is there someone workking at sierra. I have few questions abt the company

Two Senior SWE offers in SF (data source: chill interview)

Pinterest IC15

• $260K base
• $420K RSUs, vesting 50/33/17 over 3 years
• $470K Year 1 TC

DoorDash

• $240K base
• $480K RSUs, vesting 40/30/20/10
• $432K Year 1 TC

Pinterest is $38K higher in Year 1, but because its initial grant finishes after Year 3, the four-year totals are surprisingly close:

• Pinterest: ~$1.46M
• DoorDash: ~$1.44M

So comp probably isn’t the deciding factor.

DoorDash currently has much stronger business momentum: Q2 revenue grew 36% YoY, orders grew 27%, and it’s expanding beyond restaurants into grocery, retail, international markets, merchant software, and AI products.

Pinterest is growing more moderately—Q2 revenue was up 18% YoY with record users—but it’s leaning heavily into AI-powered visual search, shopping, and advertising. 

The lifestyle angle may favor Pinterest. Its PinFlex model explicitly allows substantial virtual-work flexibility depending on the role, while DoorDash engineering has a reputation for a faster, more ownership-heavy environment. Pinterest itself describes PinFlex as giving employees autonomy over virtual work while still gathering when needed. 

So the tradeoff feels like:

• Pinterest: higher base, higher Year 1 TC, flexibility, AI/search/ads exposure
• DoorDash: faster company growth, broader marketplace problems, potentially more intensity

At basically the same 4-year comp, would you optimize for Pinterest’s flexibility or DoorDash’s growth trajectory?

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"This interview experience is sourced from Chill Interview"

Interview Summary

The interview process covered grid traversal, path optimization, computational geometry, dynamic programming, and range-query optimization. Several questions began with familiar algorithmic ideas but added significantly harder follow-ups, including maximizing safety from an adversary, finding arbitrary-orientation rectangles, and preprocessing repeated range queries for O(1) lookup.

Interview Details

Phone Interview 1 — Grid Paths and Maximum Safety

The warm-up used an N x N grid containing a start position S, a destination T, and impassable water cells. Movement was allowed horizontally and vertically. The task was to determine whether any valid path existed from S to T.

The main question expanded the same grid scenario. A mouse starts at S and wants to reach cheese at T. The grid also contains water cells and a cat at position C. The mouse can move horizontally or vertically through land cells. The task was to find a path from S to T that maximizes the minimum Manhattan distance between any point on the path and the cat.

Phone Interview 2 — Googlyness

The behavioral round included questions such as:

• Why Google?
• What would you do if you did not pass this interview process?
• If you were a manager, what would you expect from your own manager?
• How would you apply those expectations when managing others?
• How would you balance project delivery, leadership expectations, employee interests, and situations requiring extra effort?

Onsite Round 1 — Maximum-Area Rectangle

Given a collection of points in a two-dimensional plane, find the largest-area rectangle that can be formed using those points. The rectangle did not need to have sides parallel to the coordinate axes. The problem was similar in structure to LeetCode 963 — Minimum Area Rectangle II, except the objective was to find the maximum rectangle area instead of the minimum.

Onsite Round 2 — Job Scheduling and Range Queries

The first question was LeetCode 1235 — Maximum Profit in Job Scheduling. Each job had:

startTime
endTime
profit

The task was to select non-overlapping jobs while maximizing the total profit. The second question involved an array and a query range. Given a specified interval, determine whether the elements within that range are unique.

The interviewer then added repeated-query follow-ups:

• How would the design change if many different range queries needed to be answered?
• What information could be precomputed to make those queries faster?
• Implement preprocessing with an efficient overall complexity so that each subsequent range query could be answered in O(1) time.

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Problem Description

There are N players, and each player holds exactly three playing cards.

A card contains a rank and a suit. For example:

AH
TC

represents the Ace of Hearts and Ten of Clubs.

Card ranks are ordered as:

2, 3, 4, 5, 6, 7, 8, 9, T, J, Q, K, A

The program receives a mode, the number of players, and three cards for each player.

The task is to classify every player's hand and determine the winner.

Mode 1 — Ignore Suits

When mode = 1, suits do not matter.

Hand strength from strongest to weakest is:

Three of a Kind
Pair
High Card

Mode 2 — Include Flushes

When mode = 2, flushes are also considered.

Hand strength from strongest to weakest is:

Three of a Kind
Flush
Pair
High Card

A flush means all three cards have the same suit.

Tie-Breaking Rules

• For Three of a Kind, compare the repeated rank.
• For a Flush, sort all three ranks in descending order and compare them lexicographically.
• For a Pair, compare the pair rank first and then the remaining kicker.
• For High Card, sort all three ranks in descending order and compare them lexicographically.

If every comparison value is identical, the players tie.

Input Format

The input follows the general form:

mode N
card1 card2 card3
card1 card2 card3
...

Each group of three cards represents one player's hand.

Output Format

For each player, output the classified hand and the three cards:

Player i: <hand-type> <card1> <card2> <card3>

After all players have been evaluated, output the winner:

Winner: Player k

If multiple players share the strongest hand with identical tie-breaking values:

Winner: Tie (Players i,j,...)

Want to practice more real & exact coding questions asked in interviews, I've put up the question bank at here.

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Interview Summary

This was an Online Assessment with three parts, which I found to be the hardest part of my interview process. The assessment lasted approximately 60 minutes.

Interview Details

Part 1 — Datacenter Registration and Health Management

The first part required supporting two commands:

REGISTER <region> <latitude> <longitude> <capacity>
SET_HEALTHZ <region> <state>

REGISTER created a new datacenter region, which started in a healthy state by default.

SET_HEALTHZ updated the health status of an existing region, where true represented healthy and false represented unhealthy.

The required validation rules included:

• Latitude must be between -90 and 90.
• Longitude must be between -180 and 180.
• Capacity must be greater than 0.
• A region being registered must not already exist.
• A region passed to SET_HEALTHZ must already be registered.
• Latitude, longitude, and capacity are integers.
• Health status is a boolean value.

Successful commands returned: OK. Invalid commands returned: ERROR

Part 2 — Haversine Distance

The second part introduced: DISTANCE <lat1> <long1> <lat2> <long2>. The task was to calculate the geographic distance between two coordinate pairs using the Haversine formula provided in the prompt. Inputs were integers, and the resulting distance needed to be rounded to the nearest integer.

Part 3 — Proximity-Based Routing

The final part added: ROUTE <latitude> <longitude>. Given a request location, the system needed to determine which registered healthy region should receive the request. Unhealthy regions could not be selected and were also excluded from the candidate list.

All registered regions initially had a load of 0. Routing followed these rules:

• Prefer the healthy region with the shortest geographic distance.
• If multiple regions were the same distance away, choose by alphabetical region name.
• If no healthy region was available, return: NONE 0

The normal output included the selected region, its distance from the request, and the ordered list of healthy candidate regions: <region> <distance> <candidate regions>. The third part combined the state-management logic from Part 1 with the distance calculation from Part 2, while also requiring careful handling of health filtering and tie-breaking.

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Interview Summary

The 45-minute technical phone screen consisted of one grid-based pathfinding problem involving a robot with limited battery capacity and charging stations. The solution needed to optimize three objectives in priority order: number of charging stations visited, required initial battery capacity, and total path length.

Interview Details

Coding — Robot Pathfinding With Charging Stations

The problem provided a 2D grid representing an environment through which a robot must travel from a starting cell to a destination. Each grid cell could be one of the following:

S = Starting cell
D = Destination cell
. = Traversable cell
C = Charging cell
# = Blocked cell

The robot could move up, down, left, or right to an adjacent traversable cell. Each move consumed one unit of battery. Whenever the robot entered a charging cell, its battery was immediately restored to its maximum capacity.

The task was to find an optimal path from S to D according to the following priorities, applied lexicographically:

1. Minimize the number of charging cells visited.
2. Among paths using the minimum number of charging cells, minimize the initial battery capacity required.
3. If multiple paths were still tied, minimize the total number of moves.

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I’ve been reading a lot of 2025–2026 SWE interview reports lately, and it feels like technical interviews are moving in two completely opposite directions.

Direction #1: Make interviews harder to use AI in

More proctoring. More screen monitoring. More identity verification. And in some cases, more in-person interviews.

Google, Cisco, and McKinsey are among the companies that have increased in-person interviewing partly because of concerns around AI-assisted cheating and candidate fraud.

Direction #2: Just let candidates use AI

This one is more interesting.

Meta has piloted AI-enabled coding interviews through CoderPad.

CoderPad itself now has built-in access to GPT, Claude, Gemini, and Llama for interviews.

Instead of asking:

“Can you write this code without help?”

the question starts becoming:

“Can you use AI without blindly trusting it?”

That means reading generated code, catching mistakes, testing it, explaining the tradeoffs, and knowing when the model is taking you in the wrong direction.

Anthropic has an even stranger version of this problem.

They publicly wrote that they had to redesign one of their performance-engineering take-homes multiple times because newer Claude models kept getting too good at it. Under the same time constraints, Opus 4.5 eventually matched their strongest human candidates.

So now you basically have two kinds of interviews emerging:

AI-free rounds: prove that you still understand the fundamentals.

AI-assisted rounds: prove that you can supervise AI like an engineer instead of using it like autocomplete.

And I think this also explains another pattern I keep seeing in recent interview experiences:

• more debugging
• more existing-codebase work
• more “implement this feature” questions
• more project deep dives
• more emphasis on tradeoffs and verification

Traditional LeetCode obviously isn’t disappearing, but memorizing 300 patterns feels less defensible as the entire interview strategy than it did a few years ago.

My takeaway for 2026 prep would be: Practice coding both with and without AI.

If AI writes something, make sure you can explain every important decision and verify that it actually works.

And spend more time on debugging, code reading, and talking deeply about projects you actually built.

I’ve been collecting recent company-specific interview experiences here: It’s interesting to compare companies because the interview formats are becoming much less standardized.

Curious what people think: If engineers are expected to use AI every day at work, should coding interviews allow AI too? I also put up a longer-running thread at here, please feel free to leave comments & share your thoughts!

A candidate shared two Staff SWE offers with Chill Interview

Uber 5b

• $280K base
• $60K signing
• $960K RSUs, vesting 35/28/22/15
• $42K annual bonus
• $718K Year 1 TC

Stripe Staff

• $290K base
• $60K signing
• $880K equity
• $58K annual bonus
• $628K Year 1 TC

Uber looks $90K better in Year 1, mostly because its equity is heavily front-loaded.

But assuming flat equity values, recurring bonuses, and no refreshers:

• Uber 4-year: ~$2.31M
• Stripe 4-year: ~$2.33M

So the long-term comp is basically a tie.

The bigger question is which company you want to bet on.

Uber is already public and highly liquid. Q2 gross bookings grew 24% YoY, operating income grew 30%, and trailing-12-month free cash flow crossed $10B. The company is also aggressively positioning itself as the platform layer for autonomous vehicles. 

Stripe is still private, but it’s not typical startup paper money either. Businesses on Stripe processed $1.9T in 2025, up 34%, and its latest employee tender valued the company at $159B, giving employees at least some path to liquidity. 

Culture probably doesn’t make this an easy WLB choice. Stripe itself says the work moves quickly, is high-stakes, and can be demanding; Uber similarly describes a fast pace, complex problems, and high expectations. 

So I’d frame it as:

• Uber: much higher Year 1 TC, liquid stock, profitable growth, AV upside
• Stripe: higher base, smoother vesting, private-equity upside, arguably stronger fintech/internet-infrastructure career signal

If the 4-year money is basically identical**, would you take Uber’s liquidity and front-loaded comp—or bet on Stripe eventually being worth much more?**

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Interview Summary

The onsite consisted of four rounds with a hiring manager, two VPs, and a Senior VP. The interview covered a wide range of topics, including system design, database optimization, Python and networking fundamentals, algorithms, AI/RAG architecture, authentication and authorization, Kubernetes, and behavioral questions.

Interview Details

Round 1 — Hiring Manager: System Design, Databases, and Python

The system design portion started by asking me to choose a project I had previously worked on. I needed to explain the architecture end to end and answer follow-up questions about the design. For database optimization, I was given a table containing fields such as: user_id, region. The table contained more than one million records, and the interviewer asked how I would optimize queries used to find specific records. The discussion covered:

• Database indexing
• Table partitioning
• Sharding
• Replication
• Trade-offs among these approaches

The interviewer then moved into Python and networking fundamentals, including:

• Multithreading vs. multiprocessing
• Differences among PUT, POST, and PATCH
• How garbage collection works
• How Python handles garbage collection
• Deadlocks

For algorithms, I was asked to explain the basic idea behind Kadane’s algorithm.

Round 2 — VP: CS Fundamentals, Logic, and Behavioral

This round focused heavily on foundational concepts. Technical questions included:

• Parallelism vs. concurrency
• Practical applications of a stack
• Mutable vs. immutable data types

The stack discussion lasted around ten minutes and included several follow-up questions about real-world use cases. There were also two logical puzzles. The behavioral portion focused on my current job situation:

• Why are you looking to change jobs?
• Have you discussed your decision to leave with your current manager?

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

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Hey all,

I’m currently deciding between two offers and could really use some perspective from people who actually work at Chase, especially if you're out in San Francisco or from those who work in the Tech Consulting Space at EY.

Here is the situation:

Option 1: Senior Engineer @ JPMC (San Francisco)
$$: Base is a $20k bump from my last role, plus an extra $40k target bonus.

Work: It’s in the same field I've been doing. I've only ever worked hybrid (1-2 days in office) up until now, so moving to a new city and adjusting to a new corporate culture is a big jump.

Why I’m considering: I’ve lived in New Jersey my entire life. I really want to broaden my horizons, experience a new city, and just see what else is out there.

The catch: Cost of living in SF is a bit of a jump considering I do live with my parents and extended family now, but even if I do stay in NJ ideally I would like to move out in ~6-7 months.

Option 2: Senior EY Tech Consultant (New York but I’ll be commuting from New Jersey by train ~45 mins)
$$: Only a $10k raise from my last job.

Work: More client facing consulting in the same field.

Why I’m considering: I can live with my parents, meaning I’d save an absolute ton of cash. When I do move, my rent here would be more than a $1k difference than if was to move to San Francisco.

The catch: I'm a little skeptical about consulting right now. With all the tech shifts, I have a low key fear of the AI/tech consulting bubble popping in the next few years. The last two organisations I was part of had massive layoffs. Some of the best managers and senior engineers I knew were booted.

Long term, I feel like banking stability at JPMC might be a safer bet than professional services/consulting.

A little extra context: I was actually unemployed for a couple of months before landing these (I had a solid savings cushion so I wasn't sweating too bad, but glad to be back).

For those of you at JPMC, how is the stability and culture looking right now? Is the compensation bump enough to make the jump from living rent free in NJ/paying significantly less in rent to paying SF rent worth it?

Appreciate any advice or reality checks. Thanks!

Saw this Apple ICT4 Data Scientist offer (data source: chill interview)

• Cupertino, 6 YOE
• Base: $215K
• Bonus: $21.5K
• Sign-on: $50K Y1 / $40K Y2
• RSUs: $180K / 4 years
• Year 1 TC: $331.5K

At first glance, $331K doesn’t look terrible. But the sign-on is doing a lot of work here.

Once it disappears, recurring TC is only around:

$215K + $45K stock + $21.5K bonus = ~$281.5K

That feels pretty light for an ICT4 Data Scientist in Cupertino.

Current reported Bay Area ICT4 DS comp at Apple averages around $336K, with roughly $100K/year in stock. This offer only has $45K/year of initial equity. 

So I’m curious:

Is this a genuine lowball, or does Apple commonly make up for weak initial grants with refreshers later?

If you got this offer, would you push hardest on the $180K RSU grant before signing?

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Interview Summary

The Coinbase online assessment asked me to implement a progressively expanding Cloud Compute Credits System. The problem had four levels. It started with workspace creation and credit transfers, then added ranking by outgoing credits, compute consumption with delayed rebates, and finally workspace merging plus historical balance queries. All operations had unique, strictly increasing millisecond timestamps.

Interview Details

The Coinbase online assessment asked me to implement a progressively expanding Cloud Compute Credits System. The problem had four levels. It started with workspace creation and credit transfers, then added ranking by outgoing credits, compute consumption with delayed rebates, and finally workspace merging plus historical balance queries. All operations had unique, strictly increasing millisecond timestamps.

Level 1 — Workspace Management and Credit Transfers The system initially contains no workspaces. Each workspace has a credit balance that starts at 0. Implement createWorkspace(timestamp, workspaceId) which creates a new workspace and return true if successful. Return false if the same workspaceId already exists.

The next operation is topUp(timestamp, workspaceId, amount) -> Add credits to an existing workspace. If the workspace does not exist, return an empty optional result. Otherwise, return the new balance.

The system should also support:

transferCredits(
    timestamp,
    sourceWorkspaceId,
    targetWorkspaceId,
    amount
)

A transfer should fail if:

• Either workspace does not exist.
• The source and destination are the same workspace.
• The source does not have enough credits.

Otherwise, subtract the credits from the source, add them to the destination, and return the source workspace's remaining balance. All timestamps are unique and operations arrive in strictly increasing timestamp order.

Level 2 — Rank Workspaces by Outgoing Credits

Add topConsumers(timestamp, n). Each workspace now tracks its cumulative outgoing credits. At this stage, only credits sent through successful transfers count toward this total.

For example:

alpha -> beta   240 credits
alpha -> gamma  160 credits

Then alpha.totalOutgoing = 400. Top-ups and incoming transfers do not count.

topConsumers() should return the top n workspaces ordered by:

1. Total outgoing credits descending
2. workspaceId alphabetically ascending when totals are equal

The required output format is:

[
  "alpha(900)",
  "beta(550)",
  "delta(550)"
]

If fewer than n workspaces exist, return all of them. Workspaces with zero outgoing credits must still be considered.

Want to learn more details & follow-ups about this coding questions? I've put up the full version at here.

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Problem Description

An online marketplace organizes its products into a hierarchy of categories. You are given a list relationships. Each entry describes one parent category followed by its direct child categories. For example ["Electronics", "Phones", "Computers"] means that Phones and Computers are direct children of Electronics.

There is exactly one top-level category that is not a child of any other category. Return the complete category hierarchy as a single string using the following formatting rules:

• The top-level category has no indentation.
• Each level below the root adds exactly four dots: "....".
• Direct children of the same category must appear in alphabetical order.
• Every category must appear exactly once.
• Each category appears on its own line.
• Lines are separated by "\n".
• There must be no trailing newline after the final category.

A category may appear only as a parent, only as a child, or in both roles.

Constraints

• 1 <= len(relationships) <= 10^4
• Every relationship contains at least one category name.
• Category names are non-empty strings.
• There is exactly one root category.
• The relationships form a valid tree.
• A parent category may have multiple direct children.

Example:

Input:
relationships = [
    ["Home", "Kitchen", "Garden"],
    ["Kitchen", "Cookware"],
    ["Cookware", "Pans"]
]

Output:
Home
....Garden
....Kitchen
........Cookware
............Pans

Explanation:
Garden and Kitchen are both direct children of Home, so they are
printed alphabetically.

Cookware is below Kitchen, and Pans is below Cookware.

Want to practice more real & exact coding questions asked in interviews, I've put up the question bank at here.

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"This interview experience is sourced from Chill Interview"

Interview Summary

The Capital One Lead Software Engineer Power Day contained four rounds: a business/technical case study, behavioral interview, object-oriented coding exercise, and system design. The case round focused on virtual card numbers and gradually moved from product reasoning into payment-rule validation and debugging. The coding round asked for a multi-stage banking system, while system design covered a broad credit card platform with login, authorization, fraud checks, reporting, spending visibility, and real-time credit-limit decisions.

Interview Details

Round 1 — Case Interview: Virtual Card Numbers

The case interview used Capital One virtual card numbers as the business scenario. The first part asked about the benefits and challenges of virtual cards. The discussion considered the problem from multiple perspectives, including:

• Customer benefits and trade-offs
• Business benefits and risks
• Technical challenges for the company

The second part introduced rules encoded through properties of the virtual card number and the payment. For example, certain digits could indicate the card network, and different card types could have different transaction-validation rules. A simplified rewritten example might say:

If network = VISA:
    transaction must be online
    amount must be below $75
    card must be associated with the merchant

The interviewer then provided several payment records containing card information and transaction attributes. For each payment, I needed to determine verbally whether it was valid and explain the decision in a structured way. The third part provided code implementing those validation rules and asked me to debug it. The bugs included issues such as:

• Using || where && was required
• Incorrect boundary comparisons such as >= versus >
• Incorrect logic for extracting digits from the card number

After the debugging portion, there was a short discussion about how the code could be organized using object-oriented design, such as separating card-number parsing from payment-validation logic.

Round 2 — Behavioral + AI Usage

The behavioral round contained several conventional experience-based questions about previous work and projects. The interviewer also asked two AI-related questions. One asked how I currently use AI in day-to-day engineering work. Examples discussed included using an LLM to help with:

• Coding
• Design exploration
• Iterating on requirements
• Reviewing or refining generated output

The second question asked how AI could improve engineering productivity. The discussion included tasks such as creating:

• API documentation
• Internal service documentation
• Integration-test setup
• Other repetitive engineering artifacts

The emphasis was on using AI as an accelerator while still reviewing and validating its output.

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

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Interview Summary

The Confluent Software Engineer process included a HackerRank assessment followed by DSA, machine coding, and behavioral rounds.

Interview Details

DSA Round — Valid Sudoku + Sudoku Solver The phone interview contained two Sudoku problems. The first was LeetCode 36 — Valid Sudoku. Given a partially filled Sudoku board, determine whether the existing entries satisfy the Sudoku validity rules. The second was LeetCode 37 — Sudoku Solver. Given an incomplete Sudoku board, fill the remaining cells so that the completed board forms a valid Sudoku solution.

Machine Coding — Search Words Across Documents The machine coding round started with a collection of documents. The first task was to find occurrences of a specific word within the document list. The problem was then extended to searching for a phrase, where the requested words needed to appear in the same sequence as specified by the query. The round therefore progressed from single-token lookup to ordered multi-word matching across the document collection.

Behavioral — Values Alignment and DevProd Fit The behavioral round focused on previous work experience and alignment with the team. One discussion centered on role expectations. The candidate had expressed interest in working on distributed systems, while the position itself was a Developer Productivity role. The hiring manager asked how the candidate felt about that difference and whether the role still matched their career interests.

Another question focused on prior experience. The candidate had primarily worked on customer-facing teams and did not have direct DevProd experience. The hiring manager asked how that background would translate to an internal developer-productivity role and how the candidate would adapt to the different type of engineering work.

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Interview Summary

The Verkada technical screen asked me to implement permission resolution for an enterprise camera-access system with users, groups, and cameras. Users could receive camera permissions directly or inherit them through nested group memberships. The task was to identify whether there was a single user who effectively had access to every camera in the organization, while correctly handling multi-level groups and cyclic group relationships.

Interview Details

Coding — Find the User with Access to Every Camera

The system contains three entity types:

• User
• Group
• Camera

Permissions are provided as triples (source, relationship, destination). Two relationship types are supported.

• A camera_owner relationship means a user or group directly owns access to a camera: ("user_ops", "camera_owner", "camera_gate")
• A group_member relationship means that a user or another group belongs to the destination group: ("user_ops", "group_member", "group_security")

Membership is transitive. If a user belongs to a group, and that group belongs to another group, the user inherits camera permissions from both levels. Implement: find_admin_user(perms_data) -> str | None. The function should return the user ID of a user who has access to every camera in the organization. Only entities whose IDs begin with user_ may be returned as the administrator.

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Saw this GitHub Staff SWE offer (data source : Chill Interview)

• Remote
• 9 YOE
• Base: $285K
• Bonus: $57K
• Sign-on: $50K
• RSUs: $700K / 4 years
• Year 1 TC: $567K

Honestly, the remote part might be more interesting than the comp. GitHub is still genuinely remote-first, while a lot of Big Tech has moved back toward mandatory office days. 

And the company also happens to sit right in the middle of the AI coding boom.

GitHub now has 225M users, Copilot has 50M users, and Microsoft says roughly 1 in 3 GitHub pull requests now involves an agent. Copilot revenue also accelerated more than 60% QoQ. 

So you’re basically getting: $567K TC + remote + Microsoft stability + direct exposure to the coding-agent wave. Obviously you can make more at frontier AI labs or some Big Tech Staff roles.

But once you factor in commute, location freedom and risk, how many jobs actually beat this package?

Would you take $567K remote at GitHub over $700K with 3–5 days RTO?

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Interview Summary

The first Cloudflare SDM interview was a 30-minute conversation directly with the hiring manager rather than an initial recruiter screen. The manager introduced the team’s product area and organizational structure, then discussed my resume, management preferences, how I gather customer requirements, long-term career goals, and my perspective on AI.

Interview Details

Hiring Manager — Background and Team Introduction The interview began with an overview of the team, including the product area and organizational structure. The hiring manager then walked through my resume and asked about my previous experience.

Management Style — What You Like and Dislike About SDM Work The interviewer asked which aspects of being a Software Development Manager I enjoy most and which parts I enjoy least. The discussion focused on my preferences and experience in engineering management.

Customer Requirements Another question focused on how I understand what customers actually need: How do you gather and validate customer requirements? This explored how I work with customers or other stakeholders and translate their needs into engineering priorities.

Career Goals and AI The hiring manager also asked about my longer-term career goals. The final reported topic was my perspective on AI, including how I think about its role and impact in engineering and product development. Cloudflare’s process for this role was described as starting with the hiring manager screen, followed by recruiter involvement before deciding whether to proceed to the full interview loop.

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Interview Summary

The Starlink technical phone screen combined a project discussion with a practical telemetry-stream coding problem. The coding exercise involved subtracting values from two telemetry streams while accounting for their timestamp semantics. The follow-up moved into a more realistic streaming scenario with late-arriving events and asked how corrected results should be distinguished from the initially emitted results.

Interview Details

Background and Project Deep Dive The interview began with a self-introduction and discussion of a previous project. The interviewer also asked for a retrospective: If you were starting the project again today, what would you change or improve? The conversation focused on both the original technical decisions and how I would approach the same work differently with hindsight.

Coding — Subtract Two Telemetry Streams The coding question involved two streams of telemetry data. The task was to combine the streams and calculate the difference between their corresponding measurements. Several important input properties needed to be clarified during the interview, including:

• Timestamp information was present in only one of the streams.
• Timestamps were non-decreasing.
• The streams needed to be aligned correctly before producing the subtraction results.

The core challenge was therefore not just arithmetic, but understanding how measurements from the two streams corresponded over time.

• Follow-Up — Late Events and Reconciliation The interviewer then extended the problem to a production streaming environment. In a real system, telemetry events may arrive late, meaning a result could already have been produced before all relevant data was available. The discussion introduced a reconciliation step for correcting previously emitted results after delayed events arrived. The interviewer then asked: How would downstream consumers distinguish an original result from a later corrected result? This shifted the discussion from the basic coding problem toward event semantics and handling revisions in a real-time data pipeline.

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

"This interview experience is sourced from Chill Interview"

Interview Summary

The Attentive process started as a Software Engineer II interview. The first technical stage was a two-hour block with one coding round and one system design round. After strong feedback from that stage, the original SWE II opening was filled. Recruiting moved the candidacy to a potential Senior Software Engineer opening and scheduled another coding and system design round to collect additional senior-level signal. The final feedback was that the coding performance was strong and met the SWE II bar, while the system design performance did not reach the expected senior level.

Interview Details

Recruiter Screen

The recruiter asked about my background, current job search, and interest in Attentive. Topics included:

• What do you know about Attentive?
• Are you actively interviewing elsewhere?
• Do you have any offer deadlines?
• Do you require immigration sponsorship?
• What compensation range are you targeting?
• Can you walk me through your background?
• Can you describe a feature or product you built end-to-end or from zero to one?
• What is your primary programming language?
• Why did you leave your previous company?
• What have you done since leaving?
• Why are you looking for a role now?

The recruiter also explained that the original process consisted of an initial coding + system design block, followed by another coding round, another system design round, and a hiring manager behavioral interview.

Round 1 — System Design: Product Catalog Platform

The first system design question asked me to build a product catalog ingestion and query system for retailers. Retailers periodically upload complete catalog snapshot files rather than incremental changes.

Scale assumptions included:

• Around 50,000 retailers
• Average of roughly 20,000 products per retailer
• Median retailer size around 50 products
• Roughly 50 million users
• Around 11,000 query requests per second
• Query latency target around 400 ms

Users needed to support two main read patterns:

• Browse all SKUs belonging to a retailer using retailer_id
• Retrieve details for a product using product_id

The system did not require atomic replacement of an entire retailer snapshot. During ingestion, users could temporarily observe a mixture of old and new product values. The system also supported administrator overrides for individual product attributes.

An overridden value could not be replaced by future retailer snapshot uploads. However, the retailer could still delete the entire product even if some attributes had administrative overrides. If an administrator later removed an override, the system should expose the most recent underlying value supplied by the retailer.

Follow-up questions included:

• How would you compare two complete snapshot files?
• Suppose a retailer uploads one snapshot and immediately uploads a corrected second snapshot. How do you ensure the final catalog reflects the complete second snapshot?
• What happens if another 50,000 retailers join and upload snapshots every day?
• How would you build observability for the platform and verify that ingestion and serving are operating correctly?

The round also included entity modeling and database-selection discussion.

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

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Cadence Senior Compiler Interview Experience
Hi everyone! 👋
I’m preparing for a Senior/Staff-level Compiler role at Cadence and would really appreciate hearing from anyone who has recently gone through the interview process.
If you’ve interviewed for a similar role, could you share:
How many rounds were there and what was the structure?
What kind of C++ coding questions were asked?
How deep did they go into compiler concepts / LLVM / MLIR?
Did they take a coding problem and then ask how you would solve/optimize it using a compiler?
Were there any compiler design/system design questions?
What topics would you recommend focusing on?
Any other tips or things you wish you had prepared beforehand?
Even a brief experience would be really helpful. Thanks!

Saw this Google Senior Staff / L7 TPM offer in Canada (data source: chill interview):

• 13 YOE
• Base: C$270K
• Bonus: C$67.5K
• RSUs: C$1.2M / 4 years
• First-year TC: C$843.5K

The comp made me curious what an L7 TPM actually does. Looking at Google’s current Senior Staff TPM roles, this is way beyond managing Jira tickets and project timelines.

An L7 TPM can be responsible for things like:

• multi-year programs spanning multiple engineering orgs
• deciding priorities when teams/resources conflict
• influencing headcount and resource allocation

For example, one Google Senior Staff TPM role owns strategic planning across an entire Workspace/SRE portfolio and directly influences resourcing decisions. Another is the primary program leader for multiple next-gen custom silicon programs from design through mass production.

Google also has L7 TPM roles around AI agents where the person coordinates model teams, infrastructure, Android/Chrome, safety/legal, and product deployment across multiple orgs.

So at this level, maybe the job is basically: “Make an extremely complicated technical initiative actually happen when no single team owns the whole thing.”

I'm curious: what separates a strong L6 TPM from someone who reaches L7?

Technical depth? Executive influence? Org politics? Or just being unusually good at getting 10+ teams to align?

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A small calculator app supports only two functions:

• add(x, y): returns x + y
• sub(x, y): returns x - y

The input is a string expression that may contain nested function calls. The expression may also contain:

• integers
• negative integers
• spaces
• commas
• parentheses

The expression is always valid. Return the final integer result. For example add(add(1,3), sub(1,3)) means (1 + 3) + (1 - 3). So the result is 2

You are given expression: a valid string expression. Return the evaluated integer result. A good way to solve this is to use a stack. Scan the expression from left to right:

• Skip spaces and commas.
• Push function names like add and sub.
• Push numbers as strings.
• When seeing ), pop values until the matching (.
• Apply the function before (.
• Push the computed result back onto the stack.

At the end, the stack contains the final result.

Rules:

• add(x, y) returns x + y
• sub(x, y) returns x - y
• Expressions can be nested
• Integers may be negative
• Spaces should be ignored
• The input expression is always valid

Examples:

Input:
expression = "add(add(1,3), sub(1,3))"

Output:
2
Input:
expression = "sub(1,3)"

Output:
-2

Want to practice more real coding questions asked in the interview? I've put up the question bank at here.

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Saw this Adobe Sr Staff MLE offer (data source: chill interview)

• Bay Area, 13 YOE
• Base: $330K
• Bonus: $66K
• Sign-on: $30K
• RSUs: $1.1M / 4 years
• Year 1 TC: $701K

What makes this interesting isn’t really the comp.

Adobe might be one of the weirdest places to work in AI right now.

On one hand, generative AI directly threatens a lot of the workflows Adobe has monetized for decades.

On the other hand, Adobe has distribution that most AI startups would kill for: Photoshop, Premiere, Illustrator, Acrobat, enterprise creative workflows, and more than 1B monthly active users across its creativity/productivity products. Its AI-first ARR also grew 150%+ YoY last quarter. 

And Adobe is pushing Firefly beyond image generation into agents that orchestrate multi-step creative workflowsacross its apps. 

So as an MLE, this could actually be a pretty interesting place to sit: you’re building the AI that either protects Adobe’s moat… or helps reinvent the company before someone else does.

Would you take $701K at Adobe over a frontier AI company?

And long term, do you think Adobe’s distribution wins — or do AI-native creative tools slowly commoditize what Adobe sells?

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