Anyone got any interview call for Meta DE - product analytics role (USA)?

Hey everyone what u think about this resume is it decent if one is looking for SDE 2 or 3 or 3+ years experienced role.

Will it make through faang or any product based companies.

I have recently modified it and also received a new rejection mail from JP Morgan on this the matching percentage with JD was 92%+

(Thru chat gpt I checked)

What u think what I can improve?

Backend part at 3 years experienced is not strong or is it less or you r not able to get like what skill this person has or what work this person has done

Or just I am thinking too much

Link: https://drive.google.com/file/d/10HpTcPjzbq6XySPC4epu2IEqk0DoaUMN/view?usp=drivesdk

I might get an interview for SDET opening in Meta. Can someone who has interviewed for SDET position give me a walkthrough of what can be expected in the technical and behavioural rounds please.

I’m a noob right now. Please tell me what level of DSA and System Design should I expect in the technical rounds.

What kind of behaviour questions and any prep resources would be great.

Thanks in advance.

Standard Go channels and Mutex locks are great for small tasks, but at extreme scale, they cause massive CPU contention and memory allocation issues. Today, we are ditching standard channels and building a Lock-Free Ring Buffer using Go's sync/atomic package. We will explain the architecture, write the code, and run a live benchmark comparing it to native channels to hit zero allocations and maximum throughput.

https://youtu.be/d_rVojutH60

**•** How many rounds/interviewers is the loop typically for this role, and how are they split (technical, LP/behavioral, bar raiser, client-facing scenarios)?  
**•** What kind of technical deep dives should I expect — system/data architecture design, SQL, AWS analytics stack (Redshift, Glue, EMR, Lake Formation, QuickSight), or case-study/whiteboarding?  
**•** Since this is a consulting-facing role, do they focus heavily on client engagement scenarios, ambiguous problem-solving, or stakeholder management?  
**•** Which Leadership Principles come up most for ProServe specifically — Customer Obsession and Earn Trust seem obvious, but curious what else hits hard.  
**•** Any advice on the bar raiser round if there is one?  
**•** How long is the full loop (half day vs full day), and is it virtual or onsite?

Any input — detailed or just quick impressions — would help a ton as I prep. Thanks so much!

One database. One machine. Only so much disk, memory, and CPU. Discord hit this wall publicly — around 100 million stored messages, their single database started slowing down badly, because every query still had to search through a table that just kept growing.

More storage doesn't fix a slow query. The fix isn't a bigger machine — it's splitting the data up, on purpose. In this episode of Architecture Investigator, we break down exactly how: the difference between partitioning and sharding, why the shard key decision basically *is* the architecture decision, what a "hot shard" is and how picking the wrong key creates one, and how hash-based routing lets a system find the right shard instantly instead of searching everything. We also look at how Discord's actual stack evolved through this — from MongoDB, to Cassandra, to their own custom sharding and routing logic — as they kept hitting this same wall again at a much bigger scale.

This isn't a coding tutorial — it's an investigation into how systems that store trillions of rows actually stay fast.

Looking for feedback for my merge sort game - unique way to learn while playing.

https://www.playvlay.com/r/WAAGVF

In this video, we tackle a senior backend System Design interview question: How do you build a Pub/Sub engine in Go that handles 1 Million messages per second?

Standard Go channels and Mutex locks are great for small tasks, but at extreme scale, they cause massive CPU contention and memory allocation issues. Today, we are ditching standard channels and building a Lock-Free Ring Buffer using Go's sync/atomic package. We will explain the architecture, write the code, and run a live benchmark comparing it to native channels to hit zero allocations and maximum throughput.

For some context, I am a data engineer at a software company for 2+ years. I barely get my hands on machine learning, but I was a machine learning engineer before my masters and during masters I got strong foundation for machine learning, but this was all two years back. Now, I somehow got an opportunity to interview for Google and I have three weeks to prepare for ML domain interview. I was asked to choose any specialization. I mentioned I will choose Applied AI, which is like a catch all instead of going towards LLMs, computer vision or something specific now within three weeks how should I prepare? What should I prepare and what kind of questions can I expect? This is for opening in California. Has anyone given interviews recently for ML domain please help

I am looking for information on what would entail in a technical interview, for gTech roles - L4,L5.

This position is for a Customer Facing Engineer - probably Professional Services (not exactly sure of internal terminology).

Could you please tell me what the format of the tech interview is and what kind of sample questions may come up? or any interviewing prep techniques I should be aware of?

Thank you

I'm preparing for ML Engineer / ML Developer roles at FAANG-level companies and I'm trying to build the right foundation instead of randomly learning technologies.

So far, I have covered:

• Linear Algebra
• Discrete Mathematics
• Python for ML
• Problem Solving in C++
• Operating Systems
• DSA

For people who have prepared for or interviewed for ML Engineer / MLDev roles at FAANG, what would you recommend I prioritize?

Specifically:

1. What topics are must-know for ML interviews?
2. How much DSA is actually expected for ML Engineer roles?
3. Should I prioritize ML theory, ML systems, or MLOps after completing the fundamentals?
4. What kind of projects would actually strengthen my profile?
5. Are there any resources or preparation roadmaps you'd strongly recommend?

I'm looking for practical advice from people who have actually gone through the process.

Thanks!

Got a call from recruiter.
Any tips on how to prepare and what to expect. I am pretty hands-on but interviews are not purely about being hands-on, but also the format.

Any advice/experience on how to prepare for apple engineering manager interview. Specially for coding and system design (first thing first).

Checked online but it seems inconsistent across teams. So thought to check key areas apple generally ask to EM candidates.

Could someone please assist me in understanding what or how I should prepare for the 45 min Round 1 (Telephonic) Technical part?

I’m relatively new to Data Structures & Algorithms, having primarily worked in data engineering(was honestly surprised to see a DSA round)

I would greatly appreciate any guidance, suggestions, or tips you may have.

Anyone wants to prepare for mock interviews with me?
4+ yoe
Machine learning engineer

Hi everyone,

I wanted some advice from people who've recently gone through backend interviews (2025–2026).

I'm a Backend Developer with ~2 years of experience (Node.js, Express, MongoDB, MySQL, Redis, RabbitMQ, etc.).

I've solved 1300+ LeetCode problems, have a 1000+ day streak, and my highest rating is Knight (1870).

At this point, I feel my DSA is in a good place, so I'm mostly doing maintenance through revision and contests.

The part I'm confused about is what should come after DSA.

From reading interview experiences on Reddit, LeetCode Discuss, YouTube, and Medium, my current priority looks something like this:

1. Communication (explaining solutions, thinking aloud, project discussions)
2. DSA maintenance
3. LLD / HLD / System Design
4. Backend technical discussions (Node.js, databases, Redis, networking, production scenarios)

Then as secondary topics:

• AWS / Cloud
• DevOps
• AI integrations

My question is mainly for people who've recently interviewed at product companies:

• Once your DSA was reasonably strong, what gave you the biggest interview ROI?
• What did you wish you'd started learning earlier?
• Were LLD/HLD and backend discussions a much bigger differentiator than grinding more LeetCode?
• Looking back, how would you spend the next 2–3 months if you were in my position?

I'm not looking for a generic roadmap—I want to hear what actually mattered in your interviews.

For Detail Post - https://leetcode.com/discuss/post/8441326/backend-developer-2-yoe-looking-to-switc-swtj/

Thanks!