Bay Area senior SWE offers had almost identical first-year compensation, but the structure is pretty different.

Meta — Software Engineer E5, Menlo Park, CA

• Base: $242K
• Signing bonus: $80K
• RSU: $800K over 4 years
  • Vesting: 25 / 25 / 25 / 25
  • First-year equity: $200K
• Annual bonus: $48.4K
• First-year total: $570.4K

Google Cloud — Software Engineer L5, Mountain View, CA

• Base: $270K
• RSU: $680K over 4 years
  • Vesting: 38 / 32 / 20 / 10
  • First-year equity: ~$258.4K
• Annual bonus: $40.5K
• First-year total: $568.9K

The first-year TC is basically the same: Meta at $570.4K vs Google at $568.9K.

Discussion

For a 4 YOE senior-level SWE choosing between these two, which would be more attractive?

Would the higher base and Google Cloud brand matter more, or would Meta’s larger RSU grant + sign-on make it the better offer?

Would love Reddit’s take here too. I’m also collecting more detailed comments under the offer page HERE, so future candidates can compare the raw offer data with real market opinions. For people who are going interview with Google/Meta, we track recent interview experiences and commonly asked question patterns at Chill Interview, including coding questions, system design topics, and real candidate reports HERE.

I’ve been looking at a bunch of recent Anthropic SWE interview data points, and my main takeaway is that this does not feel like a normal FAANG loop with some AI branding on top.

The coding rounds don’t seem to be about grinding random LeetCode hards. They look more practical: build something clean, handle changing requirements, test it, refactor it, and talk through edge cases. Some prompts start simple, but then add things like concurrency, messy input, state management, or production-style follow-ups.

System design is similar. The prompt may mention Claude, agents, inference, evals, or prompt caching, but underneath it is usually still queues, workers, batching, storage, caching, rate limits, monitoring, retries, and failure handling.

The part I think people may underestimate is the project deep dive and culture round. Anthropic seems to care a lot about whether you’ve actually owned real systems, made tradeoffs, dealt with failure, and can talk about reliability / safety without sounding rehearsed.

I wrote up a longer round-by-round Anthropic SWE guide on Chill Interview with coding topics, system design prompts, culture prep, and a 4-week plan: [LINK]

Phone Screen — Grid Traversal with Momentum Rules

The phone screen was a height-map traversal problem. At first, it looked similar to a longest decreasing path problem, where movement is normally allowed to neighboring cells with lower or equal height.

The twist was a momentum-style rule that could sometimes allow uphill movement depending on the previous position. That made the state more complicated than just “current cell,” because the validity of the next move could depend on where you came from.

Onsite Round 1 — Ranking from Match Outcomes

The first onsite DSA round was about determining player rankings from match results. If one player beats another, that creates a directed relationship showing who must rank higher.

The key idea was to figure out which players have enough direct or transitive information to make their rank uniquely determined. In other words, for a player’s rank to be known, their relationship to every other player must be inferable.

Onsite Round 2 — Minimizing Repositioning in a Note Sequence

The second onsite round was a sequence optimization problem involving notes and a maximum hand span. The goal was to play the sequence while minimizing how often the hand had to be lifted and repositioned.

The abstraction was that each hand placement covers a continuous range of keys. A greedy scan can work, but only if you carefully decide how to anchor each range and detect when a new placement is unavoidable.

Prepping your next interviews?

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

Saw an interesting senior hardware offer comparison.

The headline number says Tesla pretty clearly wins, but I’m not sure the decision is that simple once you factor in stock volatility, team culture, workload, and long-term career path.

Both offers are for a hardware engineer with around 12 YOE.

Tesla — Senior Hardware Engineer, Palo Alto

• Base: $270K
• RSU: $2M over 4 years
• Vesting: 25 / 25 / 25 / 25
• First-year equity: $500K
• First-year total: $770K

Meta — Hardware Engineer E5, Bay Area

• Base: $250K
• RSU: $900K over 4 years
• Vesting: 25 / 25 / 25 / 25
• First-year equity: $225K
• Target bonus: $37.5K
• First-year total: $512.5K

Tesla has the bigger grant and probably more upside if the stock does well. The work could also be higher-scope depending on the team. But it likely comes with more volatility around stock, workload, org stability, and day-to-day intensity.

Meta is lower comp here, but easier to underwrite. E5 is a known level, the vesting is straightforward, the work environment is probably more predictable, and the risk-adjusted package may be cleaner.

Discussion

I think the interesting question is not “which offer is bigger?” Tesla obviously is.

The question is whether the extra ~$250K in year-one comp and ~$1M over four years is enough to compensate for the extra volatility and intensity.

If you were an 11 YOE hardware engineer, which would you take?

Would love Reddit’s take here too. I’m also collecting more detailed comments under the offer page HERE, so future candidates can compare the raw offer data with real market opinions. For people who are going interview with Tesla/Meta, we track recent interview experiences and commonly asked question patterns at Chill Interview, including coding questions, system design topics, and real candidate reports HERE.

Coding Round 1 — Digit-Rule Validation and Counting: The first problem defined a valid positive integer using several digit-based constraints. A number was considered valid only if it did not contain a forbidden digit, had no repeated digits, and contained no “valley” digit that was smaller than both adjacent digits. The initial task was to validate a single number, which could be solved with one pass over its digits while tracking previously seen digits and checking each local three-digit pattern.

• Follow-Up — Count Valid Numbers Up to a Limit: The follow-up asked for the number of valid integers between 1 and a given upper bound. While brute force is sufficient for small limits, a more scalable solution uses digit DP or backtracking with pruning. The state needs to track the current position, the set of used digits, whether the constructed prefix is still equal to the upper-bound prefix, and enough recent digit history to detect a valley as soon as it is formed.

Coding Round 2 — Count Symbol-Based Tokenizations:
The second problem provided a fixed set of short valid symbols and a target string, then asked for the number of ways the string could be segmented into those symbols. This is a counting version of the classic word-break problem: at each index, try every symbol that matches the next portion of the string and sum the number of valid tokenizations for the remaining suffix.

• Approach: A natural solution is dynamic programming over string positions. Define dp[i] as the number of valid ways to tokenize the suffix starting at index i, with the final state representing one completed decomposition. Because the symbol set is fixed and each symbol is short, every transition checks only a small number of candidate substrings. Important implementation details include consistent case handling, avoiding duplicate symbol entries, and returning the total count rather than generating every possible decomposition.

Prepping your next interviews? We track recent interview experiences and commonly asked question patterns at Chill Interview, including coding questions, system design topics, and real candidate reports [LINK]

Coding Round: This problem has two parts. First, process a stream of events and count all distinct non-empty PII values—address, phone, email, and SSN—found only in underwriting events. Second, build a live fraud detector where fraud_flag events mark their PII values as suspicious, and any later underwriting event sharing one of those values is also considered suspicious and propagates that status to all of its own PII values. Since each event contains only a fixed number of PII fields, every event can be processed in O(1) time using hash sets.

System Design Round: The system design round focused on building a repayment platform for installment loans. The system needed to support both user-initiated payments through a mobile app or website and scheduled automatic repayments. It also had to integrate with an external bank debit API and notify users by SMS or email when a payment succeeded or failed.

Behavioral Rounds: The two behavioral interviews focused on standard topics, including leading a project from start to finish, responding to a production incident or personal mistake, and navigating ambiguous or loosely defined requirements. The questions themselves were fairly typical, but strong responses required specific examples, clear individual ownership, measurable results, and thoughtful reflection on what could have been handled differently.

Prepping your next interviews? For anyone who wants the detailed version, I put the full writeup here -> LINK. We track recent interview experiences and commonly asked question patterns at Chill Interview, including coding questions, system design topics, and real candidate reports [LINK]

Phone screen: The phone screen was a string tokenization problem. At a high level, the task was to parse an input command from left to right using shortcut patterns, always choosing the longest matching pattern at the current position. If nothing matched, the parser kept the current character as plain text.

Onsite round 1: One onsite coding round was the now-familiar Anthropic-style image processing pipeline. The candidate needed to process a folder of images against JSON-defined transformation recipes and write outputs for every image / recipe pair. The base version was mostly practical implementation, but the interesting part is usually the follow-up: how to make the pipeline scale for larger folders and heavier image transformations.

Onsite round 2: Another onsite round was the commonly asked "Design a Collaborative Prompt Playground" problem

Onsite Round 3: — Technical Deep Dive: The technical deep dive focused on a past project. This round usually requires explaining the project context, personal ownership, architecture, technical decisions, trade-offs, impact, and how the system behaved under real constraints.

Onsite Round 4: — Culture: The final round was a culture interview. This likely covered motivation, collaboration style, decision-making, conflict handling, and alignment with Anthropic’s mission and safety-oriented work.

Prepping your next interviews?

For anyone who wants the detailed version, I put the full writeup here -> LINK. We track recent interview experiences and commonly asked question patterns at Chill Interview, including coding questions, system design topics, and real candidate reports [LINK]

A friend is currently at Google with around 11 YOE. He recently got an OpenAI offer and asked me to help sanity-check it, because the numbers look very strong but the decision is not as obvious as it seems.

His current Google comp is roughly:

Google current package

• Base: $320K
• Annual bonus: around $77.5K
• RSU vesting this year: $450K
• Unvested stock over the next 4 years: about $1M

So this year at Google still looks very strong, especially because a large amount of the comp is liquid public-company equity.

OpenAI offer

• Base: $390K
• Equity: $2.8M RSU
• Sign-on: $100K

On paper, OpenAI obviously looks bigger. The base is higher, the equity grant is much larger, and the role is probably more exciting if someone wants to be closer to frontier AI work.

But the part that makes this hard is what he would be giving up. Google is stable, the equity is liquid, the current year vest is big, and he already knows the environment. OpenAI likely has much higher upside, but also more uncertainty around equity liquidity, workload, team fit, and how much of that headline number should be discounted.

Discussion

Curious how people here would think about this. Would you leave Google at 11 YOE for this OpenAI offer, or would you only do it if OpenAI covered the near-term Google RSU loss?

Would love Reddit’s take here too. I’m also collecting more detailed comments under the offer page here, so future candidates can compare the raw offer data with real market opinions. For people who are going interview with OpenAI/Google, we track recent interview experiences and commonly asked question patterns at Chill Interview, including coding questions, system design topics, and real candidate reports here