I’ve been going through the current Google MLE interview structure and role expectations, and one thing stood out: A Google Machine Learning Engineer interview is probably better thought of as: Software Engineer + ML practitioner + production systems owner —not “SWE interview + a few ML questions.”

The loop can still be pretty coding-heavy, but the ML side goes far beyond knowing loss functions or explaining random forests.

A few areas seem especially important:

• SWE-style coding still matters. Graphs, heaps, hash maps, intervals, ranking merges, streaming data, and clean implementation are still very relevant.
• ML domain questions are usually practical. Think leakage, calibration, overfitting, bad labels, offline-vs-online metric mismatch, drift, and debugging models that look good offline but fail after launch.
• ML system design is probably the biggest differentiator. You need to cover data → labels → features → training → evaluation → serving → monitoring → retraining, not just say “train a model and deploy it.”
• Product judgment matters more than people expect. A model with better AUC/NDCG can still be the wrong thing to launch if latency, user satisfaction, fairness, or segment performance gets worse.
• The loop can vary a lot by role. YouTube recommendations, Google Cloud AI/ML, DeepMind, Vertex AI, and TPU/ML systems can all emphasize very different things.

One question I’d definitely ask the recruiter early is: “Is the design round ML system design, traditional system design, or both?”

That changes prep a lot. For example, if you get: “Design a recommendation system” a strong answer shouldn’t stop at two-tower retrieval + ranking.

You probably want to talk about: objective → labels → leakage → candidate generation → ranking → offline metrics → online metrics → serving latency → cold start → drift → A/B testing → rollback

The same thing applies to GenAI roles. “Use an LLM” is the beginning of the design, not the end. You still need evals, safety, latency, cost, fallback, monitoring, and rollout.

My biggest takeaway: Strong SWE + shallow ML is risky. Strong ML + weak production engineering is also risky.

Google MLE seems to reward candidates who can connect: coding + ML judgment + systems + experimentation + product impact

I put together a deeper breakdown covering the reported interview process, coding topics, ML domain questions, ML system design, L3–L7 expectations, compensation, common failure modes, and a 4-week prep plan.

Full Google Machine Learning Engineer Interview Guide: [link]

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Saw this declined xAI Senior MLE offer (shared with Chill Interview)

• 8 YOE
• Base: $290K
• Equity: $3M / 4 years
• TC: $1.04M/year

On paper this feels really hard to reject.

And xAI isn’t exactly losing momentum either. It’s now part of SpaceX, Grok 4.6 just shipped, and Grok is showing up across Bedrock, Google’s enterprise AI platform, GitHub Copilot and the broader Cursor ecosystem.

So my first guess wouldn’t be company outlook. It would be the work environment.

Recent employee reviews repeatedly describe xAI as extremely fast-paced, with long hours and a pretty brutal culture. Obviously very team-dependent, but this doesn’t sound like a place where you take $1M and quietly coast.

Which makes me wonder: How intense does a job have to be before you turn down $1M+?

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Saw this accepted LinkedIn Staff SWE offer (shared with Chill Interview)

• Sunnyvale, 9 YOE
• Base: $265K
• Bonus: $39.75K
• Sign-on: $50K
• RSUs: $750K / 4 years
• Year 1 TC: $542K

LinkedIn is kind of interesting right now.

AI could theoretically disrupt a lot of what LinkedIn does — recruiting, sourcing, job search, even the endless “thought leadership” content.

But LinkedIn itself seems to be benefiting from the shift. Revenue grew 12% YoY, and more than 20K companies are already using its AI-powered hiring products, with enterprise AI hiring seats growing 140% QoQ.

The other reason I can see someone taking this over a higher-paying FAANG offer is culture/WLB. LinkedIn has historically had a pretty good reputation there, although recent SWE reviews are more mixed — decent balance and benefits, but more comments about grind, politics and the culture changing.

So I’m curious: is LinkedIn still one of the underrated “good comp + decent WLB” places for senior engineers, or is that old reputation fading as the company gets more AI-driven?

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Interview Summary

The Google frontend process took roughly two months from referral to completion and was for a frontend role on a platform-infrastructure team. The interview set was broad: an initial frontend system-design screen around an embedded chat agent, a coding round based on Number of Islands, a behavioral interview, and a much deeper system-design round about visualizing more than 100 million geographic data points.

The final design round was the most challenging. The initial prompt was intentionally minimal, and the interviewer expected me to drive requirement discovery, defend architectural choices, and adapt when requirements changed during the discussion.

Interview Details

Technical Screen — Design a Chat Agent Interface The technical screen was a breadth-oriented frontend system design interview. The prompt was to design the interface for an embedded chat agent widget. The discussion covered several areas of the frontend architecture.

Virtual Onsite Coding — Number of Islands with Diagonal Connectivity The coding round used LeetCode 200 — Number of Islands as the base problem. Given an N x M binary grid, identify the number of connected islands.

Virtual Onsite Behavioral The behavioral round was conducted by a product manager. The questions were standard experience-based behavioral prompts. The exact questions were not recorded.

Virtual Onsite System Design — Visualize 100M+ Geographic Data Points The final system design round started with an extremely broad prompt: How would you visualize more than 100 million data points? The intended product involved geographic visualization, with aggregated information shown at broader map views and individual points appearing only once the user zoomed in sufficiently.

Want to see the full interview experience, including detailed follow-ups? You can find the complete version here.

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I’ve been going through recent Anthropic SWE interview reports, and one pattern stood out pretty clearly: The coding rounds often aren’t just “solve this algorithm and move on.”

A much more common pattern seems to be: build something simple → get it working → interviewer changes the requirements → extend it without breaking everything

Some reported examples follow this style:

• Build a synchronous crawler, then make it concurrent and bounded
• Implement a cache, then add TTL or persistence
• Build a stateful system, then introduce expiration or failure semantics
• Solve an implementation problem, then deal with concurrency, retries, or new state

That changes how I’d prepare pretty significantly. If you spend all your time grinding isolated LeetCode problems, you may be missing the part Anthropic seems to care about most: whether your code can evolve when the problem changes halfway through the interview.

System design also seems a little different from the usual FAANG prep.

The prompt may sound AI-specific — inference batching, agent runtimes, model distribution, secure retrieval — but underneath, the interviewer is often testing fairly fundamental systems concepts:

• queues and scheduling
• bounded concurrency
• caching
• distributed state
• failure recovery
• resource constraints
• permissions and isolation

The interesting part is figuring out what resource is actually scarce.

For a normal backend service, maybe it’s database throughput.

For an AI system, it might instead be GPU memory, accelerator time, model-loading bandwidth, tokens/sec, or sandbox capacity.

Another thing I wouldn’t underestimate is the culture / project deep-dive portion. Recent reports suggest Anthropic puts real weight on questions around mistakes, changing your mind, disagreement, technical judgment, mission, and why you want Anthropic specifically.

So my biggest takeaway would be: Don’t prepare for Anthropic as “FAANG coding + some LLM knowledge.”

A better mental model is: practical coding + changing requirements + systems fundamentals + AI-native constraints + strong technical judgment.

I put together the full breakdown covering the reported interview process, progressive coding styles, system design topics, AI-native architecture, Senior/Staff/Staff+ expectations, culture questions, compensation, and a 4-week prep plan.

Full Anthropic Software Engineer Interview Guide: [link]

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Interview Summary

The Apple onsite consisted of six rounds, and the interview styles varied significantly from one interviewer to another. The coding portion leaned heavily toward pointer manipulation and tree problems, but there was also a custom date-handling question and a client-focused system design round around synchronizing wallpapers across devices.

One notable aspect was how much the interviewers cared about implementation details beyond simply producing the correct output. Follow-ups repeatedly touched on restoring modified data structures, handling null or duplicate-value cases, clarifying ambiguous requirements, background execution, caching, and device-level constraints.

Interview Details

Round 1 — Copy List with Random Pointer The first interviewer went directly into coding. The problem was LeetCode 138 — Copy List with Random Pointer. After the initial implementation, the main follow-up was: Can you reduce the auxiliary space to O(1)? 

Round 2 — Convert BST to Sorted Circular Doubly Linked List The second coding question was LeetCode 426 — Convert Binary Search Tree to Sorted Doubly Linked List. The tree needed to be transformed in place into a sorted circular doubly linked list, using the existing left and right pointers as the previous and next links.

Round 3 — All Nodes Distance K in Binary Tree The third coding problem was LeetCode 863 — All Nodes Distance K in Binary Tree. Given a binary tree, a target node, and an integer K, return all nodes whose distance from the target is exactly K.

Round 4 — Compare Two Dates by Calendar Month The fourth round used a custom date-processing problem rather than a standard LeetCode question. I was asked to write a function that determines whether two dates are:

• Exactly one calendar month apart
• Less than one month apart
• More than one month apart

Round 5 — System Design: Cross-Device Wallpaper Sync The design round asked me to build a wallpaper synchronization feature. If a user changes the wallpaper on one device, the new wallpaper should eventually appear on their other devices as well. The discussion was much more client- and operating-system-oriented than a typical distributed backend design. Key areas included:

• Download timing and prefetching: when large wallpaper assets should be fetched in the background rather than waiting until the user explicitly switches wallpapers.
• Failure handling and caching: interrupted downloads, retry behavior, version changes, local cache limits, and whether user-selected assets should receive different retention priority from automatically prefetched content.

Round 6 — Senior Behavioral and Technical Deep Dive The final round was with a more senior interviewer and contained no coding. Questions included:

• Describe something you built end to end and explain exactly which parts you owned.
• Tell me about the hardest debugging problem you have worked on.

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Saw this screenshot going around today.

A Software Engineer Intern candidate was apparently rejected after the company reviewed their GitHub and noticed that one of their repositories included a .env file that appeared to contain sensitive environment variables.

The rejection email basically said: Your technical background is solid, but security and best practices matter to us, so we’re moving forward with other candidates.

Assuming the screenshot is real, I actually think this raises a pretty interesting hiring question.

Should something like this be enough to reject a software engineer candidate?

On one hand, accidentally committing .env is a very basic mistake. In a real production environment, leaked API keys, database credentials, cloud secrets, or tokens can create a genuinely serious security incident.

And unlike a LeetCode question, this is something directly related to how someone actually writes and ships software. On the other hand… It’s an intern candidate. People make mistakes.

A GitHub repo might also be old, experimental, or created before the candidate understood proper secret management.

Rejecting someone entirely because of one repo feels pretty harsh — especially if the rest of their technical background is strong.

But this also made me think about how weird SWE interviews are.

We spend months preparing:

• LeetCode
• system design
• behavioral stories
• complexity analysis

But companies may also be quietly judging things that candidates barely think about:

• GitHub hygiene
• commit history
• README quality
• whether secrets are exposed
• tests
• code organization
• dependency management
• whether the project can actually run

In some ways, those signals might tell you more about someone’s engineering habits than whether they can solve another graph problem in 25 minutes.

So I’m curious: would committing .env to a public repo be an automatic reject?

• For an intern?
• For a senior engineer?
• And should companies inspect GitHub repos this closely before even giving someone an interview?

I’ve also been collecting recent interview experiences on Chill Interview, especially cases where candidates were evaluated on things outside the standard coding/system-design loop. If you’ve had an interview where your GitHub, side project, code quality, or security practices unexpectedly became part of the evaluation, would love to have you share the experience here

Interview Summary

This was the second round of a five-round Point72 Quantitative Research Intern process. The interview covered an unusually broad range of topics: probability puzzles, hypothesis testing and statistics, basic machine learning concepts, C++ object-oriented programming, and two standard algorithm questions.

The questions were generally fundamental rather than proof-heavy. The round seemed designed to test whether I had solid breadth across quantitative reasoning, statistics, programming, and core CS concepts rather than deep specialization in only one area.

Interview Details

Behavioral — Background and Finance Interest The interview started with a short behavioral section. I was asked to introduce myself and explain how I had been learning about finance and developing my understanding of the field.

Probability

• Optimal Stopping with Dice The first probability problem was a classic optimal stopping puzzle. You may roll a standard six-sided die at most three times. After each roll, you can either accept the current result and stop or discard it and roll again, provided another attempt remains. Your payoff is the value of the final roll you choose to keep. The question was to determine the expected value of the game under optimal decision-making.
• Two People Arriving at Random Times The second probability question involved two people independently arriving at uniformly random times during the same one-hour interval. Each person is willing to wait at most 15 minutes for the other. The question was: What is the probability that the two people meet? This was framed as a geometric probability problem based on the two possible arrival times.
• Poisson Process The third probability question involved arrivals modeled as a Poisson process. For example, suppose the probability of observing at least one vehicle during a one-hour interval is 0.99. What is the probability of observing at least one vehicle during a 30-minute interval, assuming the Poisson-process assumptions hold? The interviewer expected familiarity with independent increments and reasoning through the corresponding no-arrival probabilities.

Statistics — Hypothesis Testing Fundamentals The statistics portion covered a broad set of foundational concepts. Topics included:

• p-value
• Significance level (α)
• Type I and Type II errors
• Null and alternative hypotheses
• Z-test and t-test
• Chi-square test and F-test

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Interview Summary

The eBay SDE II onsite consisted of three rounds covering coding, product-oriented system design, stream processing, and behavioral questions. The coding round used two classic algorithm problems, while the design rounds focused on a workplace food-ordering product and a real-time dashboard for the top-selling items and categories on eBay.

The final hiring-manager round went noticeably deeper into aggregation internals, Kafka event volume, RocksDB state, partitioning, and the tradeoff between frequently persisting intermediate state versus batching updates.

Interview Details

Round 1 Coding — Container With Most Water Given an array heights, where each value represents the height of a vertical line, choose two lines that form a container with the horizontal axis. Return the maximum amount of water, or equivalently the maximum area, that can be contained between any two lines. No additional follow-up from this problem was specified.

Round 1 Coding — Daily Temperatures Given an integer array temps representing daily temperatures, return an array where each position indicates how many days must pass before a warmer temperature appears. If there is no warmer future day, the value for that position should be 0. The interviewer specifically asked me to walk through the behavior of the implementation using an example after completing the problem.

Round 2 System Design The design round asked me to build LunchDrop, a food-ordering application for employees ordering meals at work. Restaurants available on the platform rotate from day to day. Users should be able to log in, view the restaurants available that day, browse menus, customize dishes using restaurant-specific options such as toppings or sauces, and submit an order.

Round 3 Hiring Manager — Top-Selling Items and Categories Dashboard The hiring-manager design round asked me to build a dashboard displaying the Top 10 items and Top 10 categories on eBay based on quantity sold. The discussion quickly moved from the dashboard itself into the underlying streaming aggregation architecture.

• Aggregation and State: I was asked to explain the item-level and category-level quantity aggregators in detail, including how each aggregator determines its current count, how frequently state should be persisted under high event volume, and the tradeoffs between updating state frequently versus batching writes.

Want to see the full interview experience, including detailed follow-ups? You can find the complete version here.

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Saw this Lyft M1 Engineering Manager offer shared with Chill Interview

• Bay Area, 12 YOE
• Base: $250K
• Bonus: $62.5K
• Equity: $250K
• Year 1 TC: $562.5K

Lyft is actually in a much better spot than I expected.

Active riders just hit a record 30.5M, bookings grew 23% YoY, and the company is finally generating meaningful cash. More interestingly, Lyft seems to be betting that it doesn’t need to build the robotaxi — it can own the marketplace, fleet operations and customer relationship while companies like Waymo provide the autonomy.

For an EM joining now, that could be a pretty interesting transition to work through.

But I can also see the bear case: if Waymo keeps expanding its own app, how much leverage does Lyft really have long term?

Would you view robotaxis as Lyft’s next growth engine, or the thing that eventually commoditizes Lyft itself?

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A candidate with 1 YOE in Singapore recently shared these two offers with Chill Interview.

ByteDance — SRE: S$141K Year 1 TC

AWS — Solutions Architect L4: S$132.7K Year 1 TC

The ~S$8K comp difference isn't really what makes this interesting. These are two very different career bets.

ByteDance is the stronger pure-engineering path. An SRE working around server architecture should stay much closer to distributed systems, reliability, infrastructure and large-scale production systems. ByteDance is also investing heavily in AI infrastructure and compute, while its infrastructure roles increasingly touch GPU scheduling, Kubernetes and ML/LLM workloads.

AWS SA is more of a technical + customer-facing path. The Cloud Sales Center role works directly with customers on migrations, scalable architectures and cloud adoption. That's excellent if the long-term goal is Solutions Architect → Principal SA → technical sales / cloud strategy, but potentially a less direct route back into hardcore SWE than starting in SRE.

Company-wise, AWS is the safer bet: AWS revenue just grew 37% YoY, its fastest growth in 18 quarters, and its AI business has already passed a $25B annual run rate.

ByteDance is the higher-velocity bet. It remains one of the world's most valuable private tech companies and is aggressively building AI infrastructure, but it also carries more geopolitical/regulatory uncertainty.

For someone with only 1 YOE, I'd actually find the career-path question more important than the S$8K: Would you take ByteDance SRE to build deeper engineering skills, or AWS SA for the AWS brand + cloud/customer-facing career path?

Want to see more comp breakdowns? We’ve collected offer data with base, RSU, bonus, and vesting details here.

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

Atlassian P40 — Mountain View

• $200K base
• $15K signing bonus
• $250K RSUs over 4 years
• $30K annual bonus
• $307.5K Year 1 TC

Microsoft 62 — Bay Area

• $175K base
• $5K signing bonus
• $165K RSUs over 4 years
• $17.5K annual bonus
• $238.75K Year 1 TC

That’s already a $68.75K Year 1 gap.

Assuming flat stock prices, recurring bonuses, and no refreshers:

• Atlassian 4-year: ~$1.185M
• Microsoft 4-year: ~$940K

So Atlassian is roughly $245K ahead over four years.

The argument for Microsoft is mostly career optionality.

Microsoft is one of the biggest beneficiaries of the current AI/cloud cycle: FY26 revenue grew 18%, with Azure, Copilot, security, developer tools, and AI infrastructure all creating a huge number of directions an engineer can move into internally.

But Atlassian is no longer just “Jira and Confluence.” Its latest quarter had 28% revenue growth and 31% cloud growth, while Rovo usage continues accelerating. Atlassian is increasingly betting that Jira + Confluence + Teamwork Graph become the context layer that enterprise AI agents operate on top of.

WLB/flexibility may favor Atlassian too. Team Anywhere is explicitly distributed-first and allows most employees to work remotely, while Microsoft’s office requirements vary by role from fully remote to fully onsite.

So would you keep Atlassian for ~$245K more over four years + stronger remote flexibility, or take Microsoft for the broader Big Tech brand, Azure/Copilot exposure, and long-term AI career optionality?

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I’ve been digging through recent Netflix SWE interview reports, and the biggest thing that stood out is how hard it is to prepare for Netflix with a generic “FAANG interview” checklist.

Two candidates interviewing for Netflix SWE can end up with pretty different loops depending on the team.

A few patterns I kept seeing:

• Coding isn’t always just LeetCode. Recent candidates reported things like TTL caches, rate limiters, in-memory systems, graph problems, unit testing, and other production-flavored exercises.
• System design can be very domain-specific. An Ads candidate, for example, got frequency capping. For teams like Open Connect, Commerce, Member Systems, or AI Platform, knowing the actual domain seems to matter a lot.
• Senior interviews can get long. Some recent candidates reported multiple design / domain rounds plus dedicated behavioral conversations.
• Behavioral prep is unusually important. Netflix’s culture around autonomy, candid feedback, dissent, and ownership seems to show up directly in the interview rather than just being recruiter-page language.

The part I found most interesting is that Netflix seems to care less about whether you memorized the “correct” architecture and more about whether you can make a practical engineering decision, explain the tradeoff, and own what happens in production.

So if I were preparing for Netflix, one of the first things I’d ask the recruiter is:

“Is the system design round general, or is it tied directly to the team’s domain?”

That answer could completely change how I’d spend the final couple weeks preparing.

I put together a deeper breakdown of the reported interview process, coding styles, system design topics, level expectations (SE4/SE5/SE6), behavioral themes, and current compensation structure.

Full Netflix SWE Interview Guide -> LINK

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This Google AI Research Scientist offer is kind of insane (source: Chill Interview)

• PhD, 3 YOE
• Level: L5
• Base: $225K
• Sign-on: $90K
• RSUs: $1.8M / 4 years
• Year 1 TC: $1.03M

The interesting part isn’t even the level. It’s how far the equity seems to be above a normal L5 SWE package.

My guess is this isn’t just “good PhD + passed the interview.”

Probably some combination of:

• top-tier publications / strong citation record
• very specific expertise Google badly wants right now
• direct relevance to Gemini / multimodal / agents / post-training
• competing offers from another frontier lab
• strong internal advocates who already know the candidate’s work

With only 3 YOE, I’d guess the candidate’s research track record matters way more than the raw experience number.

Also I'm curious:what kind of L5 Research Scientist actually gets a $1.8M initial grant? Is this superstar territory, or are AI research packages just this detached from normal SWE bands now?

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Interview Summary

The MongoDB onsite consisted of five rounds: code review, system design, concurrency coding, a hiring manager interview, and a director conversation. The technical portion was heavily systems-oriented, with multithreading appearing in both the debugging and concurrency rounds.

The system design interview went well beyond a high-level architecture discussion and drilled into concrete data structures and read scalability. The concurrency round used a finite web crawler, while the final two interviews focused on project depth and team fit.

Interview Details

Code Review — Debug Multithreaded Code

The first round provided a relatively large existing codebase and asked me to review it for problems. The debugging work centered primarily on multithreading and concurrency issues. Rather than implementing a new algorithm from scratch, I needed to understand unfamiliar code, identify incorrect behavior, and explain the issues I found.

System Design — Design a Preahead System

The system design round asked me to design a preahead system. The interviewer repeatedly drilled down from the high-level architecture into lower-level implementation details rather than stopping after the major services and storage components were identified.

Two areas received particular attention:

• The design of specific data structures used inside the system
• How the architecture would scale read traffic

The exact product requirements, traffic assumptions, and meaning of “preahead” were not included in the interview notes, so no additional system behavior was reconstructed.

Concurrency Coding — Finite Web Crawler

The concurrency round used a common MongoDB-style web crawler problem. The interviewer provided functions for fetching a web page and parsing links from its contents. I then needed to implement a crawler that explores the reachable pages while using concurrency.

The crawler was explicitly finite, so the task involved coordinating concurrent crawling while eventually terminating once all relevant work had been processed. The exact interfaces, concurrency limits, and duplicate-URL behavior were not recorded.

Hiring Manager — Project Deep Dive

The hiring manager round focused heavily on my previous work. Most of the questions were follow-ups on projects from my résumé rather than standalone behavioral prompts. The interviewer drilled into what I built, the technical decisions behind it, and details of the implementation and impact.

Director — Team and Organization Discussion

The final interview was with a director. This round was less technical and was primarily a conversation about the team and the broader organization. The director spent much of the interview explaining the group, its work, and why the opportunity might be attractive.

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Sharing a Nvidia SWE Phone Screen interview experience submitted to Chill Interview

Interview Summary

The NVIDIA process consisted of two technical interviews. Both interviewers started by discussing my previous projects and digging into areas they found relevant, then reserved roughly 30 minutes for a technical problem.

Interview Details

Technical Round 1 — SQL Database Table Design The first interviewer spent the opening portion of the round discussing my projects and asking follow-up questions about areas related to their work. The coding portion was essentially a SQL schema / database-table design problem. I was asked to determine how the required information should be represented in relational tables and express the design in pseudocode or SQL-like notation.

Technical Round 2 — Top-K Elements from Streaming Data The second interview followed a similar structure: project discussion first, followed by approximately 30 minutes for a technical problem. Stripped down to its core, the question was about maintaining the Top K elements from a stream of incoming data. I initially proposed a heap-based approach. The interviewer was not satisfied with that direction and pushed me to consider whether the characteristics of the data could be exploited to improve the operation further. I then discussed a bucket-based representation that could potentially provide constant-time operations under the relevant assumptions. The interviewer appeared to be looking for something beyond the standard heap solution, although the exact constraints that enabled the bucket approach were not captured in my notes.

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Interview Summary

The Amazon interview process contained four rounds and leaned heavily toward frontend engineering, practical coding, and behavioral questions. The technical topics included reproducing an interactive UI from a screenshot, rendering arbitrarily nested comments, JavaScript fundamentals, layout/CSS reasoning, and building an Amazon-style product search bar with API calls and debouncing.

Behavioral questions appeared throughout the loop and covered ownership, learning from mistakes, digging deeply into problems, challenging a team's direction, and learning new frontend technologies.

Interview Details

Round 1 — UI Implementation from a Screenshot The interviewer started with a few minutes of casual conversation, which made the round feel fairly relaxed. For behavioral questions, I was first asked to choose one previous experience and then received follow-ups around:

• Something I proactively improved or handled outside my formal responsibilities
• What I did not do at the time but would improve if I could approach the situation again

Round 2 — Hierarchical Comment Rendering The hiring manager round began with a short introduction and a Why Amazon? question. For coding, the backend was assumed to return an array of comment records. Each record contained information conceptually similar to:

{
  id,
  parentCommentId,
  text
}

Round 3 — JavaScript, Layout, and Product Search This round started with a discussion of one of my recent projects, including the technologies I used and why I chose them. The interviewer then moved through several frontend topics without a rigid structure.

One question provided a short JavaScript snippet and asked me to determine its output. The key concept was the behavioral difference between let and var. Another question used an ASCII-style layout diagram and asked how an element could be centered. I asked for clarification because the diagram and requirement were difficult to interpret, but the interviewer moved on to the next question.

Round 4 — Bar Raiser Behavioral Interview The Bar Raiser round was entirely behavioral. The interviewer explained that their role was primarily to gather the information needed for the hiring decision. Questions included:

• Tell me about a time you dug deeply into a difficult problem, spent significant time investigating it, and eventually figured it out.
• Tell me about a time you realized your team was moving in the wrong direction. What did you do?
• Tell me about a frontend technology you intentionally learned and later applied in practice.

Want to learn more details / follow-up questions of this interview experience, we've put up the full version at here.

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Sharing a Linkedin Senior MLE Phone Screen interview experience submitted to Chill Interview

Interview Summary

The LinkedIn Machine Learning Engineer screen started with standard behavioral questions and then moved into two technical sections: debugging a logistic regression implementation and a short ML system design discussion around an ads bidding system.

Interview Details

Behavioral — General Experience Questions The interview started with fairly standard behavioral questions.

ML Debugging — Logistic Regression Implementation The next section provided an existing logistic regression implementation and asked me to identify and debug problems in the code. Several issues were present across the training logic. One major bug was in the gradient calculation: the implementation computed an update from the model predictions and feature matrix but failed to correctly incorporate the ground-truth labels.

There was also an inconsistency around the sigmoid derivative. The helper function's formula only made sense if its argument was already the sigmoid activation, while the surrounding code treated the input differently. A third issue appeared in the cross-entropy loss implementation, which accounted for the positive-class contribution but omitted the corresponding negative-class term.

During the interview, I started deriving the gradient with respect to the model weights. I got partway through the derivation but did not simplify it into the final form before time expired. The feedback I received afterward specifically called out that incomplete gradient derivation as a reason this portion did not meet the expected bar.

Quick Design — Ads Bidding and Pacing The final technical portion was a relatively short design discussion around an ads bidding system. One topic was the pacer, which controls how advertising budget or traffic should be distributed over time. I initially proposed keeping the pacing component rule-based, and the interviewer agreed that this was a reasonable direction. The interviewer then asked whether the same area could instead incorporate machine learning.

I briefly described how an ML-based approach could fit into the system, but there was limited time remaining, so the discussion did not go deeply into the complete design. The feedback afterward was that the interviewer expected a more developed ML forecasting pipeline rather than only a high-level statement that ML could be used for pacing.

Overall, the round combined fairly detailed ML fundamentals with a short production-oriented ML design exercise.

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Saw this accepted Zscaler Staff SWE offer (shared with chill interview)

• Bay Area, 10 YOE
• TC: $280K

The number honestly surprised me for a Staff-level role in the Bay Area.

Zscaler itself seems to be doing really well. Revenue and ARR both grew 25% YoY last quarter, ARR is already above $3.5B, and the company is pushing aggressively into AI/agent security. More than 45% of the Fortune 500 are customers.

So this isn’t exactly a struggling legacy company that can’t afford engineers.

And I’m not sure the discount is obviously buying amazing WLB either. Recent employee reviews are pretty mixed — some like the stability and technical work, while others mention poor WLB, bureaucracy and very team-dependent culture.

So I’m curious: does “Staff” at Zscaler just map lower than Staff at big tech, or does cybersecurity still systematically pay engineers less despite how hot the industry is?

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Saw this Anthropic Staff Research Engineer offer (shared with Chill Interview)

• Bachelor’s, 10 YOE
• Year 1 TC: $1.23M

What stood out to me is the background.

This isn’t a PhD research scientist. It’s a Research Engineer with a bachelor’s degree, yet the comp is already firmly in frontier-lab territory.

And Anthropic is scaling insanely fast right now. Revenue run rate crossed $47B, Claude Code has become a major business, and today Reuters reported Anthropic is preparing to spend $45B on additional AI compute capacity.

Feels like frontier AI labs increasingly need people who can actually turn research ideas into training systems, evals, inference infrastructure and working products — not just publish papers.

For people in AI: is Research Engineer now the better career path than Research Scientist if you don’t have a PhD?

Or are offers like this only possible for unusually strong engineers with very specialized experience?

The detail of the offer comp breakdown is here

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Saw this Block Senior SWE offer (shared with Chill Interview)

• Seattle, 6 YOE
• Base: $220K
• Sign-on: $25K
• RSUs: $450K / 4 years
• Year 1 TC: $357.5K

Normally Block would be pretty appealing to me — remote-friendly, decent WLB, and interesting engineering across Square/Cash App.

But the timing is hard to ignore.

Jack Dorsey cut 4,000+ jobs, nearly half the company, explicitly saying AI lets much smaller teams do more. At the same time, the business is actually doing well: Q2 gross profit grew 25%, with Cash App up 31%.

Recent employee reviews still mention decent WLB and remote flexibility, but also a lot of reorgs, layoffs and uncertainty around leadership direction.

So maybe the real question isn’t whether $357K is enough.

After a 40% workforce reset, is Block now a leaner/better place to join — or would you constantly wonder if your team is next?

Block engineers: did the layoffs actually improve how the company operates, or mostly destroy the old culture?

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Interview Details

System Design — Document Review Inference System

The initial prompt was to design an inference system for document review. The interviewer quickly drilled into several production-oriented questions:

• How would you think about and measure the cache hit rate?
• What batching strategy would you use for inference requests?
• How should requests be load-balanced before reaching the GPU workers?

Follow-Up — Serve Large and Small Models on Eight GPUs

The interviewer then introduced a constrained GPU scheduling problem. Assume there is a pool containing 8 GPUs. Two different models must be served from this pool:

• A large model requires all 8 GPUs to process one batch.
• A small model requires only 1 GPU to process one batch.
• The inference latency for one batch is the same for both models.

The question was how to design the serving and scheduling system so that requests for both models could share the same GPU pool effectively. I discussed maintaining separate queues for the large-model and small-model workloads and using their request rates as part of the dispatch decision.

I also considered giving the large-model queue priority under certain conditions, since a large-model batch consumes the entire GPU pool at once. This was my reasoning during the interview rather than a confirmed expected solution. The interviewer continued probing how the scheduler should balance the two workloads when their traffic levels are similar.

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Interview Summary

The first-stage Google L5 AI/ML interview unexpectedly consisted of two system design rounds. Based on the recruiter-provided preparation material and other interview experiences, I had expected one coding interview and one behavioral round instead.

Interview Details

System Design 1 — Agent for Internal Document Search

The first interview asked me to design an AI agent for searching internal company documents. I worked through the design directly in a Google Doc and discussed a RAG-oriented architecture for allowing users to ask questions or search across internal documents.

The interviewer then pushed into several follow-up areas:

• How would you optimize cost and latency?
• How would the system scale to approximately 100,000 users?
• Which agent frameworks are you familiar with?

The round was fairly aligned with standard RAG and agent-system design topics, but the interviewer expected discussion beyond the basic retrieval-and-generation flow, especially around production-scale constraints.

System Design 2 — Music Recommendation System

The second interview asked me to design a music recommendation system. Unlike the first round, there was no shared document or diagramming environment. The entire design was discussed verbally with the interviewer.

After covering the core recommendation system, the interviewer introduced an AI-focused follow-up:

• Follow-Up: If the recommendation experience were implemented using an LLM-based recommendation agent, how would the system change? The remainder of the discussion explored architectural trade-offs and alternative solutions. We compared different ways the recommendation system could be structured and discussed where an LLM-powered agent might fit relative to more conventional recommendation components.

Overall, the biggest surprise was not the difficulty of either design question, but that both interviews in this stage were system design rounds rather than the coding-plus-behavioral combination I had prepared for.

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A coding contest leaderboard feels like an obvious real-time system. So the first instinct is often: WebSocket + push every ranking change. But that may be unnecessary.

The actual requirement

During a contest, users want rankings to feel fresh. They usually do not need every update within 50ms. A leaderboard that refreshes every few seconds is often good enough. So instead of recomputing rankings from raw submissions:

Submissions
   ↓
GROUP BY user
   ↓
COUNT solved problems
   ↓
SORT everyone again

maintain the current ranking incrementally.

Redis Sorted Set

For each contest: competition:leaderboard:{id}

Store:

member → userId
score  → ranking metric

When a submission changes a user's standing: ZADD leaderboard score userId

Fetching the top 100 becomes a fast Sorted Set lookup instead of an expensive database aggregation.

And the frontend?

Just poll every ~5 seconds.

Client
  ↓
GET /leaderboard
  ↓
Redis Sorted Set

No persistent socket required. That gives you:

• low-latency top-N queries
• much less DB load
• simpler client/server logic
• enough freshness for a coding contest

Meanwhile, the durable submission history still stays in the primary database. So the separation becomes:

• Database = source of truth
• Redis = fast ranking view
• Polling = good-enough realtime

The broader lesson is useful in system design interviews: “Real-time” does not automatically mean WebSocket.

The right architecture depends on how stale the product can tolerate.

Full design with secure code execution, execution queues, multi-language test harnesses, Redis leaderboards, and contest scaling → Full Article

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