The same comment-tree filtering question came up in an Android phone screen and a general SWE phone screen, each time with a points or score threshold added on top.

This article has two role sections, newest report first:

• Android engineer: one phone screen (report 01).
• Software engineer (general): one virtual onsite and one phone screen (reports 02 and 03).

All three candidates were changing jobs and applied online for full-time roles.

Android engineer

Coverage: One full-time Android (mobile engineering) phone screen. The report dates the interview to October–December 2026 and went up on November 2, 2026. The candidate was put on a waitlist.

What came up

The call opened with a short project deep dive and a question on coroutines and suspend, then moved to a comment-filtering coding question with a points-threshold follow-up.

Questions we found

Algorithms / Data Structures

Android Knowledge

1 detailed candidate timeline

01: Project deep dive, then the dog and cat comment filter

Interview period: 2026 Q4 · Report date: November 2, 2026 · Role: Android engineer, full-time · Degree listed: Master's · Candidate's rating: Positive, average difficulty · Result: Waitlisted

Technical phone screen

1. The first 15–20 minutes felt like a small project deep dive. The candidate introduced a past project and answered a few short follow-up questions about it.
2. Since this was an Android role, the interviewer also asked how the candidate understood coroutines and suspend.
3. Coding only started in the last 25 minutes. The candidate recognized it as a known dog person / cat person question, the one practiced in Filter Undesired Comments and Their Descendants. You get a set of comments, and each comment has a parent comment. Each comment is dog or cat. For a dog person, hide every cat comment along with all of its child comments; a cat person is the reverse. Output the IDs that need to be excluded.
4. The candidate found the problem itself easy: build the graph, scan it with BFS or DFS, and add each matching parent and all of its children to the result.
5. At first they misread the rule and thought a dog person's check was isDog && !isCat. The interviewer pointed out that only !isCat matters. The candidate thinks this may have cost them some points.
6. Follow-up: if a comment's score is above some threshold, it should still be shown. The candidate described a rough idea, and then time ran out.

The candidate found the interviewer friendly. They gave hints at the key moments, and the back-and-forth went well.

Software engineer (general)

Coverage: Two reports for a full-time general software engineering role, both posted on November 1, 2026, nine minutes apart: a virtual onsite (dated October–December 2026) and a technical phone screen (dated July–September 2026). Both candidates list a PhD, were changing jobs and applied online. They may be the same person, but the reports don't say so, so they're counted separately here.

What came up

The virtual onsite had four rounds: a hiring manager chat, an agentic design round, a coding round built around a TCP server, and a redesign of an existing ML prediction service. The phone screen was a two-part comment-filtering question on Reddit post data.

Questions we found

System Design

Project Coding

Algorithms / Data Structures

2 detailed candidate timelines

02: Three hires and one soft no in a closely matched onsite

Interview period: 2026 Q4 · Report date: November 1, 2026 · Role: Software engineer (general), full-time · Degree listed: PhD · Candidate's rating: Positive, average difficulty · Result: Rejected

The candidate said the role's background was exactly what they do in their current job, a very close match, and HR was very positive too. Of the four rounds, three came back as hire and one as a soft no, and they were still rejected.

Round 1: Hiring manager, behavioral

1. Mostly a talk about the candidate's current work and what this team is about to build. Because their backgrounds lined up so closely, the candidate felt they were, in a way, giving the hiring manager some direction.

Round 2: Agentic design

1. Design a BigQuery agent. That's the whole prompt as reported. FastPrep's practice version is Design a BigQuery Analytics Agent; its details are the practice version's own, not the interview's.

Round 3: Coding

1. The TCP question. The test cases are actually a TCP client, and you write the server. The candidate said you need to know how TCP communication works. They learned it on the spot during the interview, time was very tight, and they finished only the basic part.

The report doesn't say what the server had to do beyond that.

Round 4: Improving an existing ML prediction service

1. You're given an ML prediction service that already exists. First say where its problems are. Then update the system to fix the problems you just named. The FastPrep practice version is Design Production ML Serving Observability.

Afterward

The candidate calls the soft no the system design round. From the list, that's most likely round 4, but the post doesn't say so directly. They thought their answer went well and didn't expect it to be picked apart; maybe the bar is high.

In a reply, the candidate added that it was very close. The system design interviewer said they didn't want to block, and the hiring manager really wanted them, so they felt a bit wronged. The feedback was roughly that they reached for a cache for everything. The candidate disagreed: they had covered load balancing, sharding, queues and caching, and didn't feel they used that much cache. Their guess is that they missed one point the interviewer was looking for.

03: A two-part comment filter on Reddit post data

Interview period: 2026 Q3 · Report date: November 1, 2026 · Role: Software engineer (general), full-time · Degree listed: PhD · Candidate's rating: Positive, average difficulty · Result: Passed

Technical phone screen

1. You get some Reddit post JSON. Part 1: filter the comments by the user's preference. If the user is a cat person, remove the dog comments and each one's whole subtree. This is the question practiced in Filter Undesired Comments and Their Descendants.
2. Part 2: each comment has points. If a subtree's sum is over the threshold, keep it. The candidate stressed that you have to compute the subtree sums starting from every dog comment.

The candidate found Part 2 a bit tricky. They got Part 1 right, but Part 2 still had a bug when time ran out. The interviewer passed them anyway.

Patterns across the reports

1. The comment filter showed up in two different roles. Reports 01 (Android) and 03 (general SWE) both got the dog person / cat person comment filter in a phone screen, and both had a points or score threshold added after the basic filter. In 03 the threshold applies to a subtree's sum; 01 describes a comment's score above a threshold and gives no more detail. Neither candidate finished the threshold part: 01 ran out of time after describing an approach, and 03 had a bug they couldn't fix in time. 03 passed; 01 was waitlisted.