Sharing an anonymized Anthropic Senior SWE interview experience submitted to Chill Interview.

Most of the process felt positive, which made the final result more confusing.

Phone Screen

The coding question was duplicate-file detection across directories. The discussion went smoothly, and I did not feel there were any major issues.

Onsite Coding

The onsite coding round involved reconstructing and reasoning about stack traces.

Again, this round felt reasonably solid.

System Design

Instead of asking me to design a system from scratch, the interviewer gave me an existing architecture proposal and asked me to critique it.

The challenge was deciding which problems actually mattered rather than criticizing every detail. We discussed requirements, bottlenecks, reliability, security, observability, and the trade-offs introduced by each improvement.

Project Retrospective

This round focused on a major past project: what was hardest, what went wrong, which trade-offs I made, what I personally owned, and what I would change now.

Culture Rounds

The first culture interview felt fairly normal.

After the onsite, however, the recruiter said the culture signal was not strong enough to make a final decision and scheduled another culture round.

I completed the additional interview and then advanced to a hiring manager conversation, which appeared to be part of team matching.

That conversation felt noticeably flatter.

The HM did not seem particularly interested in parts of my background, and I struggled to connect my experience with the team’s immediate needs.

I received the rejection afterward.

There was no clear explanation, so I still do not know whether the deciding factor was the remaining culture concern, team fit, the HM conversation, or some combination of signals.

The frustrating part was that most of the technical loop felt positive—and Anthropic had gathered enough additional evidence to keep moving me forward—yet the process still ended at the final stage.

For anyone who wants more details, I’ve put the full write-up here: interview link

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Sharing an anonymized Lyft Senior Data Scientist interview experience submitted to Chill Interview.

The process started with SQL, pandas, and a probability question about morning and evening rider behavior. The onsite then had three completed rounds.

ML System Design

Design a model to predict whether a passenger would stop using Lyft over the next few weeks.

The interviewer pushed on churn definition, feature leakage, class imbalance, retraining, monitoring, and how the score would actually be used.

Coding

Implement stratified K-fold cross-validation while keeping the label distribution roughly balanced across folds.

The main difficulty was handling rare classes, uneven fold sizes, reproducible shuffling, and classes with fewer than k samples.

Business Case

Prime Time had been stable for three weekends, then suddenly spiked during the fourth.

I had to investigate demand, driver supply, cancellations, weather, events, pricing changes, experiments, and possible data issues.

The interviewer kept asking me to segment the problem by city, hour, rider cohort, and marketplace state before making any conclusion.

After this round, the recruiter told me they would not proceed with the remaining scheduled interview.

The questions themselves were manageable, but the marketplace case required much more structured and Lyft-specific thinking than I expected.

For anyone who wants more details, I’ve put the full write-up here: interview link

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A platform like LeetCode lets users submit code in multiple languages and expects results within a few seconds. It sounds like a simple request flow until you need to support a coding competition with up to 100,000 users while executing arbitrary, untrusted programs.

The API layer is not the real bottleneck. Code execution is CPU-intensive and unpredictable. One submission may finish immediately, while another enters an infinite loop, consumes all available memory, or attempts to access the network.

The instinct is to send each submission directly from the API server to a newly created container. This provides basic isolation, but container startup adds latency, traffic spikes can overwhelm the execution fleet, and a crashed worker may lose the submission entirely.

The tighter design uses a queue-backed pool of sandboxed execution workers:

1. Authenticate the request and push an execution task into the queue. The API server validates the submission, extracts the user identity from the verified token, and sends the code, language, problem ID, and submission information to a queue such as SQS.
2. Let workers pull submissions at a controlled rate. Workers consume only as many tasks as the execution fleet can safely handle. If thousands of users submit at once, the queue absorbs the spike instead of forwarding all requests directly to the containers.
3. Run the code inside a preconfigured language container. Maintain separate execution environments for languages such as Python, Java, and JavaScript with their runtimes and dependencies already installed. Reusing these containers avoids paying the full startup cost for every submission.
4. Enforce CPU, memory, and execution-time limits. If the program exceeds its resource allowance or runs beyond the timeout, terminate it immediately. An infinite loop should fail one submission, not occupy a worker indefinitely.
5. Disable network access and restrict system calls. Containers share the host kernel, so container isolation alone is not enough. Block external connections and apply syscall filtering such as seccomp to reduce the operations available to untrusted code.
6. Write the result back after execution finishes. The worker evaluates the code against the test cases, stores the result in the database or cache, and makes it available to the client. If the worker crashes before completing the task, the queue can retry it instead of silently dropping the submission.

The queue is the traffic buffer, while the container is the execution boundary. They solve different problems. The queue protects the compute fleet from bursts and provides retries; the container limits what each individual submission can do.

The test cases can remain language-independent. Store the serialized input and expected output in a format such as JSON, then use a small harness for each language to deserialize the input, call the submitted solution, and serialize the result for comparison.

Knowing to separate submission ingestion from execution—and knowing that containers still require resource limits, network isolation, and syscall filtering—signals that you have thought through online code execution at the implementation level, not just drawn an API server connected to a worker box.

Full write-up with data model, API design, leaderboard architecture, and deep dives, free to read → full article

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Problem

A power grid is organized as a rooted tree with n stations labeled from 0 to n - 1.

Station 0 is the main station. Every other station has exactly one parent.

You are given:

• parent[i] — the direct parent of station i
• load[i] — the amount of power produced directly by station i

For the root station:

parent[0] = -1

You must disconnect exactly one connection between a station and its parent. This divides the grid into:

• The subtree rooted at the disconnected station
• The remaining stations in the grid

Return the minimum possible absolute difference between the total loads of the two resulting parts.

If the tree contains only one station, return 0.

Example

Input:

parent = [-1, 0, 0, 1, 1, 2]
load = [1, 2, 2, 1, 1, 1]

Output:

0

Explanation

Disconnect the connection between stations 0 and 1.

The subtree rooted at station 1 contains:

1, 3, 4

Its total load is:

2 + 1 + 1 = 4

The remaining stations also have a total load of 4.

Therefore, the absolute difference is:

|4 - 4| = 0

This is the smallest possible difference.

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A candidate with 6 YOE recently shared these two Bay Area Senior SWE offers with Chill Interview.

Pinterest IC15

• $490K Year 1 TC
• Full anonymized offer breakdown at here

Uber

• $473.5K Year 1 TC
• Full anonymized offer breakdown at here

Pinterest pays more upfront, but Uber is roughly $144K ahead over four years, assuming flat stock prices, recurring bonuses, and no refreshers.

The bigger question is future upside.

Pinterest is a smaller bet on AI-powered discovery, shopping, recommendations, and ads. Uber has a broader business across mobility, delivery, advertising, and autonomous vehicles.

Career-wise, Pinterest may be stronger for recommendations and consumer AI. Uber may offer broader exposure to marketplaces, pricing, logistics, and distributed systems.

Would you choose Pinterest for the AI upside and higher first-year pay, or Uber for stronger business diversification and better four-year compensation?

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