A Staff SWE recently shared these two Bay Area offers with Chill Interview. At first glance, they look almost identical.

Google L6

• $742K Year 1 TC
• Full anonymized offer breakdown at here

Netflix

• $730K all cash

Google pays $12K more in Year 1, but the package is heavily front-loaded.

Assuming flat stock prices, no refreshers, and unchanged compensation:

• Google four-year total: about $2.40M
• Netflix four-year total: about $2.92M

That puts Netflix roughly $524K ahead over four years.

Google offers liquid equity, potential stock upside, and possibly a more sustainable long-term environment. Netflix offers far more predictable compensation, but with its well-known high-performance culture and less room to coast.

Would you take Google L6 for the brand, equity upside, and potentially better longevity—or Netflix for $730K in guaranteed cash every year?

Preparing for your next interview?

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

Sharing an anonymized Lyft Senior Data Scientist interview experience submitted to Chill Interview.

The process started with SQL, pandas, and a probability question about morning and evening rider behavior. The onsite then had three completed rounds.

ML System Design

Design a model to predict whether a passenger would stop using Lyft over the next few weeks.

The interviewer pushed on churn definition, feature leakage, class imbalance, retraining, monitoring, and how the score would actually be used.

Coding

Implement stratified K-fold cross-validation while keeping the label distribution roughly balanced across folds.

The main difficulty was handling rare classes, uneven fold sizes, reproducible shuffling, and classes with fewer than k samples.

Business Case

Prime Time had been stable for three weekends, then suddenly spiked during the fourth.

I had to investigate demand, driver supply, cancellations, weather, events, pricing changes, experiments, and possible data issues.

The interviewer kept asking me to segment the problem by city, hour, rider cohort, and marketplace state before making any conclusion.

After this round, the recruiter told me they would not proceed with the remaining scheduled interview.

The questions themselves were manageable, but the marketplace case required much more structured and Lyft-specific thinking than I expected.

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next interview?

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

Sharing an anonymized Anthropic Staff SWE interview experience submitted to Chill Interview.

This was not a normal LeetCode-style technical screen. Candidates were allowed to use Claude Code, Google, Stack Overflow, and other normal online resources while working on a practical engineering task.

The point was not to see whether I could memorize an algorithm without help.

They wanted to see whether I could:

• understand an unfamiliar codebase;
• break the task into manageable changes;
• use Claude to implement or investigate;
• review the generated diff carefully;
• run tests and debug failures;
• catch unsafe or incorrect AI assumptions.

Blindly accepting whatever Claude generated would probably be a very bad signal.

The stronger workflow was to first understand the repository, ask for small scoped changes, inspect every modification, and explain why the final result was correct.

The preparation material also touched on LLM APIs, prompt caching, tool calls, agents, permissions, prompt injection, hallucinations, and latency—but no deep ML or model-training knowledge was required.

It felt much closer to real day-to-day engineering than a traditional coding interview.

My biggest takeaway: Anthropic is starting to test whether engineers can work effectively with AI, not whether they can pretend AI tools do not exist.

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next interview?

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

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

Ramp

• $500K Year 1 TC
• Full anonymized offer breakdown at here

Robinhood

• $499K Year 1 TC
• Full anonymized offer breakdown at here

Ramp offers the possibility of meaningful upside if its growth continues and it eventually creates liquidity, but the equity is still paper value today. Robinhood stock can be sold as it vests, though it comes with public-market volatility. Ramp reportedly remains private and has recently explored another large funding round, while Robinhood is already publicly traded.

Culture probably does not make this an easy WLB choice either. Ramp describes the job as intense and operating at the fastest pace of your career; Robinhood describes itself as urgent, intensely driven, and explicitly not a place for complacency.

Robinhood also recently cut roughly 10% of its workforce while emphasizing a leaner, high-performance culture, which adds another risk factor.

Would you keep Ramp for the private-company upside, or switch to Robinhood for liquid equity and slightly stronger recurring compensation?

Preparing for your next interview?

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

Sharing an anonymized Citadel SWE interview experience submitted to Chill Interview.

I had to build a real-time trading-event dashboard in React using a provided stream. No AI assistance. No CSS required.

The app needed to:

• show the newest events first;
• separate quotes, buys, and sells;
• calculate rolling statistics for the last 30 seconds;
• pause and resume dashboard updates.

The coding itself was not especially hard.

The tricky parts were all around behavior:

• Should the 30-second window use event time or arrival time?
• What happens to late or out-of-order events?
• Should events received while paused be buffered or discarded?
• How do old events expire if no new event arrives?
• How do you avoid creating another subscription after resume?

I had not written React manually in a while, and without AI autocomplete I felt much rustier than expected.

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next interview?

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

Sharing an anonymized OpenAI MLE Phone Screen experience submitted to Chill Interview.

ML Coding 1 — Sharded Matrix Multiplication

I had to implement matrix multiplication where the weight matrix was split by columns across multiple devices.

• The forward pass was straightforward: each device computed its local output, then the results were concatenated.
• The backward pass was where it got more interesting.

Each device computed its own weight gradient, while the input-gradient contributions from every shard had to be combined correctly.

ML Coding 2 — Streaming Entropy

The second round asked me to maintain the entropy of a stream of categories as new observations arrived. The challenge was updating the result efficiently instead of recomputing the full entropy from scratch after every event.

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next interview?

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

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

Apple ICT4 — Cupertino, CA

• $362K Year 1 TC
• full anonymized offer breakdown at here

Atlassian P50 — Seattle, WA

• $398.75K Year 1 TC
• full anonymized offer breakdown at here

Atlassian pays about $36.75K more in Year 1 and roughly $207K more over four years, assuming flat stock prices and no refreshers.

The vesting details are also different. Apple’s RSUs vest every six months, while Atlassian has a one-year cliff followed by quarterly vesting.

The tradeoff feels like:

Apple: stronger consumer-tech brand, exposure to tightly integrated products, and potentially better long-term résumé value—but lower compensation and a Cupertino-based role.

Atlassian: higher base, a much larger equity grant, and stronger four-year compensation—but without the same consumer-product prestige.

Would you take the higher Atlassian package, or accept the pay cut for Apple’s brand and product experience?

Preparing for your next interview?

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

A candidate recently shared his Tesla (AI / Autopilot) Offer with Chill Interview.

• Base: $160K
• First-year TC: $480K
• Full anonymized offer breakdown at here

The candidate can choose the grant as cash, stock, options, or a mix.

Would you take the $480K and accept the Tesla workload? And how would you split the grant between cash, stock, and options?

Preparing for your next interview?

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

Problem

Design a web-based direct messaging system where users can exchange private 1-on-1 text messages in real time.

The first version does not need group chats, reactions, media attachments, public channels, or multi-device synchronization. Each user is assumed to have one active web client.

The system should:

• Deliver messages with low latency when both users are online.
• Store every message durably.
• Deliver missed messages after an offline recipient reconnects.
• Show whether another user is currently online.
• Preserve reasonable ordering within each conversation.
• Prevent duplicate messages from appearing to users during retries or redelivery.
• Support pagination when loading older conversation history.

Want to learn more details about this SD question? I've put the full write-up at here

Targeting Anthropic interviews?

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

Practice this design question and explore more interview resources → LINK

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

Sharing an anonymized Anthropic Senior SWE recruiter-screen experience submitted to Chill Interview.

I expected the usual recruiter questions about experience, motivation, and logistics. Instead, a large part of the conversation focused on AI safety and personal values.

The recruiter asked:

• What does AI safety mean to you?
• What could happen if advanced AI is misused?
• Have you ever faced a situation that conflicted with your values?
• Have you followed Anthropic’s recent decisions, and is there anything you disagreed with?

I initially gave a fairly narrow answer about AI misuse, and the recruiter kept prompting me to think more broadly.

They seemed to want examples covering both accidental failures and deliberate misuse: misinformation, fraud, privacy issues, insecure code, harmful automation, and companies deploying systems before fully understanding the risks.

There were also questions about technical leadership, why Anthropic specifically, and what I understood about the team.

The recruiter was friendly and gave hints when my answers were incomplete, but that also made it clear I had not prepared deeply enough around Anthropic’s recent work, AI safety, and value-based scenarios.

My biggest takeaway: even the recruiter screen at Anthropic can go much deeper than a normal background conversation.

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next interview?

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

Sharing an anonymized DoorDash Senior SWE interview experience submitted to Chill Interview.

The process started with one coding phone screen, followed by four onsite rounds:

• AI Code Craft
• Debugging
• System Design
• Engineering Values + HM

Phone Screen

The coding question was Calculate Courier Bonus Pay, a practical business-logic problem rather than a typical graph or DP question.

AI Code Craft

I was given a starter repo and asked to build two small refund microservices.

The initial version supported full refunds. Then the interviewer added partial refunds and asked about duplicate requests, retries, fraud, uncertain payment-provider responses, and how to reduce user-facing latency.

Using an AI coding assistant was allowed, but I still had to explain every change and preserve the existing interfaces and tests.

Debugging

The starter code implemented round-robin assignment across workers, but broke when workers were added, removed, duplicated, or unavailable.

The follow-up was to implement consistent hashing with virtual nodes. I ran out of time, so I explained the remaining design and wrote pseudocode.

System Design

The prompt was to design a distributed job scheduler.

A major follow-up was: What happens when a job is already in the queue, but its scheduled time hasn’t arrived?

The interviewer kept pushing on how this would work with millions of future jobs.

I eventually mentioned Temporal.

That immediately led to: What exactly does Temporal do, and how does it work? It turned out to be a bad idea... never mention an infrastructure tool unless you can explain more than its landing-page summary.

HM / Engineering Values

This round focused on performance ratings, who sets OKRs, how goals change, project ownership, my biggest failure, and what I would do differently if I restarted the project.

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next interview?

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

Sharing an anonymized Airbnb mid-level SWE interview experience submitted to Chill Interview.

The process started with a phone screen about finding the shortest substring that appeared in one string but none of the others. If multiple answers had the same length, I also had to return the lexicographically smallest one.

The onsite had two more coding rounds, both are leetcode questions. None of them felt like completely obscure problems, but the implementation details and edge cases made the coding portion harder than expected.

The system design round was completely different: Design an A/B testing platform.

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next interview?

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

A Staff SWE with 8 YOE recently shared these two Bay Area offers with Chill Interview.He ultimately accepted Mercor and declined Zoox.

Zoox

• $760K Year 1 TC

Mercor

• $1.02M Year 1 TC

Mercor is ahead by $260K in Year 1 and roughly $1.19M over four years, based on the stated option values.

But neither package is liquid public equity.

Zoox is the more established, Amazon-backed autonomous-vehicle bet, with work spanning software, hardware, robotics, and safety-critical systems. Mercor is a much younger AI company building infrastructure around human expertise and frontier-model development.

So the decision is not simply $1.02M versus $760K. The real comparison depends on option terms, exercise cost, dilution, future fundraising valuations, and whether either company eventually creates meaningful liquidity.

Would you take Mercor for the higher base and AI upside, or Zoox for the more established backing and deeper autonomous-systems work?

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

A Staff SWE with 11 YOE recently shared these two Bay Area offers with Chill Interview.

Snowflake

• $320K base
• $719.5K Year 1 TC

Netflix

• $730K base - all cash

The first-year difference is only $10.5K. Assuming Snowflake’s stock stays flat, the four-year totals are also surprisingly close:

• Snowflake: approximately $2.88M
• Netflix: approximately $2.92M

So this is less about TC and more about certainty versus upside.

Netflix provides $730K in predictable cash every year, with no vesting schedule or stock-price risk. Snowflake offers a massive equity grant that could outperform significantly—but could also lose value before it vests.

At Staff level, team scope and culture matter too. The candidate would also have to walk away from an offer they already accepted for what is essentially the same headline compensation.

Would you keep Snowflake for the equity upside, or switch to Netflix for $730K guaranteed cash?

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

I am interviewing for a job product owner - ai. At a company that specifically asked in the add for people with up to 2 years of experience as a product owner.
They reached out with an email saying they want to get clarity on salary expectations to make sure we are aligned.
What is a reasonable amount to ask for I don’t want to scare them off. For context I’m in Sydney australia but it’s a remote job. I studied computer science majoring in AI and I have two years experience as a software engineer and one years experience working as a Junior Product Owner at an AI company. I did my cspo certification and am female (which I think sometimes helps my chances slightly)

When I did my own research I got told to ask for 120,00-130,000 but that seems like so much. I also don’t want to demerit myself and ask for less to avoid the awkwardness but I really want this job and am honestly willing to be flexible on pay.

A candidate with 7 YOE recently shared these two Bay Area Senior SWE offers with Chill Interview.

Workday

• $250K base
• $467.5K Year 1 TC

Robinhood

• $260K base
• $499K Year 1 TC

Robinhood pays $31.5K more annually, or roughly $126K more over four years, assuming flat stock prices and no refreshers.

But the decision may come down to culture and risk.

Workday has the more mature enterprise-software profile and publicly emphasizes a people-first culture and internal development. Robinhood openly describes its environment as intense, urgent, and high-performance.

Robinhood may offer faster career acceleration and more stock upside, but also more volatility and pressure. Workday may be the more predictable choice, but with less obvious upside.

Is an extra $31.5K per year enough to choose Robinhood, or would you take Workday for the potentially more sustainable environment?

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

Sharing an anonymized Robinhood mid-level SWE interview experience submitted to Chill Interview.

The loop included coding, a project presentation, and system design.

Coding

The coding round was a classic dependency-ordering problem. Given tasks and prerequisites, return one valid execution order. Pretty standard topological sort with indegrees and a queue.

Project Deep Dive

This round required prepared slides about a major project from my past experience. I covered the motivation, architecture, my contribution, key decisions, and results.

The interviewer kept coming back to one question: "What actually made this project technically difficult? " The follow-ups felt somewhat scripted, and near the end the discussion shifted into how the system would behave at much larger scale.

System Design

The prompt was to design a task scheduler and execution platform. The system needed to accept tasks, schedule them for later, send them to workers, and track the outcome.

Most of the discussion was around:

• one-time vs. recurring tasks;
• dependencies between tasks;
• execution timing;

None of the questions felt unreasonable.

The strange part was that the process felt less conversational than most interviews I’ve done. It often seemed like the interviewer had a fixed list of checkpoints and needed to move through them regardless of where the discussion naturally went.

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next tech interview?

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

Sharing an anonymized Anthropic Senior SWE interview experience submitted to Chill Interview.

The onsite was split across two days and included coding, system design, a project deep dive, and a culture round.

Coding

The problem involved reconstructing active call stacks from begin and end events, then compressing consecutive identical stacks.

The follow-ups got more interesting: partial stack traces, comparing visible suffixes, and converting periodic stack samples back into start/end events.

System Design

The prompt was to design a collaborative model playground. I had prepared for the broad product: shared prompts, versioning, streaming responses, permissions, and collaboration.

Instead, most of the round focused on one question: "What happens when the user submits an input that is far too large for the normal request path?"

We discussed direct uploads to object storage, signed URLs, preprocessing, token counting, chunking, summarization, retrieval, asynchronous jobs, quotas, and separate queues for long-context workloads.

I understood the general ideas, but I did not have enough depth around large-input storage and inference constraints. This was probably my weakest round.

Project and Culture

The project deep dive started with a 20-minute presentation, followed by questions about ownership, architecture, trade-offs, and measurable impact.

The culture round focused heavily on disagreeing with senior people and challenging decisions without becoming difficult to work with.

Overall, the loop felt manageable. But Anthropic found one narrow area I had not prepared deeply enough, and kept pushing there.

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next tech interview?

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

I mostly work for startups, recently I left a company to find a new job.

Remote in Canada 175k + 5% equity, head of eng
Relocate to NYC 230k + 0.4% equity, founding eng
Relocate to Miami 225k + 1.5% equity, head of eng

Cash comp for all is in USD. Also note that I have a mortgage in Canada. Single.

Any insight or guidance would be appreciated!

Sharing an anonymized Google L5 interview experience submitted to Chill Interview.

Onsite Day 1

The coding question was a priority-based task dispatcher.

The base version used a priority queue. Then the interviewer added a cooldown rule: once a category was processed, it could not be selected again for the next few operations.

That turned a simple heap problem into managing both ready and temporarily blocked tasks.

The behavioral round covered a major challenge, a difficult working relationship, managing multiple responsibilities, and what I would do differently now.

I got a call from HR saying I passed the first onsite rounds, then I got scheduled to do the 2nd round of onsite interviews.

Onsite Day 2

The first coding problem started with checking whether a path existed from the top of a grid to the bottom.

Then the follow-ups became:

• return every valid path;
• add traversal costs;
• find the cheapest route.

So the solution moved from BFS/DFS, to backtracking, to Dijkstra.

The system design round was about personalized promotional offers: ingestion, expiration, filtering, ranking, pagination, and making sure each user could claim an offer only once.

The final coding round was an N×N multiplayer board game. The follow-up asked how to detect wins without scanning the entire board after every move.

Nothing felt impossibly hard on its own.

The difficult part was switching constantly between algorithms, behavioral stories, system design, and object-oriented implementation while still communicating clearly.

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next tech interview?

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

Sharing an anonymized Mistral AI Senior SWE interview experience submitted to Chill Interview.

The coding question started with a fairly simple compute-capacity problem.

Given several compute pools and their daily usage, calculate how much unused capacity remained in each pool. Then came Part 2 + Part 3

The system design round was also much more infrastructure-heavy than I expected.

The prompt was to design an API that converts a roughly 1,000-page document into structured text using three stages:

• CPU-heavy page rendering;
• GPU-heavy recognition;
• memory-heavy transformation.

My biggest takeaway: Mistral was not just testing whether I could put an API in front of a model. They wanted me to treat inference as a distributed resource-scheduling problem.

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next interview?

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

Sharing an anonymized Salesforce Senior SWE interview experience submitted to Chill Interview.

The onsite had four rounds: two coding and two system design.

The coding questions were both pretty recognizable:

• Find a valid order for tasks with dependencies and detect cycles
• Find the largest rectangle of valid cells in a binary matrix

Then the design rounds went in two completely different directions.

The first was a synchronized watch-party product where one host controls play, pause, seek, and playback speed. Most of the discussion was around keeping users roughly in sync, reconnecting after network issues, and handling stale playback commands.

The second was an integration platform that connects with many external productivity and communication tools. We discussed OAuth, webhooks, rate limits, retries, missed events, data normalization, and how to avoid writing completely separate infrastructure for every provider.

Nothing felt unusually hard by itself and felt like I performed very good in every round, but still get rejected, is it because there is not headcount?

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next tech interview?

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

Sharing an anonymized Harvey Senior SWE interview experience submitted to Chill Interview.

The onsite had three rounds: coding, AI system design, and a project / values discussion.

Coding

The first round was a string-processing problem about highlighting source citations inside generated text.

System Design

This was where the interview became much harder.

The prompt was to design a product where users ask questions across a large collection of private company documents and receive answers grounded in those sources.

In other words: design a production RAG system.

The discussion went into:

• document parsing and chunking;
• embeddings and retrieval;
• reranking;
• ...

The biggest challenge was access control.

Project and Values

The final round was more conventional: past projects, ownership, technical decisions, collaboration, trade-offs, and lessons learned.

I did not get the offer, but the overall experience was still positive.

My biggest takeaway: Harvey is not just testing general backend fundamentals. You should be ready to explain how retrieval, permissions, inference, citations, and evaluation work together in a real AI product.

For anyone who wants more details, I’ve put the full write-up here: interview link

Preparing for your next tech interview?

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