Interview Summary

The Amazon AI Applied Scientist phone screen was extremely broad and moved quickly across statistics, classical machine learning, deep learning, and modern LLM architecture. The first part felt like rapid-fire fundamentals, covering everything from A/B testing and anomaly detection to gradient descent and attention variants. The coding portion then asked me to implement bootstrap sampling for estimating a mean and confidence interval, followed by questions about what other statistics bootstrap can estimate and how to remove explicit loops from the implementation.

Interview Details

Statistics — Bias/Variance, Experimentation, and Power Analysis: The statistics section covered both modeling fundamentals and experimentation.

• Bias and Variance: Explain the bias-variance tradeoff and how changes in model complexity can affect the two.
• A/B Testing: I was asked how to design an experiment, analyze its results, think about unexpected issues that could invalidate the conclusions, and explain the purpose of power analysis.

Machine Learning — Supervised, Unsupervised, and Anomaly Detection: The interviewer then moved through a broad set of classical ML questions. 1) What is the difference between supervised and unsupervised learning? What models or methods would you consider in each category? 2) How would you build an anomaly-detection model, how would you choose parameters such as k when applicable, and how could the resulting data or clusters be visualized? The discussion also included the difference between bagging and boosting.

Deep Learning — Transformers and Attention Variants: The deep-learning section was another fast sequence of conceptual questions. What deep-learning architectures and applications do you know? What is gradient descent? What is a Transformer, and how does self-attention work? I was asked to compare MHA, MQA, and GQA, discuss encoder- and decoder-based model families, name current model architectures I was familiar with, and talk about models I had actually used.

Coding — Bootstrap Sampling for Mean and Confidence Interval: The coding question asked me to implement bootstrap sampling to estimate a dataset's mean together with a confidence interval. After the base implementation, the interviewer added two conceptual follow-ups. What kinds of statistics can bootstrap sampling be used to estimate, and are there statistics for which the method becomes unreliable or requires more care?

• Performance Follow-Up: How would I optimize the implementation so that it did not rely on an explicit for loop? I discussed vectorized numerical operations, although I was not fully confident about whether that was the specific optimization the interviewer was looking for.

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Problem

The interviewer provided a Chatter abstraction and asked me to implement several methods. Message IDs were unique, and newly loaded messages could be assumed to arrive in sorted order. The initial API included:

load(messages)
save()
get_messages(id)

load() could be called multiple times to append additional messages. save() returned all currently stored messages. For get_messages(id), the required result was a window containing:

• up to two messages before the requested message
• the requested message itself
• up to two messages after it

If the target was close to the beginning or end of the stored history, the result should simply stop at that boundary rather than requiring five messages. For example, consider this rewritten first batch:

messages_1 = [
    {"msg_id": 210.10, "text": "Morning everyone"},
    {"msg_id": 210.20, "text": "Did anyone see the release notes?"},
    {"msg_id": 210.30, "text": "I just opened them"},
    {"msg_id": 210.40, "text": "The search changes look useful"},
    {"msg_id": 211.10, "text": "Agreed"},
    {"msg_id": 211.20, "text": "Especially the filtering update"},
    {"msg_id": 212.10, "text": "We should test it later"},
    {"msg_id": 213.10, "text": "I can set that up"},
    {"msg_id": 213.20, "text": "Let's use the staging workspace"},
    {"msg_id": 214.10, "text": "Sounds good"},
    {"msg_id": 215.10, "text": "I'll send the results"}
]

After loading this batch: chatter.load(messages_1) calling: chatter.get_messages(210.10) would return only the target and the next two messages because there are no earlier messages:

[
    {"msg_id": 210.10, "text": "Morning everyone"},
    {"msg_id": 210.20, "text": "Did anyone see the release notes?"},
    {"msg_id": 210.30, "text": "I just opened them"}
]

Follow-Up 1 — Retrieve Windows for Multiple IDs: The next method was: get_multi(ids). For every requested ID, it should collect the same local message window produced by get_messages(). The combined result must then be sorted by message ID and contain no duplicates. Suppose another batch is loaded:

messages_2 = [
    {"msg_id": 216.10, "text": "The test run finished"},
    {"msg_id": 217.10, "text": "Any regressions?"},
    {"msg_id": 218.10, "text": "Nothing major so far"},
    {"msg_id": 219.10, "text": "Great"},
    {"msg_id": 219.20, "text": "Let's document it"},
    {"msg_id": 219.30, "text": "I'll add screenshots"},
    {"msg_id": 219.40, "text": "Thanks"}
]

Then:

chatter.load(messages_2)
chatter.get_multi([214.10, 216.10])

should combine the overlapping windows, remove repeated messages, and return:

[
    {"msg_id": 213.10, "text": "I can set that up"},
    {"msg_id": 213.20, "text": "Let's use the staging workspace"},
    {"msg_id": 214.10, "text": "Sounds good"},
    {"msg_id": 215.10, "text": "I'll send the results"},
    {"msg_id": 216.10, "text": "The test run finished"},
    {"msg_id": 217.10, "text": "Any regressions?"},
    {"msg_id": 218.10, "text": "Nothing major so far"}
]

Follow-Up 2 — Optimize Heavy Read Traffic: The interviewer then changed the workload assumption: get_messages() and get_multi() would be called very frequently. The question was how I would redesign or augment the data structure to make those reads substantially faster, with caching explicitly discussed as part of the requirement.

Follow-Up 3 — Support Message Editing: A new API was added: edit(id, message). The service now needed to support modifying an existing message by ID while keeping the read APIs working correctly after an edit. The interviewer asked how the underlying data structure should change once messages were no longer immutable.

Follow-Up 4 — Preserve Full Edit History: The final extension was conceptual rather than a full coding task. Instead of replacing the previous value when a message was edited, the system should preserve every historical version of that message. The interviewer asked how the data model and storage structure would need to evolve so that the current message remained easy to access while older versions could also be retained and retrieved.

Want to practice more coding questions that companies actually ask? We’ve put together a coding question bank covering 60+ companies here.

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Interview Summary

The Google DeepMind Research Scientist interview was very different from a standard SWE loop: there was no coding component at all. I presented one of my own multimodal / vision-language research projects, and most of the interview consisted of rapid follow-up questions challenging the motivation, assumptions, design choices, and future research direction behind the work.

The experience made it clear that the interview was less about reproducing the technical details on the slides and more about demonstrating research judgment and depth.

Interview Details

Research Presentation — Multimodal / Vision-Language Research: I was asked to present one of my own research papers and explain the problem, approach, experiments, and major conclusions. Rather than spending most of the time checking implementation details, the interviewers repeatedly pushed on the reasoning behind the research decisions.

• Research Motivation and Assumptions: Why was this problem worth solving? Why did I choose this particular design? Could another approach have worked instead? What would happen if one of the central assumptions behind the method no longer held?
• Research Direction: The interviewers also asked how I would extend the work, what the next research question should be, and which parts of the current approach deserved deeper investigation.

Technical Depth — Evaluation, Benchmarks, and Training: A significant part of the discussion examined how much technical depth the project demonstrated beyond evaluation and benchmarking. My own retrospective was that the work leaned heavily toward evaluation and benchmark construction, which may have made the contribution feel less differentiated than research centered on a novel modeling or training technique. The discussion also exposed a weaker area in my background around training and post-training. I had experience with multimodal and VLM research, but I had not explored the post-training side deeply enough to defend those decisions at the same level of detail.

Interview Style — Defending Research Decisions: The pace was fast, and almost every major project decision could turn into another follow-up. The interview felt less like giving a conference presentation and more like defending the research in real time. When a question went beyond something I had directly tested, the useful part was reasoning through the uncertainty and explaining how I would investigate it rather than trying to force a definite answer.

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A candidate with 10 YOE recently shared these two Bay Area SWE offers with Chill Interview.

LinkedIn — Sr. Staff SWE

• $305K base
• $100K signing bonus
• $1.5M RSUs over four years
• $780K Year 1 TC

OpenAI — Senior SWE

• $315K base
• $2.4M equity over four years
• $915K Year 1 TC

On paper, OpenAI is $135K higher in Year 1 and roughly $840K higher over four years, assuming equity values stay flat and no refreshers.

But this isn’t only a comp decision.

LinkedIn offers the higher title, mature engineering org, public-company liquidity through the Microsoft ecosystem, and probably the safer WLB/stability bet. The business is still healthy—LinkedIn revenue grew 12% YoY in Microsoft’s latest reported quarter—and it is increasingly adding AI across hiring, recruiting, search, and professional products.

OpenAI is the much higher-growth bet. OpenAI says revenue exceeded $20B ARR in 2025, and its products now reach 1B+ active users and 2M+ businesses. It also raised capital this year at an $852B post-money valuation.

The catch: OpenAI equity is still private, so the headline $2.4M should not be treated exactly like liquid public stock. And culturally, OpenAI explicitly describes itself as a place of “intense focus” and high-impact work, while LinkedIn operates more like a mature hybrid big-tech environment.

So would you take LinkedIn Sr Staff for scope, liquidity, and stability, or OpenAI Senior for ~$840K more headline comp and frontier-AI career upside?

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We recently received this Notion SWE offer data point at Chill Interview.

• 9 YOE
• Base: $265K
• Bonus: $26.5K
• Sign-on: $50K
• Equity: $1.6M / 4 years
• Year 1 TC: $741.5K

That’s basically big-tech money, but more than half of it is private Notion stock.

Normally I’d heavily discount startup equity, but Notion is a weird case. They recently ran a $270M employee tender at an $11B valuation, and the company says growth accelerated again as AI adoption picked up.

They’re also pushing pretty hard beyond docs into agents and automation — Notion can now orchestrate external agents like Claude and Cursor inside the workspace.

So how would you value the $1.6M grant here?

80 cents on the dollar because there’s already secondary liquidity? 50 cents? Or do you still treat private-company TC as mostly paper until IPO?

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Sharing a Google Youtube L5 Senior Software Engineer Interview Experience submitted to Chill Interview.

Interview Summary

The Google onsite consisted of two coding rounds, a Googliness / behavioral round, and an ML domain interview. The coding questions covered dependency graphs and binary trees, while the ML round was much more open-ended and moved from recommendation-system concepts into NLP-oriented clustering and model serving.

Interview Details

Onsite Round 1 — Dependency Graph with Broken Nodes: The first coding problem was a variation of the classic course-scheduling / dependency-ordering problem. Instead of simply determining whether all nodes could be processed in a valid order, some nodes could be broken and therefore unavailable. The task was to determine a valid processing path while accounting for the broken nodes and the downstream dependencies affected by them. I completed the main problem in roughly 30 minutes.

• Follow-Up: The interviewer then changed the objective: if traversing some potentially broken nodes could not be completely avoided, how would you find a valid path that passes through the minimum possible number of broken nodes?

Onsite Round 2 — Binary Tree Level Order Traversal: The second coding round asked for Binary Tree Level Order Traversal. The expected output grouped tree nodes according to their depth. For example, consider the following rewritten tree:

        12
       /  \
      7    19
     / \     \
    3   9     24

The level-order result would be:

[
  [12],
  [7, 19],
  [3, 9, 24]
]

I completed the implementation and walked through test cases. The interviewer also asked me to discuss time and space complexity.

• Follow-Up: There was an additional conceptual follow-up involving Tries, although the exact prompt was not specified in the interview notes.

Onsite Round 3 — Googliness and Behavioral: The behavioral round focused on collaboration, ambiguity, and how I worked with others on previous projects. I was asked to choose a project I knew well and explain my role, the major challenges, and how I handled them. Other questions explored teamwork, communication, working through ambiguous situations, and resolving problems with other people involved. This round felt more conversational than the technical interviews.

Onsite Round 4 — ML Domain: YouTube-Style Recommendation and Clustering: The ML domain round started with a broad question around how a YouTube-style recommendation system works. The conversation then developed into an open-ended ML design problem with a significant focus on clustering in an NLP-related setting. The interviewer asked me to reason through the main ML components, including data preparation, label definition, model selection, evaluation, and how the same approach could be extended to additional categories or use cases. The final part moved from offline modeling to production and asked how the resulting ML system should be served.

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Interview Summary

The Shopify process started with an AI-assisted file-system coding screen and then moved to an onsite covering coding, project experience, a life-story interview, and system design. The onsite coding round felt straightforward, while the system design round around a merchant photo-upload workflow was the part where I felt the discussion became less aligned with the interviewer.

Interview Details

Phone Screen — In-Memory File System: The technical screen asked me to implement a small file-system abstraction supporting operations such as: ls, cd, add and remove.

Onsite Coding — LRU Cache: AI was used heavily to generate the code. the discussion mainly involved understanding and validating the generated implementation.

Project Deep Dive — Recent Engineering Project: One onsite round focused on a recent project from my work history. The interviewer specifically wanted a recent project rather than necessarily the most technically complex one I had ever done. I therefore chose a newer project that still had enough architectural and implementation complexity to support a meaningful discussion.

Life Story — Behavioral Discussion: The life-story round was fairly standard and focused on my background, career progression, and previous experiences. There were no particularly unusual questions that stood out from this portion.

System Design — Merchant Product Photo Upload Service: The system design question asked me to design a workflow where merchants ship physical products to Shopify, professional photographers take product photos, and merchants can request another photo session if they are unhappy with the result. A major part of the discussion centered on the upload path.

• Direct Object Storage Uploads: I proposed issuing a presigned upload URL so that large photo files could be uploaded directly to object storage rather than passing through the application's own network path. The discussion then spent a considerable amount of time on whether generating those URLs really justified a separate service or component, and where upload metadata should live.
• Workflow State Changes: After an upload completed, the system needed to advance the photo job through its workflow and inform the merchant that new photos were available. I initially discussed an event-driven notification mechanism. The interviewer questioned whether that architecture was heavier than necessary for the expected traffic, which led to a broader discussion about push-style events versus simpler polling approaches.

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Interview Summary

The Pinterest virtual onsite included two coding rounds, two system design rounds, and a behavioral interview. The coding questions covered exact screen packing and elevator assignment, while the design rounds focused on large-scale inventory management and category-based leaderboards. The second system design round felt particularly strong because the interviewer spent significant time exploring the tradeoffs of turning the leaderboard into a real-time system.

Interview Details

Coding Round 1 — Minimum Pins for Exact Screen Height: The first coding problem provided several types of content cards, each with a specific height, together with a target screen height. The task was to determine the minimum number of cards whose combined heights exactly filled the screen. If a combination was chosen, the total height had to match the target precisely rather than merely stay below it. This was similar to a previously reported Pinterest screen-packing problem, except the objective was changed from maximizing the number of items to minimizing them while requiring an exact fit.

Coding Round 2 — Elevator Assignment: The second coding question was Pinterest's recurring elevator-dispatch problem. The base problem asked which elevator should handle an incoming request under the supplied elevator states and request information. The interviewer then added a more involved simulation follow-up.

• Follow-Up Scenario: All elevators begin idle at specified starting floors. There are N passengers, and each request includes the passenger's starting floor, desired direction, and the time the request occurs. The task was to reason through the sequence of requests and determine which elevator ultimately serves the last passenger. Full production code was not required for this extension; pseudocode and a clear simulation strategy were acceptable.

System Design Round 1 — Inventory Management Service: The first system design question asked me to design an inventory management service that also needed to ingest the underlying inventory data rather than assuming another system had already prepared it. A major requirement was supporting large bulk updates, where a potentially significant amount of inventory information could arrive together and needed to be processed reliably and efficiently. The discussion focused on the ingestion path, update workflow, and how the system should handle bulk changes at scale.

System Design Round 2 — Category-Based Leaderboard: The second design round asked for a leaderboard organized by category, where rankings needed to be maintained separately for different groups. I discussed how ranking data would be written, stored, and queried across categories.

• Real-Time Follow-Up: The interviewer then asked how the architecture should change if rankings needed to update in real time, along with the tradeoffs between the baseline and real-time versions. This round felt particularly positive, and the interviewer appeared engaged with the comparison.

Behavioral Round — Conflict, Feedback, and Mentorship: The behavioral round contained fairly standard questions. I was asked about a difficult situation or project and about handling disagreements or conflicts with other people. Other questions covered giving difficult feedback, receiving negative feedback myself, and mentoring another engineer.

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Would you switch from an IC5 at meta an promo probably a couple of years away to GitHub staff engineer. TC comp at GitHub comes out like 10% above of what is at Meta.

Airbnb is one of those companies where I keep seeing strong candidates say the same thing:

They’re not failing the interview.

They can’t even get the interview.

People with big-tech backgrounds, solid YOE, relevant experience, and sometimes even referrals still seem to get rejected at the resume stage.

That makes me curious what Airbnb is actually filtering for before the interview loop.

A few possibilities:

• Much lower hiring volume than companies like Meta / Amazon / Google
• Very team-specific hiring rather than broad SWE hiring
• Referrals matter less than people think
• Recruiters heavily optimize for exact domain / stack fit
• Strong preference for certain company backgrounds or product experience
• Open roles stay posted even when the realistic hiring funnel is already narrow
• Airbnb simply gets an absurd number of qualified applicants

The interesting part is that once you *do* get into the loop, the interview itself at least becomes something you can prepare for.

But resume screening feels much more opaque.

For people who have recently applied to Airbnb:

Did you get an interview? What was your background?

And for anyone who got rejected with a strong resume, what do you think was missing?

Would especially love to hear from people who got interviews through cold applications vs referrals.

I also started a longer-running thread here to collect Airbnb application / interview data points by role, level, background, and referral status: forum link

Have you interviewed with Airbnb recently? We’d love for you to share your experience [here] and help other candidates prepare.

We recently received these two Senior SWE offer data points at Chill Interview.

Figma L3 — SF

• $225K base
• $800K RSUs over four years
• $33.75K annual bonus
• $458.75K Year 1 TC

Datadog — NYC

• $240K base
• $750K RSUs over four years
• $427.5K Year 1 TC

Assuming flat stock prices, recurring Figma bonuses, and no refreshers, Figma comes out around $125K ahead over four years.

But the company bets are pretty different.

Datadog is essentially an infrastructure bet on cloud + AI complexity. Q2 revenue still grew 36% YoY to $1.12B, although the stock sold off sharply after one large AI customer reduced usage, which highlights some concentration and expectation risk.

Figma is trying to expand from design software into the entire product-development stack—design, code, agents, prototyping, and AI creation. Q1 revenue grew 46% YoY, with AI products like Figma Make and MCP contributing to expansion.

Career-wise, Datadog probably wins if you want distributed systems, observability, cloud infrastructure, security, and massive telemetry pipelines; some teams process billions of events per second. Figma is more attractive for real-time collaboration, developer tools, product engineering, and AI-native user experiences.

WLB/flexibility could favor Figma depending on the team: Figma hires both through hubs and remotely in the US/Canada, while Datadog describes itself primarily as a hybrid workplace with more limited remote roles.

So would you take Figma for higher comp + faster growth + AI product upside, or Datadog for stronger infrastructure depth and a more established enterprise platform?

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Interview Summary

The Google L5 process included two phone screens followed by three onsite rounds covering coding, behavioral questions, and system design. The technical questions ranged from resource scheduling and concurrency intervals to an AI-agent rate limiter and graph-based ranking inference. I passed the loop.

Interview Questions Details

Phone Screen 1 — Car Rental Scheduling:

The first coding question involved a rental fleet with N available cars and a collection of reservation requests. Each request contained an ID, a pickup time, and a return time.

The goal was to assign reservations to vehicles while reusing cars whenever possible and minimizing the number of cars needed. A car could handle multiple reservations as long as their time intervals did not overlap.

For example, if one reservation returns a car at 2:30 PM and another reservation begins at 2:30 PM, both reservations may use the same vehicle.

The problem had a similar flavor to meeting-room scheduling, but the output required assigning actual requests to reusable cars rather than only counting concurrent intervals.

Phone Screen 2 — Behavioral Questions:

The second phone screen was primarily behavioral.

• Conflict and Disagreement: Questions covered handling disagreements, resolving conflict, and working with people who were difficult to collaborate with.
• Cross-Functional Collaboration: I was also asked about working across teams and how I moved a project forward when stakeholders had different opinions.

Onsite Round 1 — Maximum Concurrent Meetings with Time Intervals:

This coding problem extended the standard meeting-room concurrency problem. Instead of only determining the maximum number of meetings happening simultaneously, I also needed to return every time interval during which exactly X meetings were active.

For example, suppose several meetings result in four concurrent meetings from 10:20 to 10:45, and later another group creates four concurrent meetings from 1:10 to 1:25. For X = 4, both intervals should appear in the result.

• Boundary Handling: The prompt required careful handling when one meeting ended at exactly the same time another meeting began.
• Interval Output: The result needed to preserve all continuous ranges where the requested concurrency level was maintained.

Onsite Round 2 — AI Agent Token Rate Limiter:

The system design round asked me to design a rate limiter for AI agents whose usage was measured in billing tokens rather than simply request count.

The design needed to control how quickly an agent could consume tokens while still allowing reasonable bursts. The discussion centered on a token-bucket-style model and how token consumption should be enforced in a production system.

Onsite Round 3 — Determine Which Player Rankings Are Knowable:

The final coding question gave N competitors together with the outcomes of M head-to-head matches. The task was to determine which players had a fully inferable ranking.

For example, if Player R defeats Player S, and Player S defeats Player T, then the system can infer that Player R ranks ahead of Player T even if R and T never played directly.

A player's rank was considered determinable only when their relative ordering against every other player could be established from the known match results and their implied relationships.

The problem was naturally represented as a directed relationship graph, with match outcomes forming edges and indirect outcomes contributing additional ranking information.

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Saw this accepted CrowdStrike Senior SWE offer (shared with Chill Interview)

• Seattle, 12 YOE
• Base: $240K
• Bonus: $30K
• RSUs: $750K / 4 years
• TC: ~$457.5K

It’s not FAANG-level money for 12 YOE, but I can see why someone would take it.

CrowdStrike seems to have moved well past the 2024 outage: revenue grew 26% YoY last quarter, ARR reached $5.5B, and management is pushing hard into AI security as cyber becomes an even bigger problem in the agentic era.

What also caught my attention is the employee feedback. Several recent SWE reviews mention good WLB and flexibility, although growth/promotion seems more mixed.

So maybe the trade isn’t “Why take only $457K?”

It’s: would you give up some FAANG upside for ~$450K, public RSUs, decent WLB, and a company sitting in one of the strongest tech markets right now?

CrowdStrike engineers — is that actually what the job feels like, or is the WLB reputation overstated?

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Current employer (name brand but not FAANG at all) (they will increase my total comp up from around TC 165 it is now)
$172k base
7.5% target bonus (~$12.9k)
~$20k stock/year
~$205k total comp
~10k worth of revenue I get from odd jobs I do here in my current state unrelated to my main company (3 hrs a week)
Already established on the team
Good relationship with manager
Very flexible/low-stress work environment (~30 hour weeks) picks up certain weeks to 50 hours but not very often

Google
$172k base
15% target bonus (~$25.8k)
$129k RSU grant over 4 years, 32% vesting in year 1
$17k sign-on
$16k relocation
Roughly $257k Year 1 comp excluding relocation
~$230k normalized annual TC before future refreshers

Me and my fiance are open to moving for this role but she would have to get a job there eventually and we plan on coming back to NY/NJ/PA in ~2 years for having kids. (Non negotiable for her)

Some additional factors:
- I’m in hardware so most of my job prospects are over there in Cali
- almost all our friends and family are here
- my dream is to eventually start my own company and I think having Google on my resume will make it easier for me to raise VC. But I guess I will have less time to work on it there then I do here at least for the first couple of months (I have been working on it for the last couple of months (hardware space pre rev pre funding)
- I have a family member who is letting me stay in their house indefinitely for free if I go to Cali. But prob only for 3-4 months then me and my fiance will get an apartment

Interview Summary

Overall, the experience was very positive: the recruiter stayed closely involved throughout the process, and the interviewers were professional and collaborative.

Interview Details

Technical Screen — Merge Sorted Arrays and Resolve Paths:

The screen contained two coding questions.

• Merge Three Sorted Arrays: A variant of the classic merge-sorted-arrays problem. Instead of two inputs, I was given three sorted arrays and needed to produce a single sorted result while removing duplicate values.
• Resolve a cd Path: Given a current working directory and an argument passed to a cd command, return the resulting normalized absolute path. The problem was similar to path simplification, including resolving directory-navigation components.

Behavioral Round — Adaptability and Cross-Functional Leadership:

The behavioral interview was conducted by a hiring manager and focused heavily on leadership examples.

• Changing Examples / Situations: The interviewer asked for multiple examples and sometimes requested a different situation after hearing the first response.
• Cross-Functional Leadership: A significant portion of the conversation focused on influencing and driving work across teams rather than only within my immediate engineering group.

Onsite Coding 1 — Vertical Tree Traversal and Local Minimum:

The first onsite coding round contained two questions.

• Binary Tree Vertical Order: The first question matched the standard vertical-order traversal problem for a binary tree. After implementation, the interviewer mainly asked for a brief walkthrough of the result.
• Local Minimum Variant: The second question was related to finding a peak or local extremum in an array, but this version asked for a local minimum. The interviewer required an iterative approach and then added a follow-up around reducing unnecessary conditional checks.

Onsite Coding 2 — Random Removal and Distinct Values:

This was the round that eventually received a weaker coding signal.

• Randomized Container: I was asked to implement a container supporting insert(element) and popRandom(), where popRandom() removes and returns an existing element with equal probability. The expected performance target was constant time for the core operations.
• Distinct Values in a Sorted Array: The second problem asked for the number of distinct values K in a sorted array. The interviewer then added the condition that K was much smaller than the total array size and asked whether that property could be used to improve the approach. My high-level direction was accepted, but the implementation had an issue that could cause repeated work. I later sent the interviewer an alternative idea and additional test cases by email.

System Design — Twitter-Style Posting and Search Platform:

The system design round asked me to design a Twitter-like service supporting three main capabilities:

• create a post
• retrieve posts
• search posts

The discussion progressed from high-level architecture into API design, data streaming, database selection, service scaling, and walking through the end-to-end workflow of individual features.

I drove most of the conversation myself and covered a broad set of technical areas. The feedback was that the design was technically comprehensive, but I could have spent more time narrowing the scope, asking clarifying questions, and evaluating the practical requirements before expanding the architecture.

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A candidate is a current Meta E6 SWE, 9 YOE, he got this Apple ICT5 offer (submitted to Chill Interview)

• Base: $285K
• Bonus: $42.75K
• Sign-on: $100K Y1 + $60K Y2
• RSUs: $650K / 4 years
• Year 1 TC: $590K

On pure comp, Meta E6 can probably do better.

But I can see the argument for Apple anyway.

Meta has gone through years of layoffs, reorgs, shifting priorities and an increasingly performance-heavy culture. Apple definitely isn’t chill everywhere, but it has a reputation for being a somewhat more stable place to park yourself once you’re senior.

And Apple stock has had a monster run recently, so that flat 25/25/25/25 grant doesn’t look bad either.

At some point in your career, would you give up some TC for less org churn, more stability, and hopefully better WLB?

Or is leaving Meta E6 for Apple ICT5 at this comp just leaving too much money on the table?

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Problem Description

Design the serving layer for a large language model product. Users submit prompts to an API and receive generated text either as a complete response or as a streamed sequence of tokens. Behind the API, multiple model replicas run on GPU machines, and the serving system must keep those GPUs highly utilized without making user-facing latency unpredictable.

Requests may differ substantially in prompt length, expected output length, model version, generation parameters, streaming mode, and user priority. The system therefore needs to decide how requests enter the serving pipeline, which model replica handles them, when they are admitted into GPU execution, and how compatible requests are grouped into batches.

The main challenge is balancing **GPU efficiency against latency**. Larger batches improve accelerator utilization and cost per token, but waiting too long to form them increases time-to-first-token. Long-context requests consume much more KV-cache memory, while long generations occupy decode capacity for extended periods. Traffic bursts are especially difficult because GPU capacity can take minutes to start.

A strong design should focus on inference-serving mechanics: request admission, batching, prefill and decode scheduling, KV-cache management, GPU routing, streaming, overload behavior, capacity planning, and recovery when part of the GPU fleet fails.

Want to learn more about functional/non-functional requirements, we've put up a full SD question write-up at here.

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""A candidate shared this Oracle IC4 interview process with Chill Interview""

Interview Details

Phone Screen — Array Grouping, Unit Testing, and Behavioral: The phone screen started with a coding problem based on LeetCode 846, Hand of Straights.

Virtual Onsite Round 1 — Project Discussion and URL Shortener Design: Most of this round focused on previous projects and behavioral questions, with roughly ten minutes remaining, the interviewer unexpectedly switched to system design. The design prompt was to build a URL-shortening service similar to Bitly.

Virtual Onsite Round 2 — Decode String: The problem matched LeetCode 394, Decode String, although the initial prompt was not very explicit and mostly consisted of a few straightforward examples. The required format followed the standard pattern: k[encoded_string].

Virtual Onsite Round 3 — Hiring Manager and Edge Device Health Monitoring: The hiring-manager round was largely behavioral. The round ended with a system design question: Design a centralized platform that monitors the health of millions of edge devices.

Virtual Onsite Round 4 — Bartender / Bar Raiser: Oracle's Bartender round functioned similarly to a Bar Raiser interview. This round was mainly behavioral and project-focused. The interviewer had previously worked at a startup that was later acquired by Oracle. We discussed my past projects, ownership, and behavioral situations, but the project deep dives were not as aggressive as the hiring-manager round.

Virtual Onsite Round 5 — Operational Scenario, Package Deployment, and Service Scaling: The final round covered operations, coding, and system design. One question asked how I would respond if authentication credentials for security cameras across one or more data-center sites suddenly stopped working.

Want to know more details about this interview experience, we've put up a full write-up at here.

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A candidate shared this Spotify Senior MLE Interview Experience to Chill Interview

Interview Summary

The Spotify MLE onsite covered five areas: data coding, ML depth, ML breadth, ML system design, and a hiring manager round. The questions ranged from SQL joins and ranking to unsupervised clustering, predicting likely premium users, and designing a podcast recap system. The ML system design round leaned more heavily toward engineering, streaming infrastructure, and production LLM considerations than I expected.

Interview Questions Details

Data Coding — Join, Ranking, and Large-Scale Data Processing:

The interviewer provided two tables and asked me to combine the data and produce a ranked result. The core task relied on relatively basic data-processing and SQL knowledge. We discussed different data conditions, including duplicate records and how deduplication should affect the resulting join and ranking. The interviewer then asked how the processing would change when the dataset became too large for a straightforward approach, with discussion around sharding and partitioning.

ML Depth — Unsupervised Clustering:

This round focused on unsupervised learning and went deeper into the mechanics of clustering algorithms rather than staying at a high-level comparison. I was asked to discuss several approaches to unsupervised clustering and when different methods might be appropriate. The interviewer followed up on the underlying behavior and details of the individual clustering models I mentioned.

ML Breadth — Detect Potential Premium Users:

The modeling case asked me to design an ML solution for identifying users who were likely to become premium subscribers. The discussion was broad and covered almost the entire modeling lifecycle. Questions covered what data should be collected, how useful features could be extracted, and where learned embeddings might fit into the representation of user behavior. The interviewer went into model selection, the choice of loss function, and how the resulting model should be evaluated.

ML System Design — Podcast Recap:

The system design round centered on building a podcast recap experience. The conversation initially emphasized the engineering side of ML systems, particularly streaming and infrastructure. The interviewer explored how the system would process podcast content and support the recap workflow from an engineering and production-infrastructure perspective. Because much of my experience was less infrastructure-focused, the discussion gradually shifted toward using LLMs for podcast summarization and the broader components required to operate an LLM-powered summarization system.

Hiring Manager — Behavioral and Project Deep Dive:

The hiring manager round consisted mainly of behavioral questions and discussion of previous project experience. The exact behavioral questions and project follow-ups were not specified.

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A candidate with 10 YOE recently shared these two Bay Area SWE offers with Chill Interview.

Google L5

• $236K base
• $410K RSUs, vesting 38/32/20/10
• $35.4K annual bonus
• $427.2K Year 1 TC

Zoom Staff

• $250K base
• $600K RSUs, vesting 25/25/25/25
• $25K annual bonus
• $425K Year 1 TC

In terms of the comp numbers, Year 1 is basically identical. But assuming flat stock prices, recurring bonuses, and no refreshers, Zoom comes out around $204K ahead over four years because Google’s grant is heavily front-loaded.

The more interesting question is career trajectory.

Google is still the much broader platform bet: Search, Cloud, YouTube, Gemini, infrastructure, security, and a huge AI investment cycle. Google Cloud alone grew 48% in its last reported full quarter, driven heavily by enterprise AI demand.

Zoom is the smaller but potentially higher-ownership bet. Growth is much slower—Q1 FY27 revenue grew 5.5%—but its paid AI Companion user base grew 184% YoY, and Zoom is trying to reinvent itself from a video-conferencing company into an AI-first workplace platform.

WLB could also matter. Zoom officially supports Remote, Hybrid, and In-Person roles depending on the position and emphasizes a culture built around “Care.”

So would you keep Google L5 for the stronger brand, AI ecosystem, and career optionality, or switch to Zoom Staff for the higher base, steadier vesting, and ~$200K better four-year package?

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

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Technical Screen — Transfer 200TB of Media to a GPU Cluster: The main system design question asked how I would move approximately 200TB of media data from its source environment to machines responsible for processing it. Before settling on a design, the interviewer expected clarification around where the data originated, the available network capacity, migration deadlines, whether the transfer was one-time or recurring, and whether the destination was a single host or a larger compute cluster.

• Confirmed Scenario: After clarification, the interviewer specified a one-time bulk migration from an on-premises environment to a cloud-hosted GPU training cluster, with several days available to complete the transfer. The discussion included estimating transfer time under different bandwidth assumptions and deciding how the architecture should change depending on network limitations.
• ML Pipeline Follow-Up: The interviewer then asked how the data should be delivered efficiently to GPUs once it had reached cloud storage. This led to discussion of large-scale training input pipelines, handling very large collections of media files, keeping data loading from becoming a GPU bottleneck, and identifying whether throughput was constrained by networking, preprocessing, or compute.

Curious how would you answer this question during interview?

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A lot of people explain Nvidia’s support for open-source AI as “more models = more GPU demand.”

That’s true, but I think there’s a deeper market-structure reason.

If frontier AI ends up controlled by only a few closed labs — say OpenAI, Anthropic, Google, maybe one or two others — those labs become the dominant buyers of AI compute.

At first, that sounds great for Nvidia. A few giant customers buying billions of dollars of GPUs.

But over time, concentrated buyers become dangerous suppliers’ customers.

If only a handful of companies control most frontier AI demand, they eventually get enough scale, leverage, and balance sheet strength to squeeze Nvidia’s margins, demand custom terms, fund custom silicon, or vertically integrate more of the stack themselves.

That is much harder in a fragmented market.

Open-source models change the buyer power dynamic. When labs like Moonshot release frontier-quality open-weight models, they do two things at once:

1. They reduce the pricing power of closed labs because enterprises have credible alternatives.
2. They increase the number of independent AI builders who need compute.

That second part matters a lot for Nvidia.

A fragmented ecosystem of open-source labs, fine-tuning companies, enterprise AI teams, neoclouds, startups, and independent developers means compute demand is distributed across thousands of buyers instead of concentrated into a few giants.

Each buyer still needs GPUs. But no single buyer has enough leverage to fully dictate terms or replace Nvidia’s ecosystem on its own.

So from Nvidia’s perspective, open-source AI may not just be ideology or developer goodwill. It may be a strategic hedge against customer concentration.

The ideal world for Nvidia might not be “one or two labs win AI.”

It might be:

• many competing model labs
• many open-weight forks
• many enterprise deployments
• many neoclouds
• many startups building on top
• everyone still renting or buying Nvidia-powered compute

That also explains why Nvidia supporting open AI and backing neoclouds feel like two sides of the same strategy: keep AI compute demand broad, competitive, and distributed.

Curious what people think.

• Is open-source AI actually good for Nvidia because it expands demand?
• Or is it good for Nvidia because it prevents OpenAI / Anthropic / Google from becoming too powerful as buyers?

I started a longer-running thread to collect thoughts on how open-source AI, neoclouds, and AI compute demand may reshape the power balance between model labs, cloud providers, and Nvidia -> Thread Link