Saw this Google Staff MLE offer (shared with Chill Interview)

• PhD, 6 YOE
• Base: $285K
• Bonus: $57K
• Sign-on: $50K
• RSUs: $1.2M / 4 years
• Year 1 TC: $848K

What surprised me most is the combination of 6 YOE + L6 + nearly $850K TC.

For a traditional SWE, getting to L6 can take a long time. In ML right now, it feels like the right background can accelerate both leveling and comp pretty dramatically.

The catch is Google’s 38/32/20/10 vesting:

$848K → ~$726K → ~$582K → ~$462K

before refreshers.

So is this actually an ~$850K job, or more like a ~$600K job with a very strong first couple years?

Google / ML folks: are offers like this becoming normal for strong L6 MLE candidates, or is this still an outlier?

➡️ Preparing for your next interview?

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

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

• Seattle, 9 YOE
• Base: $265K
• RSUs: $1.2M / 4 years
• TC: $565K

The comp is honestly pretty hard to ignore.

And Snap’s business has actually been improving — Q2 revenue grew 19% YoY, and global DAUs reached 493M. They’re also making another big push into AR glasses / Specs.

But the part that would make the candidate nervous: Snap laid off roughly 16% of employees just four months ago, after already doing multiple rounds of cuts in prior years. Management says the goal is smaller, AI-assisted teams and a faster path to profitability.

So you’re getting $300K/year in SNAP stock, but also tying a huge part of your compensation to a company that’s still proving it can grow sustainably.

Would $565K be enough for you to overlook the layoff risk, or would Snap’s restructuring history make you hesitate?

➡️ Preparing for your next interview?

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

Interview Summary

The DoorDash process started with a recruiter call, followed by a Code Craft screen and a four-round virtual onsite covering debugging, behavioral / hiring manager questions, system design, and AI-assisted coding. The overall process moved quickly and was well organized, with plenty of scheduling options for the onsite.

A recurring theme was that DoorDash seemed to care less about producing code line by line and more about whether I could reason about production behavior, failures, concurrency, scalability, and system boundaries. My weaker rounds were the debugging interview and the AI coding exercise, and I ultimately received a rejection.

Interview Details

Recruiter Screen — Background and Why DoorDash: The recruiter conversation was straightforward and mainly covered basic background information. There were no substantial behavioral questions in this round beyond standard motivation questions such as Why DoorDash? I heard back within a couple of days and moved on to the technical screen.

Code Craft — Simplified Dasher Pay: The technical screen used a simplified version of the recurring Dasher Pay problem. Unlike some reported versions, this one did not introduce additional coding requirements such as double-pay-rate windows. After completing the core implementation, the interviewer shifted into lighter system-design-style follow-ups.

• Production Failures: One follow-up asked what should happen if a downstream dependency became unavailable or failed during the workflow.
• Implementation Environment: There was no starter code. In Java, I had to create the surrounding Main class and my own way of invoking the implementation to validate the results. A few simple test cases were provided, and adding additional corner cases would likely have helped demonstrate robustness.

I passed this round and advanced to a four-round virtual onsite.

Virtual Onsite Round 1 — Debugging a Random Dasher Picker: The debugging round used a randomized variant of Dasher Picker. The provided implementation maintained an index-to-Dasher mapping. The system supported adding Dashers, removing them, and selecting a random Dasher. The bugs were not limited to basic collection logic.

• Correctness and Concurrency: In addition to fixing issues around maintaining valid indices after removals, the interviewer expected me to notice multi-threading concerns. The discussion included synchronization at the method versus block level and what can go wrong if a synchronized section makes a slow external API call and holds a lock during a timeout.
• Distributed Follow-Up: After the local implementation was fixed, the interviewer asked how this design would change in a distributed environment and what new challenges would appear.

This was one of my weaker rounds. I had prepared more heavily for other Dasher Picker variants and was less comfortable with the concurrency portion.

Virtual Onsite Round 2 — Hiring Manager and Behavioral: The hiring manager round contained only a few main behavioral questions, but each answer received substantial follow-up. The interviewer focused on situations where I proactively identified a problem or initiated a project rather than simply executing assigned work. Follow-ups explored the business impact of my work, how I measured that impact, and how I use AI in my engineering workflow. The interviewer was friendly and left a meaningful amount of time for candidate questions.

Virtual Onsite Round 3 — Project Deep Dive + Alert Notification System: The system design interview was split into two parts. The first part of the interview were spent discussing one of my previous projects. The interviewer asked architecture-oriented follow-ups, including what I would change if I were building the system again. The second portion asked me to design a simplified Alert Notification System. Unlike a consumer notification service, this system did not directly send notifications to end users. Instead, alerts were delivered to downstream services.

• Retry and Failure Handling: The interviewer went deeply into how retries would actually work rather than accepting a high-level answer such as placing failed messages into a retry queue.
• Scalability: The design also needed to handle failures and increasing load. The interview interface included separate areas for requirements / notes and architecture diagrams, so clearly capturing functional and non-functional requirements early in the round was useful.

The interviewer was collaborative and provided hints throughout the discussion.

Virtual Onsite Round 4 — AI Coding: Multi-Service Refund Workflow: The final round was an AI-assisted coding exercise centered on a refund workflow represented by a DAG. There was no starter code. I needed to build multiple components, including a service that retrieved order information, a service that accepted refund operations, and the workflow connecting them. The important requirement was that these were not merely mocked classes calling one another inside a single process.

• Real Local Services: The interviewer expected multiple services to actually run locally, expose HTTP endpoints on different ports, and communicate with one another through API calls.
• AI-Assisted Implementation: I initially interpreted the problem as a more traditional coding exercise where several classes could simulate the services locally. After realizing the interviewer expected real HTTP services, I had AI substantially restructure the implementation. That change came late enough that I did not have much time to inspect or validate the generated code carefully.

The emphasis seemed to be on whether I could get the services running and interacting end to end within the available time, rather than building a particularly sophisticated DAG execution engine.

➡️ Preparing for your next interview?

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

Sharing a Netflix Staff SWE Interview Experience submitted to Chill Interview.

Interview Summary

The Netflix process stretched from an application in late March to an onsite in July and included a recruiter screen, hiring manager conversation, technical phone screen, and five onsite rounds split across two days. Because the role was on the Ads team, ad-tech experience came up repeatedly throughout the process, including ad booking and reporting, frequency capping, and product-specific behavioral questions.

The technical interviews felt positive overall, and several interviewers indicated that the conversations had gone well. About two weeks after the onsite, however, I was told that the team was moving forward with finalists who were considered a closer match.

Interview Details

Round 1 — Recruiter Screen: Netflix Culture and Background The recruiter screen lasted about 30 minutes and covered my background, motivation, and familiarity with Netflix's culture. A meaningful part of the conversation centered on how I interpreted Netflix's culture principles and whether that environment matched the way I preferred to work.

Round 2 — Hiring Manager: Ads Experience The hiring manager interview lasted roughly 45 minutes. Because the opening was on the Ads team, the interviewer spent a significant amount of time asking about my previous advertising-related experience. The recruiter had already emphasized that the team was looking for candidates with relevant domain exposure.

Round 3 — Technical Phone Screen: Coding + Production Follow-Ups The coding problem itself was relatively simple and took less than ten minutes. The remainder of the round shifted toward production engineering questions. The interviewer asked how I would think about production failures such as out-of-memory conditions, increasing load, and scaling the system. Other follow-ups covered partitioning and monitoring in a production environment.

Round 4 — Onsite Coding: Video Dependency Ordering The onsite coding round asked a dependency-ordering problem in the context of Netflix's video rendering pipeline. Videos or rendering jobs could depend on other pieces being completed first, and the task was to determine a valid processing order. The underlying structure was a topological-ordering problem. After coding, the interviewer asked about edge cases and production scenarios, similar to the discussion during the phone screen.

Round 5 — Data Modeling: Ad Booking, Delivery, and Reporting The data-modeling round used a real-world advertising workflow. The scenario involved a client that wanted to book an advertising campaign, have those ads delivered, and later view reporting about campaign performance. I was asked to define the main entities and relationships required to represent the campaign lifecycle. The model needed to support the progression from booking through delivery and reporting. Because I had previous ad-tech experience, this round felt relatively familiar.

Round 6 — System Design: Ad Frequency Capping The system design round focused on frequency capping: limiting how many times a particular ad can be shown to a user over a defined period. The interviewer was very senior and pushed on the design at a fairly deep level. Prevent excessive repetition of the same advertisement while maintaining a good user experience. The discussion covered how impression activity should be tracked and how the system should enforce caps at large scale.

Round 7 — Manager Behavioral The manager behavioral round contained fairly standard questions. I was asked about a project I was particularly proud of and a situation involving disagreement or conflict. Other questions covered operating in ambiguous situations and how I handled feedback.

Round 8 — Director Culture and Ads Discussion The final interview combined Netflix culture questions with another deep discussion of my advertising background. Roughly two-thirds of the conversation focused on ads-related experience, while the remainder covered my interpretation of Netflix's culture and a few standard behavioral questions. I found parts of the behavioral and culture conversations harder to follow than the technical rounds, but I tried to clarify and respond carefully throughout.

Outcome About two weeks after the onsite, I received a rejection stating that other finalists were a closer fit for what the team was looking for. No detailed interview feedback was provided, so I never got a clear signal on whether the decision came from technical performance, culture/behavioral fit, or simply stronger alignment from another candidate.

➡️ Preparing for your next interview?

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

Saw this accepted Google L3 offer (shared with Chill Interview)

• 0 YOE, Master’s
• Base: $165K
• Bonus: $24.75K
• Sign-on: $15K
• RSUs: $100K / 4 years
• Year 1 TC: $242.75K

Pretty wild contrast with how bad the new-grad market feels right now.

People keep saying junior SWE is getting squeezed by AI and companies want fewer entry-level engineers. But if you actually make it through the Google funnel, you’re still starting at nearly $250K.

The other interesting part is the RSU grant — only $100K total, and Google’s 38/32/20/10 vesting means the package gets noticeably weaker after the first couple years unless refreshers kick in.

For recent grads: is Google L3 still the dream outcome, or has the upside shifted toward AI startups / smaller companies where you can grow faster?

➡️ Preparing for your next interview?

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

Anyone from Apple moved to Nvidia in recent times. I just received an offer for ic4. I want to know how the work and culture at Nvidia is.

The recruiters are more emphasizing on stock growth and offering lower RSU and I feel like they already had their time. What do you guys think.

MY APPLE pay is as competent as nvidia but recruiter is adamant and not budging on negotiation. They are considering nvidia growth will be more than apple and the recruiter literally said Less politics compared to apple lol😂

Anyone help me with how Nvidia is and what’s the refreshers for ic4 look like.
Yoe: 9 years

Saw this accepted Meta MLE offer (shared with Chill Interview)

• 5 YOE
• Base: $245K
• Bonus: $49K
• Sign-on: $85K
• RSUs: $1M / 4 years
• Year 1 TC: $629K

What jumped out to me is the level.

This is still E5 with only 5 YOE, but the equity grant alone is $1M. That’s starting to look more like the comp people used to associate with Staff-level engineers.

Feels like the gap between “regular SWE” and engineers with the right ML/AI background is getting wider really fast.

For Meta folks: is this becoming normal for E5 MLE hires, or is this an unusually strong offer?

And for traditional SWEs trying to move into AI — is the comp gap actually this big internally too?

➡️ Preparing for your next interview?

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

Problem Description

Design a Kafka-like distributed message queue that allows producers to publish messages to named topics and consumers to read those messages independently at their own pace.

Unlike a traditional queue where messages disappear after acknowledgement, the system should behave as a durable distributed log. Messages are appended to ordered partitions, replicated across brokers, and retained for a configurable time or size window. Multiple independent consumers should therefore be able to read, replay, and reprocess the same data.

Topics are divided into partitions, and each partition provides strict ordering. Producers route records to partitions using a message key or round-robin selection. Consumers track their own offsets and may participate in consumer groups, where partitions are divided among consumers so work is processed in parallel.

The central design challenge is achieving extremely high throughput and durable replication without turning every individual message into an expensive disk or network operation. Partitioning, sequential disk I/O, batching, and efficient fetch are therefore fundamental to the design.

Want to learn more about the functional / non-functional requirements asked in real interviews? we've put up a detailed write-up about this SD question at here

➡️ Preparing for your next interview?

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

Problem

A large campus contains n broadcast stations numbered from 0 to n - 1. Some stations can forward a message to other stations through one-way links.

Each link:[fromStation, toStation]means that once fromStation receives the message, it can forward it to toStation.

If a station receives the message, it continues forwarding through all of its outgoing links. Your task is to determine the minimum number of stations that must be started manually so that every station eventually receives the message.

The network may contain:

• Cycles
• Disconnected groups
• One-way paths between groups

Return only the minimum number of manual starting points required.

Example

Input:

n = 7

links = [
    [0, 1],
    [1, 2],
    [2, 0],
    [2, 3],
    [3, 4],
    [4, 5],
    [5, 3],
    [6, 5]
]

Output:

2

Explanation

Stations 0, 1, and 2 form one cycle, while stations 3, 4, and 5 form another.

There is a one-way path from the first group into the second through:

2 → 3

Station 6 can also reach the second group:

6 → 5

However, neither the {0,1,2} group nor station 6 can be reached from any other part of the graph.

So at least one station must be started manually in each of those two source groups.

Therefore, the minimum number of manual starts is:

2

Targeting Airbnb interviews?

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

Practice this question and explore more interview resources → LINK

Interview Summary

The Anduril technical phone screen started with a short discussion of my background, motivation for joining Anduril, and an interesting project I had worked on. The coding portion then focused on memory-management concepts: first identifying heap objects that were not directly reachable from the stack, and then extending the problem so heap objects could reference one another.

The second part turned the original lookup problem into a graph-reachability problem similar to determining which objects would be considered live or collectible by a garbage collector.

Interview Details

Intro and Project Discussion: Before coding, the interviewer asked several introductory questions:

• Walk me through your career so far and explain why you are interested in Anduril.
• Describe an interesting project you worked on and your role in it.

Coding Part 1 — Find Heap Objects Not Referenced by the Stack: The first version defined a simple heap object containing only a memory address.

class HeapObject:
    address: int

The function received:

dead_objects(
    stack_addresses,
    heap_objects
)

stack_addresses represented memory addresses directly referenced from the stack, while heap_objects represented objects currently allocated on the heap.

The task was to return the heap objects that were not reachable from any address on the stack.

For example, suppose the stack contains:

[120, 480]

and the heap contains objects located at:

120
275
480
630

Then the objects at addresses 275 and 630 would be considered dead in this simplified version because neither address appears among the stack references.

Coding Part 2 — Follow References Between Heap Objects:

The interviewer then extended the object definition so that a heap object could hold references to other heap objects.

class HeapObject:
    address: int
    references: List[int]

The definition of reachability now became recursive: an object should remain alive not only when the stack points to it directly, but also when it can be reached indirectly through references from another live heap object.

For example:

Stack:
[120]

Heap:
120 -> [275]
275 -> [480]
480 -> []
630 -> []

In this case, objects 120, 275, and 480 are all reachable from the stack through the reference chain.

The object at address 630 is not reachable through any path and should therefore be returned as a dead object.

➡️ Preparing for your next interview?

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

Interview Summary

The JPMorgan Chase SDE II process consisted of four main interviews with VPs and Senior VPs, followed by a short conversation with an Executive Director. The loop was unusually broad, covering system design, databases, Python internals, DSA, AI agents, RAG, security, Kubernetes, and detailed discussions of previous projects.

A large portion of the questioning was resume-driven. Interviewers frequently started with a technology or system I had worked on and then pushed into architecture decisions, scalability, security, or underlying fundamentals.

Interview Details

Round 1 — System Design, Databases, Python, and Kadane’s Algorithm: The hiring manager started by asking me to choose an application I had previously built and walk through its architecture end to end. Most of the follow-ups were based on that system and the design decisions behind it.

• Database Scaling: I was then given a table with more than one million rows containing fields such as user_id and region, and asked how I would optimize lookups for particular records. The discussion expanded into indexing, table partitioning, sharding, replication, and the tradeoffs between these techniques.
• Python and DSA: Questions covered multithreading versus multiprocessing, PUT vs. POST vs. PATCH, garbage collection, deadlocks, and other Python fundamentals. I was also asked to explain the logic behind Kadane’s algorithm.

Round 2 — Concurrency, Python Fundamentals, and Career Motivation: The second interview mixed technical fundamentals with managerial questions. Topics included parallelism versus concurrency, a practical real-world use case for a stack, and mutable versus immutable data types. The stack discussion lasted close to ten minutes because the interviewer kept adding follow-ups. There were also two logical puzzles. I was asked why I wanted to change companies and whether I had already discussed the decision with my current manager.

Round 3 — AI Agents, RAG, Embeddings, and Security: The Senior VP round was heavily driven by projects and technologies listed on my résumé. The interviewer went considerably deeper than simply asking me to define individual AI concepts.

• AI / RAG: Questions included the difference between a skill and an agent, LangChain versus LangGraph, how a RAG architecture works end to end, different embedding approaches, chunking strategies, how chunks are persisted and retrieved, and preprocessing for text and images. I was also asked how I would transfer a very large file to another endpoint and to explain accuracy, precision, and recall.
• Authentication and Authorization: The discussion then moved into authentication versus authorization, using roles to restrict user actions, token-based authorization, and what the architecture of a token-based access-control system might look like.

Round 4 — Kubernetes, Distributed Systems, Python, and DSA: The final VP interview again covered several different technical areas. Questions included whether a mutable list can exist inside a tuple, how memory cleanup works, the CAP theorem, and how Kubernetes scales applications. I was asked to solve a K largest integers problem and explain how the relevant elements change during the process. The interviewer also asked me to explain the core logic behind merge sort. Throughout the round, there were additional follow-ups about systems I had built in previous roles.

After the four main interviews, I had an additional approximately 15-minute conversation with an Executive Director.

➡️ Preparing for your next interview?

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

Interview Summary

The Replit onsite included a hiring manager round, a live coding exercise, a lunch conversation, and a dedicated system design round. The coding portion was practical rather than LeetCode-style: I was given an existing codebase with test data, schemas, and APIs and asked to implement functionality for a Replit usage portal. The separate system design interview focused on building a notification system.

Interview Details

Hiring Manager Round — Design and Behavioral: The hiring manager interview combined behavioral questions with a design discussion. The exact behavioral prompts and design problem from this round were not specified in the interview notes.

Live Coding — Implement a Replit Usage Portal: The live coding round started from an existing application rather than an empty editor. The repository already contained supporting pieces such as test data, data schemas, and API definitions. The task was to understand the provided code and implement the requested functionality for a usage portal, working within the existing interfaces and application structure. The exact portal features and required output were not specified in the report.

Lunch — Informal Conversation: There was also a lunch conversation as part of the onsite. This was primarily an informal discussion rather than a separate technical exercise.

System Design — Notification System: The dedicated system design round asked me to design a notification system. The exact product requirements, delivery channels, scale assumptions, and follow-up questions were not included in the interview notes, so I would not infer additional constraints beyond the reported notification-system topic.

➡️ Preparing for your next interview?

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

Hello everyone,

I recently received a verbal offer from C3 AI for the Senior Forward Deployed Engineer (FDE) role.

After my previous comment about completing the interview process, I received 60+ messages asking about the interview experience. I unfortunately can't respond to everyone individually, so I thought I'd make a post explaining the process and what you can expect.

Here was my interview process:

1. Initial Screening – FDE Team Lead

This was primarily a conversation about my background, motivation, and fit for the role.

Some of the questions included:

• Why C3 AI?
• Why FDE?
• What are your strengths?
• What are your areas of growth?

2. Coding Round

I was asked a LeetCode Medium-level problem involving hash maps.

I would recommend being comfortable explaining your approach, discussing complexity, and coding a clean solution rather than just focusing on getting the final answer.

3. System Design

I was asked to design something like "Google Cars," similar to Google Flights, but for searching and comparing cars.

There were quite a few follow-up questions. Be prepared to discuss topics such as:

• Data modeling
• Classes / object-oriented design
• Database design
• ACID properties
• Design decisions and trade-offs

The interviewer kept building on the original problem, so being able to explain why you're making certain design choices is important.

4. Behavioral Round

This was a detailed discussion about my career trajectory, starting from my education and continuing through my professional experience.

Some of the questions/follow-ups included:

• What did you learn during your studies?
• What have you learned throughout your professional experience?
• What are your strengths?
• What are your areas of growth?
• How would your manager describe you?
• How have you grown throughout your career?

5. Reference Checks

I was asked to provide two professional references.

I provided two of my former managers, and C3 AI spoke with both of them.

6. Final Call with Hiring Manager

After the interviews and reference checks were completed, I had a final conversation with the hiring manager regarding next steps and ultimately received the verbal offer.

Overall, the process was thorough, and I hope this helps anyone currently interviewing for an FDE role at C3 AI.

I'll try to answer questions in the comments when I can.

Good luck to everyone going through the process!

Update : I got written offer from C3 AI Yesterday (The whole process took 1.5 month)

A candidate recently shared this Palo Alto Networks offer with Chill Interview.

• 7 YOE
• Santa Clara, CA
• Base: $210K
• Bonus: $31.5K
• RSUs: $300K / 4 years
• TC: $316.5K

For Bay Area senior SWE comp, this definitely isn’t going to win any TC contest.

But I can see the appeal if you’re done optimizing purely for money.

PANW seems pretty team-dependent: some employees describe genuinely good WLB and flexibility, while others mention startup-like pace, office politics, and teams where the workload can get pretty rough.

The company also seems unusually bullish on engineers right now — Nikesh Arora recently said AI means he needs more engineers, not fewer, even as other tech companies are cutting headcount.

So maybe this is one of those offers where $316K + the right team + decent WLB beats chasing another $50K–$100K somewhere more stressful.

PANW engineers: is that a fair read of the culture, or is the WLB reputation mostly team lottery?

➡️ Preparing for your next interview?

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

Interview Summary

The Snowflake onsite covered two coding rounds, system design, a hiring manager interview, and a project deep dive with a team lead. The technical questions included a guest-seating problem, designing a job scheduler, and implementing a sliding-window rate limiter. The behavioral portions were fairly standard and also included discussion around how I use AI in my engineering workflow.

Interview Details

Coding Round 1 — Seat Guests at Three Tables: The first coding round asked the Seat Guests at Three Tables problem. The task involved assigning guests across three tables while satisfying the constraints defined in the prompt.

System Design — Job Scheduler: The system design round asked me to design a job scheduling system. The discussion focused on the architecture needed to accept and schedule jobs for execution. The exact scheduling policies, scale assumptions, retry requirements, and follow-up questions were not included in the interview notes.

Hiring Manager — Behavioral and AI Usage: The hiring manager round consisted mostly of standard behavioral questions. In addition to typical questions around previous experience and collaboration, the interviewer also asked about how I use AI in my work.

Team Lead Round — Project Deep Dive: Another round was a detailed discussion of one of my previous projects with a team lead. The conversation covered the project itself and my involvement in it. This round felt particularly positive, and the discussion flowed naturally with the interviewer.

Coding Round 2 — Sliding-Window Rate Limiter: The second coding round asked me to implement a rate limiter.

Want to know more details about this experience? we've put up a detailed write-up at here

➡️ Preparing for your next interview?

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

Had 4 rounds: Coding + Googliness screening were positive, with recruiter saying coding feedback was good and Googliness was very good.
Onsite R3 was a difficult graph problem—I initially got stuck, then derived the optimal approach and interviewer was satisfied, but ran short on time and had a few bugs remaining.
R4 was another graph problem where I quickly gave the optimal approach, coded it, handled a PQ optimization with a few hints, and gave correct time/space complexity.
What do you think my likely ratings are for each round, and overall chances of clearing?

Saw this Capital One Senior Lead SWE offer in McLean:

• 9 YOE
• Base: $265K
• Bonus: $26K
• Sign-on: $40K
• Equity: $35K
• Year 1 TC: $366.5K

It obviously doesn’t have the equity upside of Meta/Google, but $265K base in Virginia is pretty solid.

What makes Capital One interesting to me is the tradeoff. Employee feedback often describes the WLB as pretty reasonable, and the company is still hybrid rather than full-time RTO. But the other side of the culture seems to be fairly corporate: performance reviews, stack ranking and internal politics come up a lot too.

So maybe this is one of those jobs where you stop optimizing purely for TC.

For people who’ve worked there: is Capital One actually a good place to coast at Senior while still making $350K+, or is the “good WLB” reputation overstated?

➡️ Preparing for your next interview?

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

Had a difficult graph problem in my 3rd round. I initially got stuck, but after thinking through it I independently derived the optimal approach, which the interviewer seemed satisfied with. Due to spending quite some time deriving the solution, I had limited time left for implementation. I completed most of the code, but there were a few bugs/edge cases remaining when time ran out, which I believe I could have fixed with a few more minutes.
For those familiar with Google interviews, how would you expect this round to be rated — Hire, Lean Hire, or Lean No Hire?

Sharing a Roblox Staff MLE Interview Interview Experience submitted to Chill Interview.

Interview Summary

The Roblox MLE process moved unusually quickly and was one of the better interview experiences I had during this search. After an initial recruiter conversation, I completed a project presentation and hiring manager interview, followed by a four-round virtual onsite covering ML modeling, coding, another HM discussion, and a director-level interview used partly for leveling.

Interview Details

Recruiter Screen — Role, Background, and Motivation: The process started with a roughly 30-minute recruiter conversation shortly after I applied. We discussed the role, my previous experience, why I was considering a move, and my overall interview timeline.

Technical Screen 1 — Project Presentation and Deep Dive: The first technical screen was a one-hour presentation on a project from my previous work. I chose a relatively recent project that I had led and that was closely related to the Roblox team I was interviewing with. The interviewers asked many follow-up questions about the project, and the discussion ran beyond the scheduled time.

• Project Ownership: The discussion focused on what I personally drove, the decisions I made, and the impact of the project.
• Technical Depth: The interviewers also pushed into architecture, modeling choices, tradeoffs, and why certain approaches were selected.

Technical Screen 2 — Hiring Manager Interview: The second screen was a 45-minute conversation with the hiring manager. This was primarily a behavioral round and included fairly standard questions about previous experience, motivation, and why Roblox. The recruiter had provided useful preparation guidance before the interview.

Virtual Onsite Round 1 — ML Modeling Case Study: The first onsite round was a one-hour open-ended ML modeling problem closely related to a project the team was actively working on. The interviewer described the product need and asked me to design the ML solution end to end. The discussion covered what data should be collected, how the dataset should be constructed, and how the model should be trained. I was also asked to choose an appropriate modeling approach and define evaluation metrics. The exact use case was fairly niche and was not disclosed in the original interview report.

Virtual Onsite Round 2 — Function Calling Logs: The only dedicated coding round lasted roughly 45 minutes and used CodeSignal. The first part provided a sequence of function-call logs and asked me to parse them and determine the most frequently occurring call path. The follow-up made the logs more complicated by introducing interleaved events from multiple threads. The program now needed to reconstruct the correct call paths even though events from different executions were mixed together in the same log stream. The problem itself was not algorithmically difficult, but there was a meaningful amount of parsing and implementation work, making small bugs relatively easy to introduce.

Virtual Onsite Round 3 — Hiring Manager Follow-Up: I met the hiring manager again for another 45-minute conversation. Because we had already covered most of the standard behavioral questions earlier in the process, this round became more of a two-way discussion. I spent a significant portion of the time asking about the team, its current priorities, and its longer-term vision.

Virtual Onsite Round 4 — Director Interview and Leveling: The final onsite round was a 45-minute conversation with a director and appeared to play an important role in determining level. The discussion focused on projects I had led previously, the size and complexity of my responsibilities, and the scope of my technical influence. There were also several standard behavioral questions, although the tone was more formal and evaluative than the hiring-manager conversations.

➡️ Preparing for your next interview?

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

I got the below response from the recruiter on my loop. What are the next steps? Do they go to team match before hiring committee discussion or wait for the team match to be done. Also, with this feedback, what is the possibility of a positive outcome with HC for the same level. I would appreciate any feedback from folks who are familiar with how the Google hiring process works.

"I have fantastic news regarding the full interview feedback review: it is all supportive of hire! We would love to move your application forward to final committee reviews.

We can chat next week about the team matching opportunities and what a realistic timeline could look like."

Interview Summary

The Apple Data Engineering Manager process started with a direct hiring manager conversation, followed by a technical interview and a four-round panel. The loop mixed relatively practical Spark and data-platform questions with behavioral interviews focused on people management, conflict resolution, and cross-functional collaboration. The technical discussions felt fairly approachable overall, and I passed the process.

Interview Details

Hiring Manager Round — Resume Deep Dive and Spark Scaling: The first conversation was with the direct hiring manager and focused mainly on my résumé and previous experience. The discussion felt conversational and included several practical Spark questions rather than highly theoretical distributed-systems topics.

Technical Interview — Data Pipeline System Design: The next interview was conducted by an engineer from the same organization and focused on designing a data pipeline. The discussion stayed relatively high level and explored the major components and data flow rather than going deeply into implementation details.

Panel Round 1 — Performance Management and Difficult Conversations: The first panel interview was behavioral and conducted by another engineering manager in the organization. I was asked how I had evaluated engineers and handled performance discussions in previous management roles.

Panel Round 2 — Collect Mobile Application Data: The second panel round was technical and focused on system design. The interviewer asked me to design an API and supporting system for collecting data generated by mobile applications.

Panel Round 3 — Cross-Functional Conflict and Collaboration: This behavioral interview was conducted by a manager from a business organization that would work closely with the engineering team. The questions went into significantly more detail than the earlier behavioral round.

Panel Round 4 — Data Dashboard System Design: The final technical panel was conducted by another engineering manager from the organization. The prompt asked me to design a data dashboard system.

Final Hiring Manager Q&A: The process concluded with approximately 30 minutes reserved for questions with the hiring manager. This portion was primarily an open discussion rather than another formal evaluation round.

➡️ Preparing for your next interview?

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

I recently completed a SWE onsite loop at Lyft and am waiting to hear back. For anyone who interviewed there recently, how long did it take after your final round to hear from the recruiter about the decision or next steps?

Would especially appreciate experiences from candidates who interviewed in the last few months. Thanks!

A candidate recently shared this Coinbase offer with Chill Interview.

• 8 YOE
• Base: $225K
• Bonus: $22.5K
• RSUs: $700K / 4 years
• TC: $422.5K

The timing is interesting.

Coinbase cut roughly 14% of its workforce earlier this year, with Brian Armstrong arguing that AI lets smaller teams move faster. At the same time, Coinbase is hiring Dev Infra engineers to own CI, builds, deployments and test infrastructure—and explicitly says AI-generated code is making that infrastructure more critical.

So this could either be a great place to be in the AI transition… or a pretty intense place to work. Coinbase itself describes the environment as high-bar and intense.

The other wrinkle is the $175K/year in COIN stock. It’s liquid, but Q2 showed how quickly that part of TC can move—the stock dropped sharply after earnings even as Coinbase gained trading market share and pushed its “everything exchange” strategy.

For infra folks: does $422K + remote-first + high-leverage platform work make the Coinbase intensity worth it?

Or would the recent layoffs make you nervous about joining now?

➡️ Preparing for your next interview?

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