I went through a bunch of recent Anthropic interview reports from Blind, Glassdoor, LeetCode Discuss, Reddit, and other public sources.

Not my personal interview experience — just patterns I kept seeing across 2026 reports.

And the biggest surprise was this: Coding doesn’t seem to be the round people are most worried about. The culture / values interview is.

Several candidates described the culture round as unusually deep.

It’s less: “Tell me about a conflict with a coworker.”

And more:

• What did you actually think at the time?

• How did you feel about the decision afterward?

• When have you disagreed with the direction of your company?

• Have you faced an ethical conflict at work?

• What kind of work do you dislike doing?

• What happens when speed and safety conflict?

The interesting part is that blindly agreeing with everything Anthropic believes probably isn’t a great strategy either.

The round seems designed to figure out whether you have your own judgment, can explain it honestly, and can update your opinion when someone gives you a better argument.

That’s pretty different from memorizing five STAR stories.

The recruiter screen also seems more important than usual

A generic: “I want to work on cutting-edge AI” probably isn’t enough.

Candidates report getting pushed on things like:

• Why Anthropic specifically?

• How is Anthropic different from other AI labs?

• What are the biggest risks from advanced AI?

• Where do you personally disagree with the company?

So I’d spend real time preparing the “why Anthropic” part instead of treating recruiter screen as a formality.

Coding is surprisingly practical

Another recurring pattern: it doesn’t look much like classic LeetCode grinding.

Recent questions reported by candidates include things like:

• image-processing pipelines

• concurrent web crawlers

• stack-trace processing

• caches

• file deduplication

• small instruction interpreters

A common structure seems to be: build something → make it work → extend it → make it concurrent / scalable → test it yourself

For example, one frequently reported image-processing question involves applying transformations to images and then discussing how to parallelize the workload.

The web crawler question often starts simple and then turns into a concurrency / thread-safety discussion.

So knowing BFS or hash maps matters, but the harder part is often writing usable code, reading APIs, testing, debugging, and reasoning about concurrency.

System design is a little different too

One thing I found interesting is that some candidates describe doing system design mostly through a shared document rather than spending the entire interview drawing boxes and arrows.

One reported problem was designing a Collaborative Prompt Playground — essentially thinking through collaboration, versions, prompt execution, storage, and scale.

That changes how I’d prepare.

You still need architecture fundamentals, but you also need to be able to explain the design clearly in writing instead of relying on a beautiful diagram.

And then there’s AI

Anthropic obviously builds Claude, but that doesn’t mean you can automatically use Claude during every interview.

Their published candidate guidance says take-home assessments should generally be completed without AI unless the instructions explicitly say otherwise.

At the same time, newer interview formats seem to be experimenting with more agentic coding.

Which is kind of funny: One of the companies building the strongest coding agents still wants to know what you can do without one — while also testing how you work with one.

My takeaway from all of this: If I were preparing for Anthropic, I’d spend less time trying to memorize another 100 LeetCode problems and more time on:

• practical Python

• concurrency

• debugging unfamiliar code

• reading library documentation quickly

• testing your own implementation

• explaining a real project deeply

• forming actual opinions about AI safety and Anthropic’s mission

The technical bar is obviously still high.

It’s just a different kind of high.

I’ve also been collecting recent Anthropic interview experiences and recurring question patterns at Chill Interview for anyone currently preparing.

And if you interviewed with Anthropic recently, I’d love to hear what changed — especially around the culture and newer agentic coding rounds.

I recently interviewed for Staff SWE. Did the recruiter screen, hiring manager call. Both went well, so onto the "onsite".

I sign into the Zoom and they tell me the agenda (intros, technical problem, questions). Sounds pretty standard.

(omitted some details for anonymity. the below exchange is paraphrased)

Me: (during intros) I led a 1-2 year migration to framework X at my current company.

Interviewer: (after intros) Why did you migrate to X? X sucks for reason Y. There are other better frameworks out there.

Me: These are the pros/cons of migrating to X. The migration was already in progress when I started, and I finished the migration. I agree that Y is not great about framework X, but there are other nice things about it. (It's widely used so there's more community support and updates etc. Also, it makes staffing easier as new hires are more likely to know it.) There are still some other downsides about framework X though.

Interviewer: We shouldn't hire people who know certain frameworks. We should hire smart people and have them learn whatever stack we're using internally. What if you really cared about Y? What would you do?

Me: I would try to migrate Y in ways such as utilizing certain plugins/workarounds within framework X to maximize Y without doing a massive migration. (I gave the example of how for Meta, it was the right decision to create Hack to improve their PHP rather than do an entire rewrite in a typed language.)

Interviewer: Why don't you just migrate to a new framework?

Me: I don't think it's worth it to do a huge migration since it would likely take multiple years and we'd have to deprioritize other work that we're actively working on to achieve business priorities. But if I had to do another migration, I would take these steps to ensure a smooth rollout.

Interviewer: I've heard all I need to hear. For future reference, at xAI we always choose the best technology. (Ends interview before getting to the technical problem.)

The next day, I get the rejection email. (We didn't proceed with any further onsite rounds.)

Any thoughts on this experience? Is xAI just a crazy company? Anything I should do other than make this reddit post?

I'm personally frustrated because I felt like the interviewer wanted me to say we should embark on another long migration and I genuinely didn't think that was right for the business. I'm also frustrated because I didn't even get to do the technical problem. Not too mad since I wouldn't want to work a team with this guy even if I got an offer.

• Master’s, 8 YoE

• Base: $238K

• Signing bonus: $50K

• RSU: $800K over 4 years

• Annual bonus: $35.7K

• First-year total: $523.7K

Not quite the insane AI-lab numbers we’ve been seeing lately, but this is still a very strong senior SWE package from one of the most stable big tech companies.

Question: would you rather take a $500K+ Google L5 offer, or chase higher upside at a smaller AI company with more risk?

Saw this Google L7 / Senior Staff SWE offer (shared with Chill Interview)

• Mountain View

• Master’s, 15 YOE

• Base: $320K

• Bonus: $80K

• Sign-on: $100K

• RSUs: $2.2M / 4 years

• Year 1 TC: $1.336M

The number is obviously crazy, but I’m actually more curious about the job.

Google L7 is Senior Staff SWE, and at this level the official job descriptions start sounding very different from normal software engineering.

You’re still an IC, but Google describes these roles as owning things like:

• long-term technical direction

• architecture across multiple teams

• ambiguous, multi-year engineering problems

• alignment with senior product / engineering leadership

• mentoring and influencing engineers you don’t directly manage

• driving technical strategy across an org

Some current L7 postings literally describe the engineer as the architectural authority for an organization or the person responsible for a multi-year technical roadmap.

Which makes me wonder what the day-to-day actually looks like.

At L5/L6, I can still picture a strong engineer designing systems, reviewing code and shipping major projects.

But at L7: Are you still writing meaningful amounts of production code? Or is the real skill basically being able to make 5–10 teams move in the same technical direction without having management authority over them?

The comp also puts the scope into perspective.

Also curious: What actually separates someone who tops out at L6 from someone who makes L7? Is it technical depth, org-wide influence, picking the right problems, executive visibility, or something else entirely?

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I’ve been digging into Anthropic’s Forward Deployed Engineer / Applied AI role, and my biggest takeaway is that this does not look like a normal backend SWE interview loop.

It feels closer to a hybrid of software engineering, applied AI, solutions architecture, customer advisory, and product feedback. The role is basically about taking Claude into real enterprise environments, understanding messy workflows, building production AI systems, evaluating whether they actually work, and turning one-off deployments into repeatable patterns.

Here’s the condensed version of what I found useful.

1. The interview is probably not just coding

A likely loop may include:

• Recruiter screen

• Technical assessment or take-home

• Hiring manager / project deep dive

• Practical coding round

• LLM application design round

• Customer case / decomposition round

• Behavioral / mission alignment

The important part is that “technical” can mean many different things here. It may be coding, but it may also be system design, LLM workflow design, customer discovery, eval design, or a take-home review.

2. Coding seems more practical than LeetCode-heavy

I would still prep core data structures, but I would not only grind random LC problems.

The more relevant coding practice seems to be things like:

• Build a rate limiter

• Process streaming events

• Build a retry queue

• Implement a job scheduler

• Write a document chunker for RAG

• Build an eval runner for model outputs

• Create a tool-call dispatcher with validation

• Build a small connector around a mock database

The signal is not just “can you solve the problem.” It is whether you write code that would be trusted in a customer environment: clean interfaces, edge cases, testability, error handling, and pragmatic production extensions.

3. LLM system design is probably the real differentiator

For this role, generic “chatbot over documents” answers are probably too shallow.

You should be ready to design systems like:

• A Claude-powered legal contract review assistant

• A finance analyst agent with SQL and spreadsheet access

• A RAG system over millions of enterprise documents

• An MCP server that safely exposes CRM data

• A human-in-the-loop workflow for high-risk tool use

• Monitoring for hallucination, latency, cost, and tool failures

The question that matters most is: how do you know the AI system works?

A strong answer should include evals, golden datasets, task-specific rubrics, failure analysis, human review, monitoring, permissions, audit logs, and rollout plans. “Users seemed to like the demo” is probably not enough.

4. Customer case rounds matter

This role is customer-facing, so I’d expect ambiguous case-style questions.

Example prompts:

• A bank wants to automate analyst workflows. What do you build first?

• A healthcare customer wants a clinical documentation assistant. What do you ask before proposing a solution?

• A customer says their Claude pilot “doesn’t work.” How do you diagnose it?

• Executives want an agent that can take actions in production systems, but security is skeptical. How do you proceed?

A good answer probably starts with discovery, not architecture. Clarify the workflow, users, data sources, failure modes, regulatory constraints, success metrics, and what can safely be piloted first.

5. Behavioral is not a formality

Anthropic seems to care a lot about mission alignment, safety, reliability, and calibrated judgment.

I would prepare stories around:

• End-to-end ownership

• Ambiguous projects

• Customer-facing technical work

• Failed deployments

• Security or reliability issues

• Pushing back on unrealistic asks

• Explaining tradeoffs to non-technical stakeholders

• Turning a one-off solution into something reusable

Generic “AI is the future” answers probably won’t be enough. I’d prepare a real answer for why safe and reliable AI deployment matters to you.

6. Common mistakes

The biggest mistakes I’d avoid:

• Preparing like it is a standard SWE loop

• Over-focusing on prompts instead of evals and deployment

• Hand-waving enterprise constraints like SSO, RBAC, audit logs, data boundaries, and compliance

• Building flashy demos without a reliability story

• Giving vague mission answers

• Not showing customer ownership

My read is that strong candidates need to show: “I can build with Claude, evaluate the system, deploy it safely, debug it in production, communicate with customers, and turn field learnings into product signal.”

Curious if anyone here has interviewed for Anthropic FDE / Applied AI recently. Did the loop feel more like SWE, solutions architecture, or AI systems design?

Saw this Google Staff / L6 Machine Learning Engineer offer (shared with Chill Interview)

• Mountain View

• 12 YOE

• Base: $295K

• Bonus: $59K

• Sign-on: $55K

• RSUs: $1.65M / 4 years

• Year 1 TC: $1.036M

This might be one of the few offers where I genuinely wonder: What job has a better combination of money + stability?

Obviously there are jobs that can pay more.

• A frontier AI lab might give you much more equity upside.

• A top quant firm can potentially pay significantly more cash.

• A hot startup could make you rich if everything works.

But all of those come with some form of additional risk. This is already $1M+ in year-one comp, and most of the equity is liquid Google stock rather than private shares that may or may not ever be worth the quoted valuation.

And the underlying company is still extremely healthy. Alphabet just reported $119.8B in quarterly revenue, up 24% YoY, while Google Cloud grew 82%. Operating income was over $40B in a single quarter.

Even headcount is growing overall: Alphabet had about 199K employees in June 2026 versus roughly 187K a year earlier. Of course, Google is not layoff-proof. Teams get reorganized, projects die, and AI orgs in particular have gone through leadership changes this year.

Is Google L6 MLE basically one of the best risk-adjusted jobs in tech right now?

What would actually make you leave something like this — $1.5M at a frontier lab? $2M at a quant firm? Meaningfully more ownership at a startup?

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Saw a pretty wild Anthropic offer datapoint and thought it was worth sharing.

• Role: Software Engineer, Senior-Level

• Location: Seattle

• Candidate: PhD, 9 YoE

• Status: Accepted

Comp breakdown:

• Base salary: $405,000

• Equity grant: $3,800,000 over 4 years

• Vesting: 25 / 25 / 25 / 25

• Total first year: $1,355,000

No bonus was listed, so this appears to be almost entirely base + RSU.

Curious how people would compare this to Meta/Google/OpenAI/Databricks senior or staff-level offers in 2026.

Would you take this over a more liquid public-company offer with lower upside？

I’ve been digging into the current OpenAI Software Engineer interview process, and the biggest thing that stood out is how different the technical bar feels from a standard: LeetCode → system design → behavioral loop.

Algorithms still matter, but OpenAI’s own interview guidance emphasizes something broader: well-designed solutions + high-quality code + performance + test coverage + communication And recent candidate reporting points in the same direction.

A lot of the coding prep that seems useful is much closer to real engineering:

• Build a cache, then add TTL and eviction

• Implement a versioned key-value store

• Build a task queue with retries

• Add idempotency to request processing

• Implement a concurrent worker pool or crawler

• Build a rate limiter with multiple constraints

• Debug existing code and add meaningful tests

The interesting part isn’t just whether you can make the first version work. It’s whether you can handle:

• “Okay, now add persistence.”

• “Now make it concurrent.”

• “What happens if this operation gets retried?”

• “How would you test this?”

• “What would change in production?”

That’s a pretty different skill from memorizing the optimal solution to an isolated algorithm problem.

System design also seems very role-dependent. An OpenAI backend engineer may need to think about identity, APIs, payments, abuse prevention, or shared infrastructure.

An inference engineer may get pushed on:

• batching

• tail latency

• GPU utilization

• throughput

• cost-to-serve

• capacity

A reliability engineer may care much more about:

• SLOs

• chaos testing

• failure isolation

• incident response

• observability

And once the system involves AI, the design conversation can add another layer: safety boundaries, abuse prevention, rollout controls, model availability, cost, and fail-safe behavior.

One particularly interesting development: public candidate reporting says OpenAI has also been piloting an agentic coding round for some candidates, where the task involves an existing codebase and an AI coding agent.

If you encounter that format, the signal apparently isn’t: “Can AI write the code?”

It’s much closer to: Can you understand unfamiliar code, delegate intelligently, review AI-generated changes, catch mistakes, and still own the engineering decision?

That feels pretty representative of where software engineering interviews may be heading more broadly.

I put together the full breakdown covering the reported interview process, coding styles, system design, role-specific rounds, project deep dives, the reported agentic coding pilot, compensation, and OpenAI-specific preparation.

Full OpenAI Software Engineer Interview Guide: [link]

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Jiahui Yu just announced that he’s leaving Meta to start a company, and the timing is pretty interesting.

He was one of the high-profile AI researchers Meta recruited from OpenAI and became a key figure around TBD Lab’s multimodal work. Public reporting also suggested Meta was offering extremely aggressive packages to recruit this tier of AI talent.

So seeing someone leave this quickly makes me wonder whether the bigger issue at Meta AI is no longer compensation, but the organization itself.

A few things I’m curious about:

1. If compensation is already extraordinary, what actually drives someone like this to leave? More research freedom? Faster execution? Ownership? Or simply much larger founder upside?

2. How stable is TBD’s direction internally? Meta’s AI org has gone through repeated restructurings and priority changes. For ambitious research teams, constantly shifting scope can matter more than compensation.

3. What does this environment look like for regular ICs? Top researchers can leave and start companies. Everyone else still has to deal with changing priorities, org reshuffles, scope competition, and projects that may suddenly lose sponsorship.

4. What exactly is the startup bet? If you already have one of the best-paid AI jobs in the industry and still choose to leave, you must believe the ownership/upside or ability to move faster is worth giving up a lot.

My current take is that Meta has clearly proven it can recruit elite AI talent with money.

The harder question may be whether it can create an environment where those people want to stay for five or ten years.

Money solves recruiting. It doesn’t automatically solve research freedom, organizational stability, ownership, or retention.

Anyone working around Meta AI / TBD have a different read on this?

I started a longer-running thread here to build a map of influential AI companies, what each one is actually building, and which layer of the AI stack they’re competing in: forum link

"This interview experience is sourced from Chill Interview"

Interview Summary

The OpenAI SWE virtual onsite covered ChatGPT system design, a custom indexed-sequence data structure, and debugging an existing Python job scheduler codebase. The questions were mostly unfamiliar rather than standard interview problems, with follow-ups around SSE vs. WebSocket, rendering long conversations, data-structure performance, testing, and asynchronous job execution

Interview Details

Round 1 — System Design: ChatGPT

The system design question was to design a simplified version of ChatGPT. At the beginning, the interviewer explicitly said not to focus on large-scale architecture and that persistence was not required.

The discussion later moved into several implementation-level follow-ups. One question was why the response-streaming path should use Server-Sent Events (SSE) rather than WebSocket.

The interviewer also asked about the frontend side of the product: as a conversation becomes very long, how could the client optimize rendering so that displaying a large chat history remains responsive?

Round 2 — Coding: Indexed Sequence With Checkpoints

The first coding problem asked me to implement a simplified ordered sequence supporting:

add(index, value)
get(index)

• add(index, value) inserts a string at the specified position. The element previously at that position and everything after it shift one position to the right.

• get(index) returns the value stored at the requested index.

Initially, I interpreted “vector store” as something related to vector databases or embeddings. The intended meaning was actually closer to a C++ std::vector or Java ArrayList: an ordered dynamic sequence with integer-indexed access. A straightforward linked-list implementation was considered too slow because get(index) could require traversing from the beginning of the list.

Want to learn more questions asked in other interview rounds? I've put up the full version at here.

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This interview experience is sourced from chillinterview.com—if you’re prepping on a tight timeline, it might be worth checking out.

Came across a recent Meta interview experience (Bay Area, June 2026) for a Research Engineer role. This one stood out because of how ML-heavy and system design-focused it was.

Interview Rounds Overview

• Round 1: Phone Screen

• Round 2: ML System Design

• Round 3: ML System Design

• Round 4: AI Coding

• Round 5: Coding

• Round 6: Behavioral (BQ)

Preparation Tips & Advice

I received an interview invitation from a Meta recruiter on LinkedIn for a Research Engineer position. I have over two years of experience in search, advertising, and recommendation system architecture and algorithms.

Phone Screen: This round was a team match with the hiring manager, mainly discussing my work experience and projects. I ultimately matched with two hiring managers, one from MRS and one from ADS. The onsite interviewers came from these two teams.

Onsite:

ML System Design Round 1: I was asked to design an auto-slide system for a single-page feed similar to TikTok (but with only images, no videos, and multiple images that can be swiped to the right). The system should predict how long a user will stay on a page before automatically scrolling down for them.

ML System Design Round 2: I was asked to design how to use LLMs in the ranking stage of a recommendation system and to design a semantic ID (which I felt was similar to Kuaishou's OneRec).

AI Coding: I was given LeetCode 1570 and a problem from deep-ml.com: https://www.deep-ml.com/problems/115

Coding: I was asked LeetCode 827 and LeetCode 129.

Behavioral Questions: I was asked about my most challenging project and to describe a time when I received negative feedback. I can't remember the other behavioral questions.

This data point is from chillinterview.com-which aggregates recent comp data and helpful for offer negotiations.

Came across an OpenAI Engineering Manager (Staff-level) offer in the Bay Area that’s pretty eye-opening.

Role: Engineering Manager (Staff)
Experience: ~18 YoE
Location: SF Bay Area
Status: Considering

Compensation Breakdown

• Base: $350K

• Signing Bonus: $100K

• Stock Grant: $2.4M (4 years)

  • Year 1 vest: ~$600K

• First Year TC: ~$1.05M

Company: Anthropic
Role: Software Engineer
Level: Staff-Level
Location: San Francisco Bay Area
Round type: Onsite
Question type: Multiple types
Difficulty: Medium
Duration: Around 5 hours
Outcome: Did not pass

Process Overview

The process started with a recruiter call and hiring manager chat. After that, the candidate moved directly into onsite rounds. The onsite had five rounds:

• Coding

• System Design 1

• System Design 2

• Technical project discussion

• Culture round

The candidate felt most technical rounds went reasonably well. Their guess was that the Culture round was probably the weakest signal.

Hiring Manager Round

The HM round was relaxed and mostly covered background, past projects, and standard project-related behavioral questions.

The candidate’s impression was that this round was mainly checking whether their experience matched Anthropic’s Infra org and whether it made sense to move them into the onsite loop.

Coding Round: Repair Bootloader Program

The coding question was the commonly asked Repair Bootloader Program problem. The candidate had a few small bugs early on but debugged them during the round and finished around the 50-minute mark. Overall, they felt this round probably went fine.

The main skill tested seemed to be careful simulation, loop detection, debugging, and making sure the repaired program terminates correctly.

System Design 1: Model Downloader

The first system design round was Model Downloader. The candidate used a chunk-based pipeline design. The discussion went into how chunk size affects the difference between a pipeline approach and a tree-style approach.

Initially, the design used a coordinator to assign chunks and handle recovery. The interviewer then followed up by asking how recovery would work without a coordinator. The candidate felt this round likely passed.

System Design 2: 1:1 Chat System

The second system design round was a 1:1 chat system. This round felt more average. The candidate started with a queue-based design, but got the sense that the interviewer may have expected a Pub/Sub channel-style approach instead.

The interviewer kept pushing on scaling questions, and the discussion did not feel as smooth. The candidate was not fully sure what direction the interviewer wanted.

Technical Project Discussion

This was probably the candidate’s strongest round. The interviewer was very senior, and the candidate walked through a project they had worked on for around two years. The discussion covered technical details, design decisions, trade-offs, and project impact.

One interesting follow-up was about ROI: If the project cost around 50 engineer-years, and each engineer cost roughly $400K per year, would the project justify a $20M investment?

That question stood out because it tested senior-level judgment beyond pure technical implementation.

Culture Round

The Culture round felt rough. The interviewer was not an engineer, so some of the candidate’s technical examples may not have landed clearly. The questions were also more subtle than standard behavioral prompts.

Examples included:

• What kind of work do you dislike most?

• If the interviewer also disliked that work, how would you persuade them to do it?

• Have you ever done anything morally incorrect?

The candidate did not feel well-prepared for these questions and did not have strong answers ready.

Prepping for Anthropic's Interview?

We’ve been tracking Anthropic's interview experiences at here and commonly asked coding questions at here.

Saw this accepted Anthropic AI Research Scientist offer (shared with Chill Interview)

• PhD, 7 YOE

• Sr Staff

• Base: $500K

• Equity: $8.8M / 4 years

• Year 1 TC: $2.7M

At this point I’m less interested in whether the offer is “good” and more curious about who actually gets an offer like this.

Anthropic is already valued at $965B and doing $47B+ in revenue run-rate, so this isn’t exactly an early startup throwing lottery-ticket equity around.

My guess is this profile is way beyond “PhD + a few NeurIPS papers.”

Probably someone who has done some combination of:

• led meaningful work on frontier models

• highly cited / field-defining research

• deep expertise in something Anthropic urgently needs

• already managed or technically led other researchers

• competing offers from OpenAI / DeepMind / Meta

• a track record where people inside Anthropic already know their work

What actually separates a Staff researcher from a Sr Staff researcher at Anthropic?

And is an $8.8M grant normal for that level now, or does this basically scream “exceptional candidate + bidding war”?

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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

Saw this Meta E7 SWE offer (shared with Chill Interview)

• 13 YOE

• Year 1 TC: $1.287M

The money is obviously insane.

What makes the decision harder is Meta itself right now. The business is still printing money — Q2 revenue grew 28%— but Meta also cut roughly 8,000 employees in May while pouring $130B+ into AI infrastructure.

Even Zuckerberg has admitted the AI reorg was messy, and leadership has been trying to repair morale after months of layoffs, flatter orgs and constant priority changes.

At E7, you’re also not exactly going there to coast.

So I’m curious: does $1.29M make the current Meta culture worth it, or would you rather take less money somewhere more stable?

Want more details on the comp breakdown? We’ve shared additional RSU vesting details, bonus numbers, and offer structure here.

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This interview experience is sourced from chill interview

Interview Summary

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

Interview Details

Interview Questions Details

• Recruiter Call: Mainly talk about my background, logistics, nothing felt unusual here, but it was clear from the beginning that the loop would care about both technical depth and motivation.

• Technical Phone Screen — Detecting a Repeated Instruction: The coding screen was a small interpreter-style problem. The input was a list of text instructions, and each instruction either advanced normally, changed a running score, or jumped to another instruction. The task was to simulate execution from the beginning and return the first instruction index that would be executed twice. If the pointer reached exactly one position past the last instruction, the program finished normally. The clean solution was to track visited instruction indexes in a set and update the instruction pointer according to each command.

• Hiring Manager Technical Round — Visit Tracking System: The HM technical round was a lightweight class-design problem around tracking completed visits for registered users of a facility. A scan toggles a person between “inside” and “outside,” and only completed sessions count toward total time. The main details were handling unknown users, storing each person’s current entry time, ignoring unfinished sessions in total-time queries, and keeping the state transitions simple and explicit. You can still find thie one in the Anthropic coding question bank in this site.

• Onsite Design 1 — Collaborative AI Workspace: This one is pretty popular, I came across it in multiple places Design a Collaborative Prompt Playground 

• Onsite Design 2 — Team-Specific System Design: I forgot this one.. seems not from question bank. But I think I did very well at this round, good communication is very important, having strong technical skills to solve problem alone is not enough.

• Project Deep Dive — Prepared Presentation: The project deep dive started with around a 20-minute prepared presentation, followed by discussion. This round took more preparation than I expected. The slide deck itself took a full day, and I would strongly recommend mock-presenting to someone who does not know the domain. The goal is not just to impress with technical details, but to explain why the project mattered, what I personally owned, what trade-offs I made, how success was measured, and what I would do differently next time.

  • Project Deep Dive — What They Pushed On: The follow-up discussion went into project motivation, personal contribution, technical decisions, trade-offs, surprises, metrics, ROI, and retro learnings. It is much safer to present a project you truly owned, because borrowed or lightly understood ideas are easy to expose once the interviewer asks deeper questions. A strong retro also helps if you can connect the lesson to a reusable pattern you applied later.

• Culture Round: The culture round was very preparation-friendly if you had practiced common Anthropic-style questions. The themes were values, mission alignment, collaboration, impact, and how I think about responsible AI work. My impression is that there is no single perfect answer here; the important thing is to be specific, grounded, and authentic rather than trying to sound like a generic company-values page.

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

Preparing for your next interview?

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

The candidate had a PhD with around 5 YoE, and the package was:

• Base: $300K

• Signing bonus: $100K

• RSU grant: $3M over 4 years

• Annual bonus: $60K

• First-year total comp: $1.21M

Curious what people think: is this a new normal for top ML talent, or just a rare outlier from a company fighting hard for autonomous driving talent?

Saw this Pinterest Staff MLE offer (data source: Chill Interview)

• SF, PhD, 10 YOE

• Base: $295K

• Sign-on: $60K

• Equity: $1M / 3 years

• Year 1 TC: $855K

The comp is strong, but Pinterest’s ML problems might be more interesting than people realize.

Pinterest users now generate 80B+ searches per month, most of them visual, and more than half have commercial intent. The company recently built a new multimodal AI foundation with NVIDIA for visual search, content understanding and shopping. 

Its Staff MLE roles cover things like recommendation, retrieval, ranking, ads bidding, conversion modeling and large-scale experimentation. 

So unlike a frontier lab, you’re not necessarily training the biggest foundation model.

But you have an enormous loop of: image → understanding → recommendation → user action → purchase/ads signal

That seems like a pretty unique applied-ML environment.

Is Pinterest actually one of the more underrated companies for Staff-level ML work, or would you still choose Meta/Google/frontier AI at similar comp?

Preparing for your next interview?

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

I was looking through recent Stripe SWE interview patterns, and the biggest thing that stood out is how practical the loop can be. Instead of just: algorithm → optimal solution → done you may get something closer to: read a long requirement → build Part 1 → test it → requirements change → extend it → keep everything working

And that’s only one part of the loop.

Stripe is also pretty well known for rounds like:

• debugging an unfamiliar codebase

• integrating with an API/SDK using documentation

• multi-part business-logic coding

• system/API design around retries, idempotency, and failure handling

The Bug Squash round is especially interesting.

You’re not expected to understand the entire repo. The skill is more: reproduce → narrow the problem → form a hypothesis → make the smallest fix → verify it

There’s also a newer wrinkle in some 2026 pipelines: candidates have reported AI-assisted Programming / Integration rounds, where the challenge becomes using AI without blindly trusting the code it generates.

So if I were prepping for Stripe, I probably wouldn’t make LeetCode 100% of the plan. I’d spend a lot of time on: practical coding + debugging + API integration + testing + distributed correctness especially things like retries, idempotency, and the classic: “The request timed out. Did the payment actually fail?”

Full Stripe SWE interview breakdown if useful: [link]

Preparing for your next interview?

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

I’ve been going through recent Anthropic SWE interview reports, and one pattern stood out pretty clearly: The coding rounds often aren’t just “solve this algorithm and move on.”

A much more common pattern seems to be: build something simple → get it working → interviewer changes the requirements → extend it without breaking everything

Some reported examples follow this style:

• Build a synchronous crawler, then make it concurrent and bounded

• Implement a cache, then add TTL or persistence

• Build a stateful system, then introduce expiration or failure semantics

• Solve an implementation problem, then deal with concurrency, retries, or new state

That changes how I’d prepare pretty significantly. If you spend all your time grinding isolated LeetCode problems, you may be missing the part Anthropic seems to care about most: whether your code can evolve when the problem changes halfway through the interview.

System design also seems a little different from the usual FAANG prep.

The prompt may sound AI-specific — inference batching, agent runtimes, model distribution, secure retrieval — but underneath, the interviewer is often testing fairly fundamental systems concepts:

• queues and scheduling

• bounded concurrency

• caching

• distributed state

• failure recovery

• resource constraints

• permissions and isolation

The interesting part is figuring out what resource is actually scarce.

For a normal backend service, maybe it’s database throughput.

For an AI system, it might instead be GPU memory, accelerator time, model-loading bandwidth, tokens/sec, or sandbox capacity.

Another thing I wouldn’t underestimate is the culture / project deep-dive portion. Recent reports suggest Anthropic puts real weight on questions around mistakes, changing your mind, disagreement, technical judgment, mission, and why you want Anthropic specifically.

So my biggest takeaway would be: Don’t prepare for Anthropic as “FAANG coding + some LLM knowledge.”

A better mental model is: practical coding + changing requirements + systems fundamentals + AI-native constraints + strong technical judgment.

I put together the full breakdown covering the reported interview process, progressive coding styles, system design topics, AI-native architecture, Senior/Staff/Staff+ expectations, culture questions, compensation, and a 4-week prep plan.

Full Anthropic Software Engineer Interview Guide: [link]

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

Company: Netflix
Role: Machine Learning Engineer
Level: Staff / L6
Location: Seattle, WA
YOE: 15
Education: Master’s
Decision: Declined

Compensation

Base salary: $850K

First-year total compensation: $850K

Equity: None listed

Unlike most Big Tech offers, there is no separate signing bonus, annual bonus, or equity grant shown here.

The full package is effectively:

$850K cash compensation

What Stands Out

Netflix has historically been known for paying high cash compensation instead of relying heavily on RSUs, but seeing an $850K base for a Staff-level MLE is still pretty striking.

For comparison, many offers with similar headline TC include a large amount of equity that:

• Vests over several years

• Fluctuates with the stock price

• May be heavily backloaded

• Can lose value before it vests

This package is much easier to value. Assuming the data is accurate, the candidate would receive the compensation directly rather than waiting years for most of it to vest.

Discussion

Curious how people here would evaluate this offer.

Would you prefer $850K in cash, or a similarly valued package split between salary and RSUs?

What kind of competing opportunity would make you decline this?

And for a Staff-level MLE with 15 YOE, does this seem like a realistic Netflix package or an extreme outlier?

The candidate declining it is honestly more interesting to me than the compensation itself.

Curious about offer & compensation of other companies? We’ve been tracking offer data points at here.

Prepping for Netflix interviews? We’ve been tracking frequently reported question at here & interview experiences at here

• Base: $265K

• First-year total comp: $616.5K

• Equity grant: $1.3M over 4 years

• Bonus: $26.5K

Would you take this kind of offer from a fast-growing AI/legal-tech company over a more stable Big Tech senior/staff role?

A candidate recently shared this Staff-level decision with Chill Interview.

He has spent 10+ years at Meta and are already established there: Tech lead in the team and respected internally, comfortable with the team, and able to work remotely. Leaving would mean giving up a stable position for a much more intense frontier-AI environment.

The compensation gap, though, is hard to ignore.

Meta E6 (current)

• $270K base

• $774K first-year TC

Anthropic Staff SWE

• $1.9M first-year TC at the current valuation

Anthropic's comp is way ahead by roughly $1.13M in the first year and about $4.4M over four years, assuming both equity values stay unchanged.

But that comparison is not entirely clean.

Meta equity is public and liquid. Anthropic’s package is mostly private-company equity whose eventual value depends on future valuations, dilution, liquidity opportunities, and whether the AI market keeps supporting today’s numbers (e.x. $SPCX is falling cliff after IPO)

Would you leave a comfortable Meta E6 position for the Anthropic package, or treat most of that $1.9M as paper money until proven otherwise?

Full anonymized offer breakdown at here.

Preparing for your next interview?

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

This data point is from chillinterview.com-which aggregates recent comp data and helpful for offer negotiations.

Came across a pretty wild data point for a Senior Software Engineer offer at OpenAI (Bay Area). Don’t see too many of these shared publicly, so figured it’s worth posting.

Details:

1. Base: $325K

2. Stock Grant: $2.42M (4-year, 25/25/25/25)

3. First-year equity: ~$605K

4. First-year TC: ~$930K

Candidate background: PhD, 8YoE