u/ArgumentLow4169

I applied for the Cloud AI Engineer role at Google. Around 10 days later, I received an assignment link with a message saying to complete it before 19 March. I completed the assignment on time.

After 1 day, I got an autogenerated email saying “Congratulations, you passed the assignment.” But after that, I haven’t received anything else from Google no recruiter call, no interview update, nothing.

Now I’m confused and a bit worried. Was this just an automated assessment process, or should I still expect further rounds/interviews from Google?

Has anyone else faced something similar with Google hiring? How long did it take for you to hear back after passing the assignment?

Would really appreciate any insights.

Has anyone here cleared the Amazon SDE1 OA, then got HR call for DSA rounds/interviews, but after that HR completely disappeared?

I cleared the OA and got a call from HR regarding the DSA interview rounds. After that, it’s been almost 2 months now. HR is not replying to emails, not picking calls, and there’s literally no update from their side.

I understand hiring can take time, but complete silence for this long is honestly frustrating.

Did this happen with anyone else?

What did you do in this situation?

Should I still wait or just move on?

Hi Everyone

i have finished an ML Engineer interview loop for HackerRank’s Chakra team, and honestly… this was very different from a normal AI/ML interview.

This did NOT feel like a “LeetCode + random ML trivia” interview.
The entire discussion was heavily focused on reasoning, production judgment, evaluation philosophy, conversational AI systems, and how you think under ambiguity.

The interviewer was extremely calm and conversational. No pressure tactics. But the questions were deceptively deep. A lot of them looked simple initially, but the real goal was to see whether you actually understand production AI systems beyond buzzwords.

The role itself is around Chakra their next-generation AI interviewer system. From what I understood, the core challenge is building an AI interviewer that behaves closer to a strong human interviewer:

• understanding when an answer is shallow
• deciding when to probe deeper
• maintaining fairness and consistency across massive interview volume
• evaluating candidates beyond keyword matching
• scaling judgment, not just question-answering

The interview was around 45–60 minutes and mostly discussion-driven.

A few things that stood out immediately:

• They care WAY more about thought process than textbook answers
• They keep digging deeper into “why”
• Almost every answer gets a follow-up question
• They are very interested in production trade-offs
• They want people who can connect ML quality ↔ real user behavior

A big portion of the interview was around conversational AI systems and evaluation infrastructure.

They asked me to walk through a real multi-turn conversational AI system I had built. I discussed an enterprise HR assistant system with:

• FastAPI backend
• RAG pipeline
• embeddings + retrieval
• context management
• role-aware retrieval
• session orchestration
• grounded responses

But the interesting part was the follow-up questions.

The interviewer immediately started digging into:
“How did you decide what conversational context to carry forward?”
“What signals told you the relevance-based context system was actually better?”
“Was the improvement because of removing noisy context or because of better selection logic?”
“How did you validate this in production?”

This was NOT surface-level prompting discussion.
They were trying to understand whether I can:

• reason about conversational memory
• connect offline evals to production behavior
• design feedback loops
• identify why a system improves instead of blindly optimizing metrics

A major theme across the interview was:
“Proxy metrics vs real-world quality.”

This came up repeatedly.

For example:

• How do you know your evaluation metric actually predicts user experience?
• What user behavior signals would you track?
• How would you correlate offline evaluation with production quality?
• How would you evaluate a generative AI system where the “correct” evaluation methodology doesn’t even exist yet?

This part honestly felt closer to research thinking + product thinking combined.

Another very strong focus area was:
“Production ML debugging.”

One question I got:
“What would you do if offline metrics looked strong, but production quality dropped after deployment?”

They wanted systematic reasoning:

• distribution shift
• preprocessing mismatch
• retrieval quality degradation
• latency/system failures
• edge-case behavior
• production telemetry
• real failure-case analysis

Another question:
“How do you decide whether poor validation performance should be solved with regularization or with data quality fixes?”

Again, not asking for textbook definitions.
They wanted diagnostic thinking.

The LLM section was also very practical.

Questions included:

• How do you optimize prompts for a task?
• When do you decide prompting has plateaued?
• When is fine-tuning worth it?
• How do you systematically reduce hallucinations and prompt instability?
• How would you design evaluation infrastructure for conversational AI at scale?

One thing I noticed:
They are NOT impressed by “I used GPT-4 + LangChain.”
They care much more about:

• evaluation methodology
• system reliability
• feedback loops
• production orchestration
• consistency
• grounding
• failure analysis
• trade-offs

The most interesting part came near the end when I asked questions about the role itself.

The interviewer explained that Chakra is trying to solve something much harder than simple Q&A:
“How do you build an AI interviewer that knows when an answer is shallow and when to probe deeper?”

That seems to be one of the core unsolved problems they’re actively working on.

From the discussion, their current approach is still partially heuristic-based:

• answer length
• confidence
• semantic alignment
• flow control
• conversation structure

But they want to evolve toward a learned “judgment layer.”

Honestly, that part sounded fascinating.

The interviewer also openly admitted that many parts are NOT solved yet, which I appreciated. It did not feel like corporate marketing. It felt like:
“Yeah, these are genuinely hard problems.”

A few important observations for anyone preparing:

1. DO NOT overfocus on theory-only preparation. You need practical production reasoning.
2. Be ready for deep follow-ups. If you mention something casually, they WILL explore it deeply.
3. Evaluation is a massive focus area. Offline metrics, online signals, user behavior correlation, feedback loops, benchmark design — all important.
4. Conversational AI understanding matters a lot. Especially:

• memory
• context handling
• retrieval quality
• probing logic
• grounding
• multi-turn reasoning

1. They care about systems thinking. Not just models.
2. The interview is conversational but intellectually heavy. You need to think out loud naturally.
3. Product intuition matters. A lot of questions were really: “How do you know your AI system is actually useful?”

My honest impression:
This was one of the more intellectually interesting AI interviews I’ve had.

Not because they asked impossible questions, but because they were testing real engineering judgment around modern AI systems rather than checking memorized answers.

It genuinely felt like they’re building difficult infrastructure problems around AI evaluation, conversational reasoning, and scalable interviewer quality.

If you’re preparing for Chakra / HackerRank ML roles:
Focus less on “define transformer architecture” and more on:

• evaluation pipelines
• production failures
• conversational systems
• grounding
• feedback loops
• data quality diagnosis
• online vs offline metrics
• LLM reliability
• retrieval quality
• human-AI interaction design

That’s where most of the discussion happened for me.