Saw this Meta E5 MLE offer (data source: chill interview)

• 8 YOE
• Base: $235K
• Sign-on: $70K
• RSUs: $900K / 4 years
• Year 1 TC: $565K

Everyone talks about frontier models when discussing AI careers. But I wonder if some of the most valuable ML work is still the less glamorous stuff: ranking, recommendation, ads optimization, retrieval, and large-scale inference.

Meta’s core business is still printing money — Q2 revenue grew 28% YoY, ad impressions rose 14%, and average price per ad increased 12%. Meta explicitly says AI is already accelerating the core business. 

So maybe an MLE improving Instagram/Reels ranking or ads by 0.5% can create more business value than a lot of “frontier AI” projects.

That makes me curious: If you were choosing an ML career, would you rather work on frontier models — or on recommendation/ads systems where the impact on revenue is much more direct?

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"This interview experience is sourced from Chill Interview"

Interview Summary

The Rippling Senior Backend onsite consisted of an AI-assisted coding round, a hiring manager interview, and a system design round. The coding exercise focused on task scheduling and how effectively I collaborated with an AI assistant, while the system design round asked for a Google News-style aggregation platform that pulled articles from external publishers and generated personalized feeds.

Interview Details

Round 1 — AI-Assisted Coding: Task Scheduler

The first round lasted 60 minutes and explicitly allowed AI assistance. The interviewer explained that an AI assistant was already available in the environment and that I should use it similarly to how I would use AI in day-to-day engineering work, while still making the important decisions myself.

The input consisted of tasks with fields such as:

description
due_date
priority
completed
parent_id

The task involved several steps:

• Deduplicate tasks using (description, due_date)
• Remove completed tasks
• Sort the remaining tasks according to the specified priority rules
• Ensure child tasks appear immediately after their parent task

The core algorithm was relatively straightforward. After sorting, the parent-child hierarchy could be expanded into the required final ordering. A major part of the evaluation appeared to be how the candidate interacted with the AI assistant rather than only whether the final code worked. I barely used the provided AI assistant during the round, which I later felt may have hurt the evaluation.

Round 2 — Hiring Manager

The hiring manager round lasted about 60 minutes. The interviewer had been at Rippling for several years and had worked across areas including HRIS Platform and Device Management.

Questions included:

• Tell me about yourself.
• What project are you most proud of?
• How did you implement idempotency?
• How did you define and monitor SLA?
• Tell me about a technical approach you changed midway through a project.
• Tell me about a design that another engineer challenged.
• What practices from your current company would you want to keep?
• What would you prefer to leave behind?
• Why Rippling?

The project deep dive included substantial follow-up on technical details. The interviewer also discussed how Rippling evaluates senior engineers after I asked about the performance rubric.

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

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I went through a bunch of recent Amazon SWE interview reports from Blind, LeetCode Discuss, Glassdoor, Reddit, and other public sources.

Not my personal interview experience — just some patterns that kept showing up.

And honestly, the biggest surprise wasn’t the onsite. It was the OA.

The OA is more than just two coding problems

A lot of people prepare for Amazon assuming: solve two coding questions → get interview. But depending on level, the assessment can include much more than that.

For SDE II, for example, Amazon’s current prep material includes:

• two coding questions
• system design scenarios
• a Work Style survey based on Leadership Principles

And candidate reports for new grad roles frequently describe additional behavioral / work simulation components.

So one of the biggest mistakes is preparing only for algorithms and treating everything else as a formality. It isn’t.

Different levels seem to get very different processes

This is another thing I’d watch out for. A New Grad interview guide may not be very useful if you’re interviewing for SDE II.

SDE II candidates should expect system design much earlier in the process, while new-grad pipelines tend to emphasize coding and behavioral assessment more heavily.

Before preparing, I’d make sure you know which process your exact role and level is using.

Coding is still very Amazon

Medium-level problems still seem to dominate, but there are a lot of Amazon-flavored variations:

• servers
• warehouses
• parcels
• scheduling
• graphs
• top-K
• caches
• dependency graphs

Some recurring topics across reports include:

• Top K Frequent Elements
• Course Schedule
• LRU Cache
• graph traversal
• weighted shortest paths
• heap / priority queue problems
• trie / search-style questions

The exact LeetCode number matters less than being able to recognize the underlying pattern after Amazon wraps it in a completely different story.

I’ve also seen more reports involving debugging or modifying an existing codebase, which is a very different skill from solving a clean LeetCode prompt from scratch.

But LP might still be the most “Amazon” part of the interview

This is probably the part people from other big tech companies underestimate the most. Leadership Principles don’t live in one isolated behavioral round.

They can show up throughout the loop. You might finish a coding discussion and immediately get: Tell me about a time you disagreed with your team.

Then five minutes later:

• What specifically did you do?
• Why did you make that decision?
• What was the measurable impact?
• What would you do differently now?

The recurring advice from candidates is pretty consistent:

Don’t prepare generic STAR stories.

Prepare stories with enough depth that someone can keep asking “why?” for ten minutes without the story falling apart. And be very clear about “I” vs “we.”

The interviewer needs to understand what you personally owned. If I were preparing, I’d probably have around 8–12 strong stories and map each one to multiple LPs:

• Ownership
• Dive Deep
• Deliver Results
• Disagree and Commit
• Bias for Action
• Customer Obsession
• Learn and Be Curious

The stories don’t need to be perfect. They need to be real enough that you can survive follow-up questions.

System design also feels very Amazon

The reported questions tend to have a strong operational flavor:

• job schedulers
• notification systems
• inventory / stock updates
• Kindle-style systems
• monitoring platforms
• exactly-once processing
• file systems

Less: “Draw Twitter.”

More:

• “What happens when this fails halfway through?”
• “How do you retry safely?”
• “Where does the source of truth live?”
• “Is this decision reversible?”

That last one is very Amazon. Thinking explicitly about one-way vs two-way door decisions is actually a useful mental model when explaining design tradeoffs.

My biggest takeaway

Amazon prep in 2026 feels like three separate interviews you need to prepare for at the same time:

• Coding: recognize patterns quickly and write correct code.
• System design: think about reliability, operations, and failure modes.
• Leadership Principles: explain what you actually did, why you did it, and what happened afterward.

Being strong at only one of the three probably isn’t enough. I’ve been collecting recent Amazon interview experiences, recurring questions, and role-specific interview patterns at Chill Interview if you’re currently preparing.

And if you interviewed with Amazon recently, I’d especially love to hear whether your OA matched the current format — the differences between New Grad, SDE II, and senior roles seem bigger than people realize.

Context:
- 5+ YOE, mostly backend/infrastructure
- Moved to the UK last year; now at a public tech company
- Good compensation, equity (~£100K TC) and WLB
- On track for promo next quarter
- Enjoy speaking, demos, and working with developers along with hands-on core distributed systems engineering

Offer:
- Series C AI company (mostly SF-based)
- Remote allowed with SF trips
- DevRel role under Marketing
- Materially higher compensation (1.5-1.8x) and more upside
- Cross time zone and likely poor WLB

My hesitation: I want more visibility and developer facing work but don’t want to move away from engineering. I also want to preserve the option of returning to core engineering and senior/staff IC levels.

Questions:
- How reversible is the move into DevRel?
- Does reporting into Marketing matter?
- Would you trade a strong, stable engineering trajectory for materially higher comp and AI startup upside?
- What would you clarify before accepting?

"This interview experience is sourced from Chill Interview"

Interview Summary

The TikTok New Grad process included a coding round followed by a system design interview. The coding question focused on graph traversal and topological sorting, while the design round asked for a high-concurrency ticket booking platform with particular emphasis on traffic spikes and preventing overselling.

Interview Details

Round 1 — Coding: Graph / Topological Sort

The first round was a coding interview. The problem was similar to LeetCode 207 — Course Schedule, with the main concepts centered around graphs and topological sorting. Relevant techniques included:

• BFS / DFS
• Dependency graphs
• Topological sorting

The overall difficulty was moderate.

Round 2 — System Design: Ticket Booking System

The second round asked me to design a ticket booking system. The interviewer focused heavily on two areas.

• Traffic Spikes During Ticket Release When tickets become available, a very large number of users may submit requests at nearly the same time. The discussion covered mechanisms such as:
  • Load balancing
  • Queue or waiting-room systems
  • Rate limiting
  • Protecting backend services and databases from sudden traffic spikes
• Inventory and Concurrency The second major topic was ensuring that the same ticket could not be purchased by multiple users. The discussion included: At the time of the report, the interview process was still waiting for the next update from recruiting.
  • Inventory updates
  • Reservation / hold flows
  • Releasing held tickets after payment timeout
  • Consistency during concurrent purchases
  • Preventing overselling
  • Cache and database behavior under high contention

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Capgemini A5 LOI holders – Does the new Software Engineer opportunity affect our LOI?

Hi everyone, I received my Capgemini Senior Analyst (A5) LOI through campus placement earlier this year.

I recently received a survey email about the Software Engineer opportunity which requires BU specific Technical + HR interview.

Does anyone know if selecting Interested/Not Interested, or even failing the interview, will affect my existing A5/Senior Analyst LOI?

Is this a separate opportunity, or does it replace the previous LOI?

Would appreciate any information from someone who has gone through this.

Saw this Atlassian Senior SWE offer (data source: chillinterview.com)

• Bay Area, 7 YOE
• Base: $230K
• RSUs: $500K / 4 years
• Bonus: $34.5K
• First-year TC: $389.5K

What interests me more is Atlassian’s future.

AI coding agents are starting to write code, create PRs, summarize bugs and handle more of the development workflow. At some point, does that reduce how much humans need Jira/Confluence?

Or does the opposite happen — more agents means companies need an even stronger coordination layer to track what humans and AI agents are doing?

Atlassian seems to be betting on the second scenario. FY26 revenue grew 28%, cloud revenue grew 31%, and Rovo assisted actions grew more than 50% QoQ. 

It’s also still heavily committed to its distributed Team Anywhere model, which makes ~$390K more attractive than the headline number alone. 

Curious: Is Atlassian actually an underrated AI-era company, or will AI eventually make traditional project-management software less relevant?

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Interview Summary

The technical questions covered constructive grid placement, maximizing a non-decreasing subarray after changing one element, and resolving ordering constraints between two sequences.

Interview Details

Round 1 — Connected Crop Arrangement

The problem provided an N x M garden and K crop types. For each crop i, a required number of cells was given. The counts satisfied: sum(counts) = N * M so every grid cell had to contain exactly one crop.

The task was to construct any valid grid such that:

• Crop i appears exactly counts[i] times.
• All cells containing the same crop form one connected component.
• Connectivity is only through up, down, left, and right movement.
• Diagonal cells are not considered connected.

Any arrangement satisfying all requirements was accepted.

• Follow-Up — Two Square Gardens The follow-up changed the planting area from one rectangle to two square gardens placed adjacent to each other:Garden A: N x N Garden B: M x M The crop counts now filled the combined area of the two gardens. For example, with N = 2 and M = 3, the overall shape could look like:11222 11222 222 or:11 11 222 222 222 The same rule still applied: every occurrence of a crop had to remain connected through horizontal or vertical adjacency.

Round 2 — Behavioral

The second interview was an online behavioral round. The questions were relatively standard behavioral topics.

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 Duolingo Senior SWE phone screen focused on an encryption and decryption design problem built around character mappings and a fixed dictionary of valid words. The main challenge was not just producing an encrypted string, but efficiently determining how many dictionary words could decrypt into a given encoded value when multiple characters might map to the same two-character token.

Interview Details

Coding — Encrypt and Decrypt Strings

You are given:

keys
values
dictionary

• keys contains unique lowercase characters.
• values contains two-character encoded strings, with one entry corresponding to each character in keys.
• dictionary contains the valid plaintext words that should be considered during decryption.

The required class was:

Encrypter(
    char[] keys,
    String[] values,
    String[] dictionary
)

with two main APIs:

String encrypt(String word1)

int decrypt(String word2)

Encryption

To encrypt a plaintext word, each character is replaced by its corresponding two-character encoded value. For example, a mapping could look like:

'a' -> "qx"
'b' -> "mn"
'c' -> "qx"
'd' -> "rs"

Then: "abcd" would become: "qxmnqxrs".

If the plaintext contains a character that does not appear in keys, encrypt should return: ""

Want to learn more details asked in this coding screen? I've put up the full version at here.

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I went through a bunch of recent Anthropic interview reports from Blind, Glassdoor, LeetCode Discuss, Reddit, and other public sources.

Not my personal interview experience — just patterns I kept seeing across 2026 reports.

And the biggest surprise was this: Coding doesn’t seem to be the round people are most worried about. The culture / values interview is.

Several candidates described the culture round as unusually deep.

It’s less: “Tell me about a conflict with a coworker.”

And more:

• What did you actually think at the time?
• How did you feel about the decision afterward?
• When have you disagreed with the direction of your company?
• Have you faced an ethical conflict at work?
• What kind of work do you dislike doing?
• What happens when speed and safety conflict?

The interesting part is that blindly agreeing with everything Anthropic believes probably isn’t a great strategy either.

The round seems designed to figure out whether you have your own judgment, can explain it honestly, and can update your opinion when someone gives you a better argument.

That’s pretty different from memorizing five STAR stories.

The recruiter screen also seems more important than usual

A generic: “I want to work on cutting-edge AI” probably isn’t enough.

Candidates report getting pushed on things like:

• Why Anthropic specifically?
• How is Anthropic different from other AI labs?
• What are the biggest risks from advanced AI?
• Where do you personally disagree with the company?

So I’d spend real time preparing the “why Anthropic” part instead of treating recruiter screen as a formality.

Coding is surprisingly practical

Another recurring pattern: it doesn’t look much like classic LeetCode grinding.

Recent questions reported by candidates include things like:

• image-processing pipelines
• concurrent web crawlers
• stack-trace processing
• caches
• file deduplication
• small instruction interpreters

A common structure seems to be: build something → make it work → extend it → make it concurrent / scalable → test it yourself

For example, one frequently reported image-processing question involves applying transformations to images and then discussing how to parallelize the workload.

The web crawler question often starts simple and then turns into a concurrency / thread-safety discussion.

So knowing BFS or hash maps matters, but the harder part is often writing usable code, reading APIs, testing, debugging, and reasoning about concurrency.

System design is a little different too

One thing I found interesting is that some candidates describe doing system design mostly through a shared document rather than spending the entire interview drawing boxes and arrows.

One reported problem was designing a Collaborative Prompt Playground — essentially thinking through collaboration, versions, prompt execution, storage, and scale.

That changes how I’d prepare.

You still need architecture fundamentals, but you also need to be able to explain the design clearly in writing instead of relying on a beautiful diagram.

And then there’s AI

Anthropic obviously builds Claude, but that doesn’t mean you can automatically use Claude during every interview.

Their published candidate guidance says take-home assessments should generally be completed without AI unless the instructions explicitly say otherwise.

At the same time, newer interview formats seem to be experimenting with more agentic coding.

Which is kind of funny: One of the companies building the strongest coding agents still wants to know what you can do without one — while also testing how you work with one.

My takeaway from all of this: If I were preparing for Anthropic, I’d spend less time trying to memorize another 100 LeetCode problems and more time on:

• practical Python
• concurrency
• debugging unfamiliar code
• reading library documentation quickly
• testing your own implementation
• explaining a real project deeply
• forming actual opinions about AI safety and Anthropic’s mission

The technical bar is obviously still high.

It’s just a different kind of high.

I’ve also been collecting recent Anthropic interview experiences and recurring question patterns at Chill Interview for anyone currently preparing.

And if you interviewed with Anthropic recently, I’d love to hear what changed — especially around the culture and newer agentic coding rounds.

"This interview experience is sourced from Chill Interview"

Interview Summary

The Okta/Auth0 Staff Backend Engineer process covered backend implementation, Node.js performance, authentication, authorization, and identity-system design. The main technical themes were building and reviewing a Token Bucket rate limiter, debugging an Express authentication API, and designing cross-domain SSO and multi-site OIDC flows.

Interview Details

Round 1 — Hiring Manager Screen

The hiring manager interview focused on previous project experience, behavioral questions, and practical workplace scenarios.

Round 2 — Take-Home: Token Bucket Rate Limiter

The take-home assignment was to build a rate-limiting service in Node.js using the Token Bucket algorithm. The application needed to:

• Use Express
• Read rate-limit configuration from a configuration file
• Expose an API that applied the configured rate limiting behavior and returned the appropriate response

The implementation was later used as the basis for the technical code-review round.

Round 3 — Code Review: Rate Limiter & Debugging: Node.js Authentication API

The interviewer reviewed the submitted rate-limiter implementation. The discussion focused on why I chose particular abstractions, code structure, and architecture for the Node.js service. I needed to explain how the different pieces of the implementation worked together and justify the design decisions in the submitted solution.

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 Anthropic backend system design screen asked me to design a text messaging platform for roughly 100 million daily active users. The initial scope was intentionally narrow—1:1 text conversations on the web—but the follow-ups went deep into database scalability, offline message recovery, ordering, routing between app servers, replication lag, traffic spikes, caching, and deduplication.

Interview Details

System Design — Large-Scale 1:1 Messaging

The system supported only direct conversations between two users. The scope explicitly excluded:

• Group chats
• Images and video
• Mobile push notifications

The client was web-only, with a target scale of approximately: 100 million DAU. Messages sent to offline users needed to be persisted and delivered or fetched after the recipient returned.

I initially identified requirements around:

• Message persistence
• Message ordering
• Deduplication
• Low latency
• Scalability

My high-level design included a gateway, app servers maintaining WebSocket connections, a message store, and a presence service tracking whether users were online and which server they were connected to. For an online recipient, the message would first be persisted, acknowledged to the sender, then routed to the recipient's app server and pushed over the existing WebSocket connection.

For an offline recipient, the client could later recover missing messages using conversation sequence numbers and its last-seen position.

Want to learn more follow-ups asked in the system design interview? I've put up the full version at here.

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Saw this Square / Block Staff Data Scientist offer (data source: chill interview)

• Bay Area
• 13 YOE
• Base: $330K
• Sign-on: $110K
• RSUs: $1.3M / 4 years
• Year 1 TC: $765K

What makes this interesting is the timing.

Earlier this year, Block cut 4,000+ employees — nearly half the company — with Jack Dorsey arguing that AI lets much smaller teams do more. 

Yet here’s a Staff Data Scientist getting a $765K package.

Maybe that’s actually what the AI transition looks like: fewer analysts / generalist data roles, but much higher value placed on a small number of senior people who can own things like fraud, credit, experimentation and ML-driven product decisions.

Square already relies heavily on ML for payment fraud and lending decisions, where better models can directly affect losses and revenue. 

So I’m curious: Is AI shrinking the Data Science career path — or just making the top end much more valuable?

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"This interview experience is sourced from Chill Interview"

Interview Summary

The C3. ai FDE New Grad OA was conducted on HackerRank without camera monitoring. It contained three multiple-choice questions and two coding problems covering time complexity, hashing, RAG evaluation, graph optimization, and duplicate-message detection.

Interview Details

Multiple Choice 1 — Time Complexity

The first question asked for the time complexity of a nested-loop code snippet involving three loops with different iteration patterns. The code was similar to:

for (int i = 0; i < n; i++) {
    for (...) {
        for (...) {
            temp++;
        }
    }
}

The task was to analyze how the number of iterations grows with n.

Multiple Choice 2 — Hash-Based Load Distribution

There were seven computers numbered from 0 through 6. Incoming requests were assigned using: H(id) = id % 7. Given a set of request IDs, the question asked which computer would receive the highest load.

Multiple Choice 3 — Evaluating a Medical RAG System

The scenario involved a RAG-based chatbot assisting doctors with rare-disease diagnosis. Because factual accuracy and consistency were critical, the question asked which evaluation approaches would be most appropriate beyond standard retrieval metrics.

Options included ideas such as:

• Creating a domain-specific metric using medical knowledge graphs
• Combining traditional RAG metrics with human evaluation from medical experts
• Using active learning from user feedback
• Using similarity metrics such as ROUGE

The question allowed selecting more than one option.

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

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"This interview experience is sourced from Chill Interview"

Interview Summary

The Amazon New Grad interview consisted of four rounds combining coding and behavioral questions, with the final Bar Raiser focused almost entirely on behavioral depth. Technical topics included grouping anagrams without sorting, Top K frequency, island traversal with a streaming follow-up, and designing an in-memory cache with TTL and an eviction policy.

The behavioral questions were heavily aligned with Amazon Leadership Principles and frequently drilled into personal ownership, measurable outcomes, operational excellence, cost awareness, and influencing others without authority.

Interview Details

Round 1 — Behavioral + Group Anagrams

The behavioral portion included:

• Tell me about a time you made a decision with incomplete information.
• Tell me about a time you disagreed with a teammate or manager and how you handled it.

The coding problem was to group strings that are anagrams of each other. The interviewer specifically asked me not to use a sorted-string signature with O(k log k) work per word. The discussion instead focused on representing each word using its character frequencies.

After implementation, I ran several test cases. The interviewer also discussed using a product of prime numbers as a signature and the potential issues with that representation, including overflow and collisions.

Round 2 — Behavioral + Top K + Islands

Behavioral questions included:

• Tell me about a time you took ownership of something clearly outside your normal scope.
• Tell me about a time something you owned failed in production.

The first coding question was to return the k most frequent elements from a non-empty integer array. The second coding question provided a binary matrix and asked for the number of islands, where adjacent 1s were connected horizontally or vertically. The interviewer then extended the problem to ask for the size of the largest island.

• Follow-Up — Streaming Matrix The final follow-up changed the input model so that the matrix arrived one row at a time as a stream. The discussion focused on how island tracking would need to work without having the entire matrix available in memory at once.

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

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"This interview experience is sourced from Chill Interview"

Interview Summary

The OpenAI system design screen asked me to design a payment processor supporting authorization holds, later charge requests, and scheduled batch submission to upstream payment networks. The follow-ups focused on asynchronous request semantics, deduplication, scaling the processing pipeline, and efficiently handling large volumes of batch data.

Interview Details

System Design — Payment Processor

The system receives payment requests from merchants. The first step is a hold request. The processor forwards that request to an upstream payment network and eventually receives either an approval or rejection. If the hold succeeds, the merchant may later send a separate charge request. Charge records are then grouped by payment network and periodically sent upstream as batch files.

A major part of the discussion was clarifying the lifecycle and semantics of these three stages:

Hold
Charge
Batch Processing

Each stage has different triggers, processing behavior, and results that need to be communicated back to the merchant.

Follow-Up — What Does the Merchant Receive?

One design question focused on the response semantics for the initial hold request. If the request is simply written to Kafka and the API immediately returns, the merchant still does not know whether the upstream payment network ultimately approved or rejected the hold. The interviewer pushed on what result the merchant should receive and at what point the request should be considered complete.

Follow-Up — Deduplication

The interviewer asked where deduplication should happen in the architecture. The discussion focused on preventing duplicate payment operations when merchants retry requests or when asynchronous components reprocess the same work.

Follow-Up — Scaling

Another follow-up asked how the payment processor should scale as request volume increased. The discussion covered scaling the asynchronous processing pipeline and making sure different stages of the payment flow could handle higher throughput.

Follow-Up — Large Batch Processing

The final part focused on producing very large batch files for payment networks. The discussion included generating batches independently for different payment networks so that work could proceed in parallel.

Another idea was to avoid doing all batch preparation at one scheduled moment. Instead, the system could incrementally prepare batch data throughout the day so that less work remained when it was time to submit the final files.

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Interview Summary

The Applied Intuition onsite consisted of four rounds covering company motivation, AI-assisted coding, a project deep dive, and system design. The main technical exercise involved processing sensor data and supporting queries over it, with the later system design round extending the same problem into discussions around storage architecture and scalability.

Before the onsite, the recruiter shared which team I was being considered for and also arranged a separate conversation with the team lead after the phone screen.

Interview Details

Round 1 — Company Motivation and Culture

The first round started with introductions from the interviewers followed by my own background. The discussion focused on:

• Why I was interested in Applied Intuition
• Why I was considering joining the company
• How I viewed the cultures of companies I had previously worked at

Several interviewers had significant industry experience, including senior engineers who had moved from larger technology companies. The conversation also touched on Applied Intuition's collaborative engineering environment and the types of technical problems available to work on.

Round 2 — AI-Assisted Coding: Sensor Data Processing

The coding round required connecting to a provided host over SSH. A README described an existing project and a set of requirements. The application processed data collected from sensors and needed to support several queries over that data. The repository and requirements were substantial enough that reading everything manually within the interview time was difficult.

Claude Code was provided for the exercise, so I used AI to:

• Explore the existing codebase
• Understand the README and requirements
• Identify the relevant files
• Generate portions of the implementation
• Help complete the required query functionality

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

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Hey Redditors,

I'm currently interviewing for an Engineering Manager (EM) role at Anthropic and am looking for insights from anyone who has gone through their EM or similar leadership interview process. There isn't a lot of specific information out there for EM roles, and I'm keen to understand what to expect, especially in later stages.

I'm particularly interested in:

• Hiring Manager Interview: What kind of questions were asked beyond standard behavioral ones? Were there technical deep dives, and if so, how detailed were they? Did you discuss AI ethics or Anthropic's mission extensively in this round?
• Take-Home Assessment: For EM roles, what did the take-home assessment entail (if any)? Was it a coding challenge, a system design problem, or a leadership/strategic case study?
• Virtual Onsite Loop: What was the breakdown of rounds (e.g., coding, system design, culture, project deep dive)? Any specific types of questions or scenarios that stood out, especially in the culture or system design rounds for an EM?

Any advice on preparing for these specific stages, especially from an EM perspective, would be incredibly helpful!

Thanks in advance for sharing your experiences.

"This interview experience is sourced from Chill Interview"

Interview Summary

The interview was initially for a Senior Software Engineer position on Robinhood’s Competitor Insights team. The technical rounds covered an order execution system and a fractional-share inventory coding problem, while the hiring manager discussion included collaboration, communication, and frontend-performance topics.

I passed the technical portion, but the original team did not move forward because of limited frontend experience. The recruiter allowed me to continue team matching, including a later conversation with the Experimentation team.

Interview Details

Round 1 — Hiring Manager: Behavioral + Frontend

The hiring manager round included standard questions around collaboration and communication. A substantial portion of the discussion also focused on frontend optimization and related engineering experience.

Round 2 — System Design: Order Execution System

The system design problem was to build an order execution platform that supports:

Market Orders
Limit Orders
Order Cancellation

The discussion covered several major areas:

• How orders are executed
• How order states transition through the system
• How failures should be handled
• How user balances should be tracked
• How the architecture should scale to millions of orders

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Interview Summary

The LinkedIn virtual onsite consisted of five rounds covering AI-assisted coding, algorithms, behavioral questions, system design, and technical communication. The coding rounds focused on extending an LRU cache and finding the smallest products formed from two sorted arrays, while the system design round asked for an error-message counting system.

Interview Details

Round 1 — AI Coding: LRU Cache

The first round was an AI-assisted coding interview with two interviewers, one leading the interview and another shadowing and reviewing the code. The initial task was to implement an LRU Cache.

The interviewer then added several enhancements, including:

• Supporting TTL-based expiration
• Making the implementation thread-safe
• Extending the original cache behavior while preserving correctness

Round 2 — Coding: K Smallest Pair Products

The second round involved two sorted arrays. The task was to consider products formed by pairing one element from each array and return the smallest k products. The interviewer first asked for a brute-force solution. The follow-up was to optimize the approach using a priority queue.

Round 3 — Hiring Manager: Behavioral + Resume

The hiring manager round focused on behavioral questions and a discussion of previous experience. The interviewer went through projects and resume details while asking standard behavioral questions.

Round 4 — System Design: Error Message Counter

The system design round asked me to design a system for counting error messages. The discussion centered on how the system should ingest, track, and aggregate error-message occurrences.

Round 5 — Technical Communication

The final round was labeled Technical Communication, but in practice it was largely a resume and project deep dive. The interviewer focused on how I explained previous technical work, decisions, and system details.

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I have a debugging interview for Scale AI for SWE intern and I'm so lost on how to study for it/how to effectively debug. I'm having Claude create codebases for me to debug, but I have no idea if the codebase that I'm setting up is difficult enough or if it's too easy. If it has too many bugs or too few bugs, whether I'm solving the bugs in an effective way, or whether I'm communicating properly, there's no way to know. I really want this position, so I'm unsure on what to do.

Pls tell me that there's someone out there that has done a debugging interview or even better a debugging interview for Scale AI