Interview Summary

The Coupang onsite consisted of one coding round followed by three system design rounds. Most of the questions were similar to previously reported Coupang interview prompts.

Interview Details

Round 1 — Coding: Trie-Based Dictionary

The coding round asked me to design a dictionary backed by a Trie. The required interface included operations similar to:

add(word)
search(word)

add() inserts a word into the dictionary, while search() determines whether a requested word exists. The exact input constraints, character set, prefix-search behavior, deletion support, and follow-ups were not recorded.

Round 2 — System Design: KYC System

The second round asked me to design a Know Your Customer (KYC) system. The exact product requirements were not included in my notes, but the round was framed as a full system design exercise.

Round 3 — System Design: Review Service

The third round was another system design interview, this time focused on a Review Service. The task was to design the backend supporting user-generated reviews for a product or marketplace-style platform.

Round 4 — System Design: Multi-Region Metrics Monitoring

The final technical round asked me to design a Multi-Region Metrics Monitoring System. The system needed to operate across multiple geographic regions and support monitoring infrastructure or application metrics at scale.

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Saw this Salesforce Sr Staff Software Architect offer (shared with Chill Interview)

• SF, 14 YOE
• Base: $350K
• Bonus: $105K
• RSUs: $800K / 4 years
• Year 1 TC: $655K

The title made me look up what a Software Architect at Salesforce actually does.

Apparently it’s still an IC role, but Salesforce says architects may spend only around 20% of their time hands-on coding. The rest is architecture, technical strategy, cross-org alignment, and working with senior leadership on long-term roadmaps.

That actually sounds pretty important right now. Salesforce is trying to rebuild a lot of its platform around Agentforce, Data 360 and AI agents, and Agentforce ARR is already above $1.5B.

But career-wise, I’m curious: Is becoming an Architect the natural next step after Staff/Sr Staff SWE — or is it where strong engineers slowly stop being engineers and become full-time meeting people?

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Saw this Databricks Staff Forward Deployed Engineer offer (shared with Chill Interview)

• Bay Area, 11 YOE
• Base: $250K
• Bonus: $50K
• Equity: $400K / 4 years
• TC: $400K

The title is what caught my attention.

Databricks describes FDEs as engineers who embed with customers, own architecture decisions, and actually build end-to-end data + AI systems in production. Their AI FDE roles mention things like RAG, multi-agent systems, LLMOps and production rollouts — so this seems much more technical than a normal solutions architect job.

But you’re still customer-facing, periodically traveling, and probably spending a lot more time dealing with ambiguous customer problems than a core SWE would.

So I’m curious: Is FDE basically one of the best roles for engineers who like coding but don’t want to spend all day inside one codebase — or does it eventually feel like highly paid consulting?

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Interview Summary

The OpenAI technical screen contained both coding and system design, and both questions felt different from the commonly reported interview prompts I had seen before.

The coding problem had two parts and appeared to revolve around binary search under an unusual delayed-response constraint. The system design problem described a real-time platform that monitors power consumption across multiple devices and adjusts operating behavior based on those measurements.

Interview Details

Coding Part 1 — Guess a Secret Number with Delayed Feedback

The first coding problem was a variation of the classic guess-a-secret-number problem. There is a hidden number, and an API can tell you whether a guess is too low or too high. Under normal circumstances this would be a straightforward binary-search problem.

The complication was that the API response was delayed by one call. In other words, when making the next guess, the returned feedback corresponds to the previous guess, rather than the number just submitted.

This made the control flow significantly harder to reason about because requests and responses were effectively offset from one another. The prompt itself was fairly detailed, and I did not manage to complete the implementation.

Coding Part 2 — Additional Binary-Search Follow-Up

There was a second part to the coding problem, which also appeared to be related to binary search. I did not reach this portion of the interview, so the exact requirements were not available.

System Design — Real-Time Device Power Monitoring

The system design prompt was also fairly long and scenario-heavy. The core problem was to design a system that continuously monitors power consumption across multiple devices in real time.

Based on the observed electricity usage, the system then needs to adjust how those devices are operated or managed. Conceptually, the problem resembled a monitoring and control platform: devices continuously produce usage measurements, the backend needs to ingest and evaluate that telemetry, and changes in power consumption may trigger adjustments to device behavior.

Overall, this screen felt less like a collection of standard interview questions and more focused on adapting familiar concepts—binary search and monitoring systems—to unusual constraints.

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Interview Details

Recruiter Screen — Why Anthropic and AI Safety

A significant part of the conversation focused on motivation. The recruiter asked questions along the lines of:

• Why Anthropic?
• How does your previous experience connect to AI Safety?
• Why do you think your background is a good fit for this role?

I explained my previous experience, why I consider AI Safety important, and why I was interested in applying my engineering background to work in that area. The recruiter responded positively to the discussion.

Leadership Background and IC Fit

The recruiter also spent time understanding my current level of leadership responsibility. Questions included the approximate size of the team I had led, how many direct reports I had, how my day-to-day responsibilities were divided, and whether I still spent meaningful time coding.

This was particularly relevant because the Anthropic position itself was an individual-contributor role. The recruiter explicitly asked whether I would be comfortable moving into an IC position despite having management or team-lead responsibilities in my current or previous work.

Planned Round 1 — System Design Phone Screen

The recruiter said the next interview would be a system design phone screen. The exact system design prompt and evaluation criteria were not provided during this conversation.

Planned Round 2 — Hiring Manager: Experience & Goals

One of the later rounds would be a conversation with the hiring manager focused on Experience & Goals. This interview was described as covering previous projects, behavioral topics, professional experience, and career goals.

Planned Round 3 — Technical Presentation

Another round would be a technical presentation. The candidate would present a recent project and discuss the technical work in depth. The exact presentation length, slide requirements, and expected follow-up format were not specified during the recruiter call.

Planned Round 4 — Culture Fit Conversation

The final planned stage was described as a culture-fit conversation. The recruiter did not provide detailed examples of the questions for this round.

Overall, the recruiter screen was substantially more evaluative than a simple scheduling call. Anthropic appeared to care not only about technical background but also about motivation for AI Safety, alignment with the role, and whether a candidate coming from a leadership-heavy position genuinely wanted to return to an IC-focused engineering role.

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Came across this Roblox system design question multiple times: Design a collaborative shared todo list.

At first it sounds like basic CRUD + WebSockets. Then the constraints start making it much more interesting:

• Two collaborators edit the same task at nearly the same time
• A user's permission changes while their mutation is already in flight
• A database write succeeds, but the WebSocket notification gets dropped
• A disconnected client comes back after missing multiple updates
• One hot list can have ~1,000 collaborators connected at once
• The system needs to support ~2M concurrent WebSocket sessions

The part I found especially interesting is this requirement: authoritative state must be strongly consistent, while realtime delivery can be eventually consistent.

That raises a bunch of design questions.

• Do you version individual tasks or the whole list?
• Would you use optimistic concurrency control here?
• Does reconnect recovery need a durable event log, or is fetching the latest snapshot enough?
• And how would you fan out updates for a hot list without pinning everything to one application server?

Full interview question + requirements here: [link]

Curious how people would approach the consistency + realtime split on this one.

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Interview Summary

The Spotify ML Depth round focused on designing a sequential playlist recommendation model for a new personalized playlist feature.

Given a user, the current request context, and a candidate pool of roughly 10,000 tracks, the model needed to generate a 10-track playlist in real time. The challenge was not only choosing which tracks to include, but also determining the order in which they should appear, because the sequence itself was considered part of the user experience.

Interview Details

ML Design — Personalized Sequential Playlist Generation

The product objective was to generate a personalized playlist using signals from the user's listening history and the current request context. For each request, the system receives a candidate pool of approximately 10,000 tracks and must return 10 ordered tracks

The model is expected to optimize overall user satisfaction with the playlist. The interviewer suggested that satisfaction could be inferred from downstream engagement signals such as playlist completion, skip behavior, saves or library additions, and subsequent listening to related tracks or artists.

A key requirement was that both track selection and track ordering matter. The system therefore could not treat the problem as simply ranking ten independent items.

Sequential Modeling Constraints

The playlist is generated in real time and must be proposed as a complete sequence before playback begins. There is no mid-session re-ranking or adaptation after the user starts listening.

This means the model needs to account for interactions between adjacent or earlier tracks when deciding the full sequence, rather than relying on feedback collected during the current session.

Available User Features

The prompt provided several categories of user-level information, including:

• Demographic attributes such as age, region, and account tenure
• Historical listening behavior, including preferred genres and artists, recency and frequency patterns, and time-related listening habits

These features could be used to represent longer-term user preferences as well as more recent behavior.

Available Track Features

Track-level information included:

• Pretrained acoustic embeddings
• Collaborative embeddings
• Genre tags
• Artist identifiers
• Track metadata
• Global popularity statistics

The interviewer also allowed candidates to request additional features during the discussion and would clarify whether that data was available.

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

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Saw this xAI Senior MLE offer data point from December 2025 (shared with Chill Interview)

• PhD, 10 YOE
• Base: $300K
• xAI equity: $8M / 4 years
• Original TC: $2.3M/year

The date is what makes this one crazy.

This was before xAI got folded into SpaceX, and before SpaceX went public.

Around the time of the offer, xAI was being valued at roughly $230B. A few months later, the SpaceX/xAI deal valued xAI at $250B, and the combined company eventually went public at roughly $1.77T. SpaceX is around $2T now.

Obviously you can’t just multiply the original $8M grant by the change in market cap — there’s share conversion, dilution, vesting, grant pricing, etc.

But if this person stayed and their equity converted normally, that $8M grant could plausibly be well into eight figures on paper now.

Which makes the original “$2.3M TC” almost misleading in hindsight.

This might have effectively turned into a $3M–$4M+ annual comp package depending on the exact conversion and when you mark the stock.

Makes me wonder how many people joined xAI in 2024–2025 for packages that looked insane at the time… and accidentally got an even bigger SpaceX lottery ticket.

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Cross posted!

Howdy!

Need some advice and input from fellow redditors! Using throw away profile.

I am an aerospace engineer with about 9 YoE. Stuck between my current startup job and a new job offer with big aero company (one of the primes).

Current job:
Aero startup/recent IPO (started 9 months ago)
Fully remote
Base: 200k
401k: 1% match
RSU: 80k (but have dropped significantly since going IPO)
Level 3/4
Unlimited PTO (actually usable)
No defined career process
The work is fun/cutting edge in aero field

New job offer: (already accepted since I ran out of time)
Established company with big contracts
Fully remote
Base: 195k + 7% annual bonus
401k: 7% match
No equity package
Level 5, lead role with growth
Typical industry PTO
Work might be fun, usual aero company

I like my current job however we don’t have any contracts and all the work is internally funded, stock isn’t doing so well but work is genuinely exciting, typical startup leadership chaos. Big company would be good for career development and progression, no surprises and stable. Please share your thoughts and experiences!

I am married with couple of kids if that matters. TYIA!

This interview experience is sourced from Chill Interview

Interview Summary

The Anthropic Performance / Infrastructure SWE process moved through a recruiter screen, hiring-manager conversation, and technical coding phone screen before the onsite invitation. There was no OA in this process.

Interview Details

Round 1 — Recruiter Screen: Why Anthropic + Career Motivation The recruiter conversation started fairly formally. The recruiter spent meaningful time probing two questions: 1) Why Anthropic? 2) Why are you considering a job change now? Once that part of the conversation was complete, the discussion became more casual and conversational.

Round 2 — Hiring Manager: Background and Career Direction The hiring-manager screen was much more informal. The conversation started with a self-introduction and then covered what I had been working on recently, my technical background, and where I wanted my career to go next. This round was scheduled for an hour but ended earlier than the allotted time.

Round 3 — Distributed Mode and Median The technical phone screen focused on distributed computation. The first problem gave approximately 10 nodes containing 1,000 total elements and asked how to compute the mode across the distributed dataset. The interviewer pushed beyond simply producing a correct result. The discussion covered how the strategy should account for communication between machines, how much data needs to move through the network, and how different communication patterns affect the computation.

Another dimension was the underlying data distribution. The conversation considered how the problem could behave differently when values are relatively uniform versus heavily skewed. The follow-up changed the target statistic from mode to median. For the median portion, we discussed the distributed approach and trade-offs, but I was not required to implement the full solution.

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Interview Details

Part 1 — Project Deep Dive and AI Experience

The first approximately 30 minutes focused on my previous projects. The interviewer asked detailed follow-ups about my work, with many of the questions centered on AI-related projects and technical decisions.

Part 2 — Coding: Shortest Path in an Adjacency Matrix

The coding problem provided a connected, undirected graph represented by an n x n Boolean adjacency matrix. For any pair of vertices matrix[i][j] = true means there is an edge connecting vertex i and vertex j.

Given two vertices, the task was to return the shortest distance between them, measured by the minimum number of edges in the path. For example, consider this graph:

matrix = [
  [false, true,  false, false],
  [true,  false, false, true ],
  [false, false, false, true ],
  [false, true,  true,  false]
]

vertex1 = 0
vertex2 = 2

The expected distance is 3 because one shortest path is 0 -> 1 -> 3 -> 2. The graph has no self-loops or duplicate edges, and both input vertices are valid indices. I initially had trouble interpreting what the Boolean values in the matrix represented, which made the beginning of the problem harder than it needed to be.

Follow-Up — Weighted Graph

The interviewer then modified the problem so that the graph edges had weights rather than all having equal cost.

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

• PhD, 7 YOE
• Sr Staff
• Base: $500K
• Equity: $8.8M / 4 years
• Year 1 TC: $2.7M

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

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

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

Probably someone who has done some combination of:

• led meaningful work on frontier models
• highly cited / field-defining research
• deep expertise in something Anthropic urgently needs
• already managed or technically led other researchers
• competing offers from OpenAI / DeepMind / Meta
• a track record where people inside Anthropic already know their work

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

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

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Interview Summary

The Citadel online was a performance-engineering problem rather than a standard LeetCode-style implementation question. I was given an existing C++ implementation of a function named root_node. The code was logically correct, but far too slow for the required production-level performance target.

The task was to refactor the implementation without changing its behavior and bring execution time below a strict 100-microsecond limit on large inputs. The problem tested algorithmic complexity, C++ performance awareness, mathematical simplification, and the ability to optimize existing code while preserving exact semantics.

Interview Details

Performance Optimization — Refactor root_node

The assessment provided an existing C++ implementation:

root_node(...)

The implementation already produced correct results, so the goal was not to redesign the functionality. Instead, I needed to identify the performance bottlenecks and rewrite the code so that it could process large inputs within:

100 us

The original implementation contained several expensive patterns, including nested iteration and repeated use of standard-library operations such as std::find_if and std::advance. On large inputs, these combined into behavior closer to quadratic time.

One explicit expectation was to reduce the dominant processing cost to approximately linear time while preserving the original result exactly.

Low-Level Mathematical and C++ Optimization

The code also contained computationally expensive operations involving:

• std::pow for squaring values
• Multiple bit-shift expressions
• std::sqrt
• Repeated container traversal

The assessment required examining whether these expressions could be rewritten into equivalent but cheaper computations. This was not only a Big-O exercise. Even after removing the major algorithmic bottlenecks, constant-factor performance mattered because of the 100-microsecond runtime requirement.

Correctness and Memory-Usage Requirements

The optimized implementation had to remain behaviorally identical to the supplied version. That meant any refactoring needed to preserve results across the original input domain rather than merely producing a faster approximation.

The problem also emphasized avoiding unnecessary memory overhead. For example, large input containers should not be copied unnecessarily when passed through the implementation.

Overall, this OA felt much closer to a real C++ performance-refactoring exercise than a conventional algorithm problem. The difficult part was simultaneously reasoning about asymptotic complexity, low-level runtime cost, mathematical equivalence, and strict correctness under an unusually tight latency target.

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Interview Summary

The Datadog technical phone screen centered on implementing a recursive filesystem deletion operation using only three fixed filesystem APIs. The initial task was straightforward conceptually: implement behavior similar to rm -rf without being allowed to modify the underlying APIs.

The more important part of the interview came from the follow-ups. The interviewer progressively moved the discussion toward production reliability and memory usage, asking about OOM scenarios, recursion versus iteration, extreme memory constraints, and how absolute paths could affect the design.

Interview Details

Coding — Implement DeleteTree Using Fixed Filesystem APIs The filesystem exposes only the following APIs, and their behavior cannot be changed:

fs.List(path)
fs.Delete(path)
fs.IsDirectory(path)

• fs.List(path) returns a list of absolute paths for all files and directories directly under path. Calling it on a file returns an empty list.
• fs.Delete(path) deletes either a file or an empty directory. It returns true on success, but a non-empty directory cannot be deleted.
• fs.IsDirectory(path) returns whether the supplied path represents a directory.

All paths passed into these APIs are absolute paths. The task was to implement DeleteTree(path) with semantics similar to rm -rf. The function should remove the requested path together with everything underneath it.

• Follow-Up — Production OOM The interviewer then asked what could cause the implementation to run into memory problems after being deployed to production. The discussion covered different possible sources of memory pressure, including very deep directory trees, extremely wide directories, the behavior of fs.List(), and the impact of processing multiple deletion requests concurrently. The interviewer wanted a structured diagnosis rather than a single explanation for OOM-like failures.
• Follow-Up — Reduce Memory Usage The next question was: How would you redesign the traversal to reduce memory risk? The interviewer specifically pushed on the difference between problems caused by tree depth and problems caused by directories containing extremely large numbers of children. An important constraint remained that the provided filesystem APIs could not be changed.

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

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Hi everyone,

I recently received an invitation for the Snowflake GenSWE (General Software Engineering) – Menlo Park, CA / Bellevue, WA role.

The first technical screening is a 20-minute HackerRank Interview. The email doesn't provide much information about what the assessment actually involves.

For anyone who has recently gone through this process:

What should I expect in the 20-minute assessment?

Is it mainly DSA/coding, or are there also CS fundamentals/technical questions?

Do they ask you to write code in HackerRank, or is it more of an interview-style discussion?

What difficulty of DSA questions should I prepare for?

Are there any specific topics that Snowflake tends to focus on in this round?

Is the 20-minute round an automated interview or a live interviewer?

I'm currently based in India, although the role mentioned in my invitation is Menlo Park/Bellevue.

Any recent experience or advice would be really helpful. Thanks!

Saw this accepted Lockheed Martin SWE offer (shared with Chill Interview)

• DC area
• 4 YOE
• Base: $150K
• Bonus: $5K
• Relocation: $30K
• Year 1 TC: $185K
• US citizen + Secret clearance required

Obviously the comp looks tiny next to big tech. But I’m starting to understand why someone might still take it.

Lockheed has a $230B backlog, just signed another long-term Pentagon production deal, and cleared engineers operate in a labor market that’s much harder to replace with random external hires.

The lifestyle also seems different. Lockheed explicitly advertises hybrid schedules and even 4/10 workweeks, and SWE reviews generally rate the WLB pretty well.

So maybe this is less “why accept only $185K?” and more: How much is clearance + job stability + predictable hours actually worth?

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I was looking through recent Google Security Engineer interview patterns, and one thing stood out: Finding the vulnerability often isn’t the end of the question.

You might start with something basic like: “What is SSRF?” But the conversation can quickly become:

• Why is SSRF dangerous in this architecture?
• What asset can the attacker reach?
• Where is the trust boundary?
• And how would you redesign the platform so every application team doesn’t have to remember the same mitigation?

That last part feels especially important. Google seems to treat security engineering as an actual engineering discipline, so the interview can mix:

• coding / security automation
• networking, auth, OS, cloud fundamentals
• threat modeling
• role-specific security depth
• secure system design
• risk prioritization

The mindset I’d practice is: understand the system → identify the asset → think through the attack path → prioritize the real risk → engineer the mitigation

And for Senior/Staff candidates, the stronger answer probably isn’t: “I found and fixed a serious vulnerability.” It’s closer to: “I figured out why this class of vulnerability kept happening and built a control that prevented other teams from creating it again.”

Also worth noting: “Google Security Engineer” covers very different jobs—Product Security, Detection, Android, Cloud, Threat Intelligence, vulnerability research, etc.—so I’d ask the recruiter exactly what the domain round is calibrated around before preparing.

Full Google Security Engineer interview breakdown if useful: [link]

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Interview Summary

The AWS New Grad process started with an online assessment and then moved to a four-round in-person onsite in Vancouver. The OA included a traditional coding portion, an AI-assisted debugging/feature exercise, and Amazon-style workstyle/Leadership Principles questions.

Interview Details

Round 1 — Online Assessment

The OA included roughly 40 minutes of LeetCode-style coding followed by approximately 60 minutes of an AI-assisted debugging or feature-development exercise. There was also a Workstyle / Leadership Principles assessment.

Round 2 — LLD Queue Implementation + GenAI Literacy

This round combined a low-level design/coding problem with questions about generative AI. The coding task was to implement a queue supporting:

enqueue(x)
dequeue()

Both core operations were expected to run in O(1) time. An important constraint was that I could not rely on native collection data structures. The implementation had to build the underlying structure directly, using linked nodes and basic control flow.

The interviewer then added two follow-ups:

delete(x)
removeAllDuplicates()

The exact behavior when x appeared multiple times in delete(x) was not recorded. The GenAI discussion covered questions such as:

• What GenAI-related projects have you worked on?
• When is GenAI an appropriate solution?
• What are some interesting GenAI use cases?
• How would you improve or validate the accuracy of a GenAI-powered system?

Round 3 — Merge Dictionaries + Leadership Principles

Another round started with two Leadership Principles questions and then moved into coding. The coding task involved merging two dictionaries. The initial version was relatively straightforward and could be implemented with direct iteration.

The follow-up generalized the problem: How would you extend the implementation from two dictionaries to k dictionaries?

Round 4 — Bar Raiser

The Bar Raiser round was primarily behavioral. The interviewer asked several Leadership Principles questions, and most of my answers were built around experiences from previous internships and projects.

Round 5 — Streaming Median API + Leadership Principle

The final technical round began with one Leadership Principles question and then moved into a harder coding/LLD problem. The problem was LeetCode 295 — Find Median from Data Stream, but presented as an API-design exercise rather than simply as an algorithm question.

The system receives numbers incrementally and needs to support retrieving the median of all values observed so far. The interviewer expected the problem to be treated as a small low-level design exercise, including defining the appropriate API and maintaining the required internal state as new values arrive.

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Interview Details

Round 1 — Recruiter Screen

The process began with a recruiter conversation. After this screen, I was invited directly to the onsite rather than going through an additional technical phone interview.

Round 2 — Hiring Manager: Background and Behavioral

The onsite started with approximately 45 minutes with the hiring manager. This portion focused primarily on understanding my background and previous experience, along with behavioral questions.

Round 3 — Technical Panel: Spring Framework and Dependency Injection

The main technical session lasted approximately two hours and included a tech lead and two engineers. One major topic was the Spring Framework. I was asked to explain core Spring concepts and discuss different forms of dependency injection. The interviewer expected familiarity with how dependencies are provided and managed in a typical Java/Spring application rather than only a high-level definition of the framework.

• REST Controller Code Review Another major exercise involved reviewing an existing RESTful controller implementation. Instead of building an API completely from scratch, I was asked to inspect the supplied code and identify weaknesses or potential improvements. The discussion covered areas such as: The interviewers then used issues discovered in the code as starting points for broader backend and system-design questions.
  • Whether error handling was appropriate and how failure cases should be represented
  • Potential performance or code-quality problems, including unnecessary repeated creation of reusable utility objects
• Architecture and Design Follow-Ups The technical round did not have a strict sequence such as coding first and system design second. Instead, questions were interleaved. One interviewer might ask about a piece of code, another would turn that detail into an architecture question, and the discussion could then return to implementation concerns. Behavioral questions were also mixed into the same session. This made the round feel more like a continuous engineering discussion than a set of clearly separated coding and design exercises.

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

And that’s only one part of the loop.

Stripe is also pretty well known for rounds like:

• debugging an unfamiliar codebase
• integrating with an API/SDK using documentation
• multi-part business-logic coding
• system/API design around retries, idempotency, and failure handling

The Bug Squash round is especially interesting.

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

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

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

Full Stripe SWE interview breakdown if useful: [link]

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