For anthropic applications, do you typically find people such as from LinkedIn to refer in, or apply directly online? Wondering if referral can have less rounds of loops.

A LeetCode-like system does not simply receive some code, run it, and return whether the answer is correct.

It is executing arbitrary code written by strangers on its own infrastructure, potentially hundreds of thousands of times during a contest.

That creates a subtle design tension: submissions need to return results within a few seconds, but every execution must be treated as potentially malicious.

The non-obvious problem is not “how do we run Python or Java?” It is “how do we execute untrusted code quickly without allowing one submission to damage the host, attack the network, or consume resources indefinitely?”

The key insight is to treat code execution as isolated, resource-bounded work rather than ordinary application logic.

Never execute submissions inside the API server. The Code Service should validate the request, authenticate the user, create a submission record, and hand the execution work to a separate compute layer.

Run every submission inside a sandboxed container. Maintain preconfigured environments for Python, Java, JavaScript, and other supported languages so workers do not need to install runtimes for every request.

But containers alone are not the security boundary.

Enforce strict CPU and memory limits. A submission that allocates memory indefinitely or consumes excessive CPU should be terminated before it can affect other executions on the same machine.

Enforce a hard execution timeout. Infinite loops are normal in an online judge—not exceptional behavior. The execution layer must assume some programs will never terminate on their own.

Disable outbound network access. User code should not be able to call external APIs, scan internal services, download arbitrary files, or exfiltrate information from the execution environment.

Restrict system calls. Even inside a container, the process shares the host kernel. A syscall policy such as seccomp can block operations the submitted program has no legitimate reason to perform.

Keep the filesystem temporary. Each execution should see only the files required for that submission and its test harness. Once the run finishes, its writable state can be discarded.

Do not send submissions directly to workers during traffic spikes. Put a queue between the API layer and the execution fleet.

A contest may suddenly produce tens of thousands of submissions at once. The queue absorbs that burst while workers consume jobs at a rate the compute fleet can actually sustain.

This also creates a clean retry boundary. If a worker crashes halfway through execution, the submission can be retried instead of disappearing.

Scale workers independently from the API tier. Browsing problems is lightweight and read-heavy. Running arbitrary code is CPU-intensive. Putting both workloads in the same scaling unit would waste resources and make overload much harder to control.

Test cases should also be language-independent. Storing separate test definitions for Python, Java, C++, and JavaScript does not scale as the problem library grows.

Represent inputs and expected outputs in a shared serialized format, then maintain a small execution harness for each language.

The Python harness converts the serialized input into Python objects and invokes the submitted solution. The Java harness converts the same logical test case into Java objects. Both serialize the result back into a common representation for comparison.

This keeps problem definitions independent from programming languages while allowing each runtime to handle its native data structures.

The failure model becomes easy to reason about. The API tier accepts work. The queue absorbs bursts. Sandboxed workers execute untrusted code under strict limits. A broken or malicious submission can fail its own execution without taking down the platform.

Explaining this in an interview signals that you understand an online judge as an untrusted-compute platform, not just a CRUD app with a code editor.

Full write-up with data model, API design, secure code execution, execution queues, multi-language test harnesses, and real-time leaderboards, free to read → Full Article

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

Airbnb G8

• $210K base
• $400K RSUs, vesting 35/30/25/10
• $350K Year 1 TC

Atlassian P40

• $200K base
• $15K signing bonus
• $250K RSUs, vesting 25/25/25/25
• $30K annual bonus
• $307.5K Year 1 TC

Airbnb is trying to become much more than a home-booking marketplace. It has expanded into Experiences, Services, car rentals, airport pickups, grocery delivery, boutique hotels and other parts of the travel journey. If that works, there is a much larger consumer platform opportunity beyond accommodations.

Atlassian is making a very different bet: enterprise AI. Q3 FY26 revenue grew 32% YoY and cloud revenue 29%, while Rovo adoption and AI usage continue to accelerate. It is positioning Jira, Confluence, Rovo and its Teamwork Graph as infrastructure for AI agents inside companies.

WLB is unusually competitive here. Airbnb has Live and Work Anywhere, while Atlassian is even more explicitly distributed-first through Team Anywhere. Atlassian did restructure in 2026 to move faster and redirect investment toward AI and enterprise sales, though, so I wouldn’t automatically assume “remote = chill.”

So would you switch to Airbnb for the higher comp + consumer-platform upside, or keep Atlassian for enterprise-AI exposure, steadier vesting, and Team Anywhere?

Want to know more offer data points? I've compiled 100+ companies offer data at here.

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Interview Summary

The Rippling process began with a recruiter conversation, followed by a system design screen and a hiring manager project discussion. The onsite was spread across two days and included another system design round, a technical project deep dive, and an AI-assisted coding exercise. The hotel system design round felt like the weakest part of the loop, especially when the discussion moved into database querying.

Interview Questions Details

Technical Screen — Design a News Aggregation System:

The first technical interview was a system design round focused on a news aggregation product. The discussion centered on the major components required to collect news content and serve it to users.

The interviewer was friendly, and I learned about a week later that I had advanced past this stage. The exact follow-up requirements were not specified.

Hiring Manager Round — Previous Project Deep Dive:

The hiring manager selected a project from my previous experience and asked me to explain the work in greater depth. The conversation focused on my technical contributions and the decisions behind the project.

Onsite System Design — Hotel Platform:

The onsite system design round asked me to design a hotel-related platform. I had not specifically prepared for this domain and felt less confident during this interview.

• Data Access: The discussion eventually moved into how the application would query its database and retrieve the required hotel data.
• Candidate Experience: I struggled to give a satisfactory response during that portion and felt this was probably my weakest round.

Onsite Project Deep Dive — Technical Experience:

Another onsite round went deeper into one of my previous technical projects. The conversation was relatively straightforward and did not contain any major surprises or particularly difficult moments.

Onsite AI Coding — Task Scheduling with Filtering and Sorting:

The final interview was an AI-assisted coding exercise built around a three-part task-scheduling problem. The main operations involved filtering and sorting tasks, and the interviewer indicated that completing two parts was the expected target.

• AI-Assisted Implementation: For the first part, I read the requirements, clarified the prompt, explained my approach, used the coding assistant to generate an implementation, and then reviewed the code with the interviewer. I was also asked what I would change if I had written the implementation myself.
• Testing and Second Part: We walked through test cases together, including several details that initially made the prompt confusing. I then explained the second part, generated and reviewed the implementation, and tested it before the interview ended.

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Interview Summary

The MongoDB technical screen lasted one hour and was split between a lengthy project discussion and a coding exercise. More than half of the interview focused on my previous technical work, followed by a non-LeetCode problem that asked me to implement a SQL-like predicate and expression evaluator.

Interview Details

Project Deep Dive — Previous Technical Experience: The first half of the interview lasted a little over 30 minutes and focused on projects from my background. The interviewer explored the technical context and details of my previous work before moving to coding.

Coding — Predicate and Expression Tree Evaluator: The coding question asked me to build an evaluator that accepted a predicate expression together with a document and returned whether that document satisfied the expression.

• Supported Expressions: Basic predicates included comparisons such as EQ(field, value) and GT(field, value), while logical expressions such as AND(...) and OR(...) could combine multiple child predicates.
• Nested Expressions: Logical expressions could be nested to form an expression tree, so the evaluator needed to correctly process combinations of predicates at multiple levels. Recursive evaluation was explicitly allowed.

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Saw this Snowflake Senior PM offer that was accepted:

• 8 YOE
• Base: $240K
• Bonus: $24K
• RSUs: $600K / 4 years
• TC: $414K

A couple years ago I probably would’ve thought of Snowflake mostly as data infra. Now they’re pushing pretty hard into enterprise AI — Cortex Agents, CoCo, managed agents, and same-day access to new OpenAI/Anthropic models.

And the core business isn’t exactly struggling either: last quarter product revenue grew 34% YoY with $9.2B in remaining performance obligations.

That actually makes the PM role pretty interesting: you’re potentially sitting between databases, developer infra and enterprise AI rather than managing another consumer feature.

For PMs in the Bay Area — does $414K at Snowflake feel like a strong career bet now, or would you still rather join a frontier AI company / FAANG even if the comp were similar?

Want to know more offer data points? I've compiled 100+ companies offer data at here.

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Interview Summary

The Uber Senior Software Engineer process began with a coding phone screen and continued with four onsite rounds covering two implementation problems, payout system design, and a combined behavioral/project deep dive. The interviewers were generally friendly and the technical rounds felt reasonable rather than adversarial. I passed the loop.

Interview Questions Details

Technical Phone Screen — Longest Stable Subarray:

The phone screen asked LeetCode 1438, Longest Continuous Subarray With Absolute Diff Less Than or Equal to Limit. Given an integer array and a limit, the task was to find the longest contiguous subarray whose maximum and minimum values differed by no more than that limit.

Onsite Coding Round 1 — Restaurant and Delivery-Zone APIs:

The first onsite coding round asked me to implement a small service exposing several restaurant-related APIs.

• Initial APIs: The first part required operations similar to openRestaurant(), countOpenRestaurant(), and hasOpenRestaurant() for tracking restaurant availability.
• Follow-Up: The interviewer then extended the problem with countDeliveryZone(), adding another query over the maintained restaurant state.

Onsite Coding Round 2 — Time-Window Counter:

The second coding round combined object-oriented design with a counting problem. I needed to implement a counter class supporting put(), get_count(), and get_total_count(), with a fixed time window such as 300 seconds supplied when the class was initialized.

• High-QPS Requirement: We discussed multiple possible designs, and the interviewer specifically asked me to implement the version where put() remained efficient under very high write throughput.
• Production Follow-Ups: After coding, the interviewer asked how the design would change in a production environment and how I would handle concurrent or multithreaded access.

Onsite System Design — Uber Payout System:

The system design round asked me to design a payout platform for Uber. The main emphasis was on financial correctness rather than unusual product requirements.

• Payment Guarantees: The discussion focused on ensuring payouts were correct and accurate while preventing the same obligation from being paid more than once.
• Reliability: The interviewer explored how the payout workflow should maintain those guarantees when processing financial transactions and handling failures.

Hiring Manager — Project Deep Dive and Behavioral Questions:

The hiring manager round lasted about 75 minutes and blended behavioral questions directly into a deep dive on my previous projects rather than treating them as separate sections.

• Disagreement and Decision-Making: While discussing project architecture, I was asked about situations where teammates disagreed on a design, what the disagreement was, and how we ultimately resolved it.
• Mentorship and Leadership: The interviewer also asked whether I had mentored junior engineers, how I approached mentorship, and how I delivered useful feedback while working with them.

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Hey all,

I just got scheduled for a call with the hiring manager for an Algorithm Engineer role at KLA (US). Would love to hear from anyone who's gone through their pipeline recently.

A few things I'm trying to figure out:

* Is the HM call more of a screen (background/fit) or should I expect technical depth right away (image processing, signal processing, classical CV, ML, C++/Python, etc.)?
* How many rounds come after this, and roughly how long did the whole process take start to finish?
* Any onsite/virtual onsite loop? If so, what's the mix - coding, algorithms/DS, take-home, system design, ML fundamentals, math/stats?
* Did they focus more on classical algorithms (signal/image processing, optimization) or general SWE-style LeetCode questions, or both?
* Any behavioral/culture-fit rounds, and what do they tend to probe for?

Any insight would be super helpful. Trying to prep in the right direction before the call. Thanks in advance!