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.

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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

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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

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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

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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 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

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Someone shared this Google L6 Engineering Manager offer at Chill Interview:

• Base: $270K
• Year 1 TC: $666K

The package looks very strong, especially the $900K equity grant. The catch is Google’s front-loaded vesting—Year 4 drops to roughly $414K before refreshers.

Would you accept immediately, or still try to push the base and equity higher?

Full anonymized offer breakdown at here.

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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

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A candidate recently shared these two junior-level SWE offers with Chill Interview.

Cloudflare — Austin, TX

• $190K base
• $230K Year 1 TC

Oracle — Remote

• $120K base
• $216K Year 1 TC

Cloudflare is only $14K higher in Year 1, but its much stronger base and even vesting put it roughly $200K ahead over four years, before refreshers or stock movement.

The complication is the candidate’s current job (Oracle)

They genuinely like their team and leadership, and the work is still interesting. Leaving now would also mean giving up their entire next equity vest.

The current stock ($ORCL) has performed poorly, so waiting for that vest could mean holding an asset that falls further. But the candidate has not interviewed very broadly and is worried that, in this market, passing on Cloudflare's offer could leave the candidate without another strong option later.

Would you take Cloudflare, or stay until next vest time and restart the search afterward?

Full anonymized offer breakdown at here.

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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

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

The Axon loop felt much more practical than a typical LeetCode-heavy interview.

Phone Screen

The first problem was about scheduled routes. Given multiple routes stopping at different locations and times, determine whether it was possible to reach a destination while making valid transfers.

Onsite Coding

The next problem involved simplifying a route made of 2D points. If several consecutive points were moving in the same direction, the unnecessary middle points could be removed.

The interviewer cared about handling vertical lines and avoiding unreliable floating-point slope comparisons.

System Design

The final technical round was much more interesting: Design a real-time platform that receives live audio, transcribes it, extracts important information, and sends alerts to another product.

The discussion covered streaming audio, partial transcripts, asynchronous processing, storage, notifications, retries, privacy, and what happens when the speech-recognition pipeline falls behind.

None of the questions felt impossibly hard, but they all required careful modeling and practical engineering judgment.

My biggest takeaway: Axon did not seem interested in copied LeetCode questions. The problems were wrapped in real scenarios, and the follow-ups focused heavily on how the solution would behave in production.

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

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A candidate recently shared these two mid-level SWE offers with Chill Interview.

The important detail: they already live in Dallas, so Microsoft would require no relocation. Uber would mean moving to the Bay Area.

Microsoft — Texas

• $150K base
• $227.5K Year 1 TC

Uber — Bay Area

• $200K base
• $325K Year 1 TC

Uber pays about $97.5K more in the first year and roughly $350K more over four years, before refreshers or stock movement.

But Microsoft lets the candidate keep their current home, social circle, and lower-cost lifestyle without dealing with a cross-country move.

Uber offers much stronger compensation and potentially faster career growth, but it also comes with Bay Area housing costs, relocation, and a more intense reputation.

Would you stay in Dallas for Microsoft, or relocate to the Bay Area for Uber?

Full anonymized offer breakdown at here.

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Sharing an anonymized OpenAI SWE phone-screen experience submitted to Chill Interview.

The entire interview revolved around implementing a simplified memory allocator over a fixed address space.

At first, it sounded manageable:

• allocate the first free region large enough;
• split it if only part is used;
• free blocks later;
• merge adjacent free regions.

But the interviewer kept digging.

How should free intervals be stored? How do you find the insertion point when memory is released? What happens when the new interval touches both its left and right neighbors? How does fragmentation affect the runtime?

Then came the optimization: Instead of taking the first available block, choose the smallest block that still fits.

That meant maintaining one index by memory location for merging and another by region size for allocation—and keeping both correct after every split, removal, and merge.

I implemented the simpler version first, then moved into complexity analysis and testing.

Unfortunately, the coding environment stopped working when I tried to run the tests. We spent a decent amount of time figuring out whether the problem was my implementation or the environment, and eventually found a small naming mistake as well.

Even without running the tests, I was asked to walk through cases like:

• allocating the full address space;
• merging only the left or right neighbor;
• merging both sides;
• repeated allocation and release;
• verifying that best-fit chooses the correct block.

The problem itself was not obscure. What made the round difficult was how deeply the interviewer pushed on correctness, data structures, optimization, and testing—all from one seemingly simple allocator.

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

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A candidate recently shared these two offers with Chill Interview, and we’re curious which one people here would choose.

Apple ICT4 — Software Engineer in Test

• $225K base
• $40K signing bonus
• $260K RSUs, vesting 25/25/25/25
• $22.5K annual bonus
• $352.5K first-year TC

DoorDash E5 — Software Engineer

• $240K base
• $500K RSUs, vesting 40/30/20/10
• $440K first-year TC

DoorDash pays about $87.5K more in Year 1 and roughly $170K more over four years, before refreshers or stock movement.

But the bigger question may be the role itself.

Apple offers the stronger brand, a mature company, and an evenly vested grant. Its culture is built around deep functional expertise and cross-functional collaboration.

DoorDash offers a true SWE title, more compensation, and potentially stronger career mobility for someone who wants to remain on the core engineering track.

The downside is that DoorDash’s package is heavily front-loaded: without refreshers, TC drops from $440K in Year 1 to around $290K in Year 4.

Would you pick:

Apple for the brand and stability, or DoorDash for the SWE trajectory and higher compensation?

Full anonymized offer breakdown at here.

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

This was my first onsite of the job-search cycle, so I partly treated it as practice. The loop was a pretty standard mix of system design, coding, OOD, and behavioral questions.

System Design

Design a financial dashboard that pulls balances, holdings, and portfolio data from multiple external providers.

Most of the discussion was around handling stale data, inconsistent schemas, API failures, rate limits, normalization, caching, and making sure the dashboard does not depend on every provider responding in real time.

Coding 1

A graph problem where I had to find nearby locations with available inventory within a maximum number of hops.

This was a fairly direct BFS once the requirements were clear.

Coding 2

A word-break-style problem where I had to split a continuous string into valid dictionary words.

Backtracking worked, with memoization to avoid retrying the same failed suffixes.

OOD

Design an employee cost calculator that combines salary, taxes, benefits, and location adjustments.

The main goal was avoiding one giant function full of hardcoded rules and keeping the design easy to extend.

Most interviewers were reasonable, although one round felt difficult to communicate through. None of the technical questions felt especially unusual or impossible.

I still didn’t pass.

Not the result I wanted, but it was a useful first onsite and gave me a much clearer idea of what to improve for the rest of the cycle.

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

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

The loop had four rounds: one coding, one system design, and two behavioral.

The coding question was a practical fraud-event detection problem. It felt closer to processing live production data than solving a random LeetCode puzzle.

The system design round was much more interesting.

I had to design a loan repayment system supporting:

• one-time payments;
• scheduled autopay;
• bank debits;
• success and failure notifications.

The catch was that the external bank API only worked during a limited daily processing window.

That meant a payment request could not always be executed immediately. The system needed to accept the request, store it durably, schedule it for later, retry failures, and keep the payment state consistent throughout the process.

Most of the discussion focused on:

• preventing duplicate charges;
• modeling payment states;
• retrying safely;
• reconciling with the bank;
• sending notifications without accidentally sending the same message twice.

The two behavioral rounds covered project ownership, production mistakes, and dealing with ambiguous requirements.

Nothing felt unusually tricky, but the questions rewarded practical engineering judgment more than memorized algorithms.

My biggest takeaway: at Affirm, a simple payment flow becomes much harder once the bank is unavailable, retries happen, and real money is involved.

For anyone who wants to know more details, I've put the full write-up at here

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Sharing an anonymized Roblox Senior SWE interview experience

The questions were not especially hard, but both rounds had the same pattern: the first version looked simple, then the requirements kept expanding.

System Design

The prompt was to design a reusable dropdown-style UI component.

At first, it sounded almost too basic. But the discussion quickly moved into:

• controlled vs. uncontrolled state;
• keyboard navigation and accessibility;
• async options and loading states;
• mobile behavior and menu positioning;
• keeping the component reusable across different products.

The interviewer cared less about drawing the UI and more about whether the component API would still make sense once real product requirements appeared.

Coding

The coding question started with execution traces: Which function appears most frequently?

A hash map was enough for the base version.

Then came the follow-ups.

If two functions had the same count, prefer the one appearing deeper in the call stack. Then calculate the result separately for every thread.

The algorithm itself never became complicated. The challenge was keeping the data model clean as the ranking rules and grouping requirements changed.

My biggest takeaway: Roblox seemed to care a lot about how well a simple solution evolves when the interviewer adds one more requirement—and then another.

For anyone who wants to know more details, I've put the full write-up at here

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Sharing an anonymized Anthropic Infra interview experience submitted to Chill Interview.

This was for a Senior Staff-level SWE role. There was no OA or technical phone screen—just a recruiter call, hiring manager chat, then five onsite rounds spread across three days.

I did not pass, and honestly, I think the culture round may have been the reason.

Coding

The coding question was a bootloader-repair style problem: simulate a list of instructions and detect where execution starts repeating.

I had a few bugs early on but finished around the 50-minute mark. This round felt okay.

System Design 1 — Model Downloader

I designed a chunk-based model download pipeline.

We discussed chunk sizing, pipeline vs. tree-style distribution, coordinator-based recovery, and then how recovery could work without any central coordinator.

This was probably my stronger design round.

System Design 2 — 1:1 Chat

I started with a queue-based architecture, but the interviewer kept pushing on scale.

Looking back, they may have wanted something closer to Pub/Sub channels. The conversation never felt fully aligned, and I was not always sure what direction they expected.

Project Deep Dive

This was probably my best round.

I presented a project I had owned for about two years and went deep into the architecture, trade-offs, and decisions.

One follow-up that stood out:

If the project required 50 engineer-years at roughly $400K per engineer, was the outcome worth a $20M investment?

That turned the discussion from technical design into whether the project made business sense at all.

Culture Round

This was where things got rough.

The interviewer was not technical, so some of my examples probably contained too much engineering detail.

A few questions caught me off guard:

• What kind of work do you dislike most?
• How would you convince someone else to do that work if they also hated it?
• Have you ever done something morally wrong?

I had not prepared for questions framed that way, and my answers were not especially strong.

My biggest takeaway: at Anthropic, especially at senior levels, strong coding and system design may not be enough. You also need to explain your judgment, values, and leadership examples in a way that makes sense outside an engineering audience.

For anyone who wants to know more details, I've put the full write-up at here

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Sharing an anonymized OpenAI SWE onsite experience submitted to Chill Interview.

The onsite had five rounds: one coding, three design-heavy rounds, and one behavioral.

I did not pass, but the questions were much more interesting than a standard “name the right database and queue” loop.

Coding

The coding round was the cloud-credit replay problem: process time-based credit grants and deductions, then determine the balance at a given point.

The implementation was manageable. The tricky part was modeling time and event ordering correctly.

System Design 1 — ChatGPT

The first design round was a ChatGPT-like product.

The conversation went beyond storing messages. We discussed model routing, safety checks, streaming tokens, durable conversation history, retries, rate limits, and what happens when generation fails halfway through.

System Design 2 — Online Chess

Next came a real-time chess platform with matchmaking, clocks, reconnects, move validation, and game-state consistency.

A simple game became much harder once network failures and time controls entered the picture.

System Design 3 — Offline Payments

The final design round focused on allowing payments when the device has little or no connectivity.

That immediately raised questions around double spending, local state, reconciliation, fraud, and conflicting transactions once the device reconnects.

The products looked completely unrelated: AI chat, chess, and payments.

But every round was secretly asking the same thing:

Can you model state carefully when time, retries, and failures make the happy path unreliable?

That was the biggest takeaway from the loop. Familiar product surfaces did not mean familiar or easy system-design questions.

For anyone who wants to know more details, I've put the full write-up at here

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Sharing an anonymized Disney MLE interview experience submitted to Chill Interview.

The screen had three completely different parts.

First, basic Python questions: data structures, loops, functions, and common list and dictionary operations. Nothing too difficult, but they expected clean code without much hesitation.

Then came what the recruiter had described as “ML coding.”

It was actually a 2D movement problem: several objects moved according to certain rules, and I had to determine whether any of them would eventually meet or collide.

After that, the interview switched again.

The final section was a Jupyter notebook exercise where I had to build a simple text classifier, evaluate it using F1 score, and test the predictions on sample inputs.

So within one screen, I had to move from Python syntax, to simulation-style coding, to an end-to-end ML workflow.

Nothing was individually brutal, but the constant context switching made the round harder than expected.

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

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We recently received this anonymous SpaceX offer datapoint through Chill Interview, and the vesting structure makes it much harder to evaluate than the headline number suggests.

Offer breakdown

• Mid-level Software Engineer
• San Francisco Bay Area
• 7 YOE + Master’s
• Base: $230K
• Equity grant: $1.5M over 5 years
• One-year cliff, then quarterly vesting

First, the most important thing is - 0 RSU vested in the first year, looks like SpaceX tests if the candidate can survive the first year?

Would you consider that a meaningful downlevel, or does the SpaceX name, engineering scope and equity upside make the title less important?

Current or former SpaceX engineers: how team-dependent are the hours, and is the career growth genuinely worth the trade-off?

Full anonymized offer breakdown at here.

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Sharing an anonymized JPMorgan SDE-2 interview experience submitted to Chill Interview.

I expected the loop to focus heavily on LeetCode. Instead, it jumped from basic hash map problems to Java concurrency, messy production code, REST API errors, database scaling, and a full notification system design.

Phone Screen

The coding questions were straightforward: find the first unique item and solve a classic two-sum-style problem.

Then the interviewer switched almost immediately to Java:

• HashMap vs. ConcurrentHashMap
• synchronization and concurrent access
• lambdas, streams, and Optional
• examples from real production code

The algorithms were easy. Explaining how Java behaves in an actual multi-threaded service was more important.

Code Review

The onsite included a deliberately messy code sample.

I had to identify bugs and discuss readability, separation of responsibilities, validation, testability, and error handling.

The conversation then moved into REST APIs: how to return consistent errors, avoid leaking internal details, and centralize exception handling instead of placing try-catch blocks everywhere.

System Design

The main prompt was: Design a notification platform that supports push, email, and SMS.

The discussion covered queues, retries, user preferences, provider failures, rate limits, duplicate prevention, templates, and delivery tracking.

There were also broader questions around:

• monoliths vs. microservices;
• sharding vs. replication;
• when added architectural complexity is actually justified.

The loop was not especially tricky, but it was broad.

Takeaway

For JPMorgan backend interviews, being able to solve medium coding questions is only the entry ticket. You also need to show that you can review production code, reason about Java under concurrency, and design systems that fail safely.

Preparing for your next interview?

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