u/Aggravating-Ant-8234

X post : https://x.com/ranaharshraj7/status/2068353994800660617?

This article contains the overall experience of my 3rd year onsite internship in Tokyo, Japan.
Not a research intern

Role : AI development engineer

Internship benefits + stipend : Flight tickets(to and fro) + flat to stay + travel card + bi weekly Tokyo tours (cruise, disneyworld, baseball match in tokyo dome, fujisan visit, etc) + wifi + 300,000 yen per month.

PPO conversion : 7.8 million yen + bonus
They will take care of visa and other formalities.

The complete process started in ~oct'23 and went till jan'24.
Internship duration was standard 8 weeks.

R1: resume shortlist + >7.5 cgpa
R2: 2 dsa (medium - hard) in 75 minutes (One was graph based, don't remember the other one) -> had to solve both to go ahead

Around ~18 people got shortlisted including me.

The interview process was very fragmented ranging in 2 months.

Interview round 1 : Fit round asking if we would be okay travelling or possible shifting to Japan, will parents be okay, want to do masters, etc -> generic questions.

We all had to again give a test -> quick mental maths and some ML basics. (30 minutes)

Preparation for interview round 2 : had to create a 6-8 page ppt (should have some Japanese text too) demonstrating your life + hobbies + technical interests + why Japan?

Interview round 2: It was conducted by the CTO, HR and the team lead + translator. They started with their introduction, then I had to present my ppt in next 10-15 minutes (I did learn some Japanese), TL asked about some of my past projects, CTO asked about what problem I would want to solve for the world and give an approximate plan.
I don't remember if there were any technical questions asked apart from projects and ppt (i still have that ppt lmao)

After a week, I got a call that I was selected, and was the only one to get selected from my college.

Verdict : Accepted offer
~3 others were selected from different colleges in India.

>Looking back, the interviews themselves weren't hard. The real issue was how fragmented the process was. At IITs, once you get an offer, you're automatically exempted from further interviews. But the results for this company only came in at the end of January 2024, while all the good companies in Phase 1 had already finished their selections by the first week of December 2023. I had to skip multiple interviews because of the above rule and more importantly I had trust in me. I don't recommend this to anyone but this rule needs to change.

About my work, I led the ASR part (mostly whisper, it's fine tuning, etc) and it's integration with LLM's and my teammates worked on the VAD integration.
But there was no technical speech knowledge required.
Just critical thinking and connecting to 1st principles.

PS : As far as I know they only go to IIT's for internship (not sure about now).
PPS : no tokens were harmed in writing this.

This was during campus placements-dec'24 (freshers take notes).
CTC : 84 LPA (including esops)

Disclaimer : No DSA was asked

To get an interview call, we had to build a VAD (Voice Activity Detector) from scratch in 2.5 hours on-site (with proctorship), although we were allowed any tool we could use except any external api's (I do remember u/ChatGPTapp giving me hallucinated responses that I had to go back to docs.)

Dataset was provided (~50 audio files).

We were judged on :

1. Accuracy of speech detection
2. Code quality
3. Possible improvements to the approach that we couldn't implement.

Also any kind of architecture was welcome for building VAD, I went with Denoiser + WebRTC (GMM based) approach as I knew it would give the highest accuracy and they had the highest weightage for the same.

7 got shortlisted and I was one among them.
The interview was led by the head of ASR team.

We started with my internship experience at Tokyo where I led the ASR, VAD and open source LLM's integration for a company which were into warehouse management robots, and pivoting into adding speech functionalities into the robots.
We discussed :
> how I patched the WER using NLP to correct/ fill in the gaps if voice breaks in between.
> what VAD architecture I used
> how did I reduce CPU/GPU load

How I used different u/OpenAI whisper models to get p95 latency <800ms.
and high level scaling methodologies I used to benchmark and stress test STT models.

Then we moved onto Ml and transformer's basics (because I was more into LLM's) :

&gt; explain whisper-jax architecture and how it processes audio chunks
&gt; coding naive gradient descent from scratch on docs (as u/GoogleColab was auto completing for me lmao)
&gt; explain perplexity and what other benchmarks do we use for LLM's
&gt; touched self attention, differences between encoder - decoder architecture and that day i realized that almost all the new SOTA models are decoder only
&gt; He also went into a deep discussion as how we can relate linear algebra with transformers (I took a LinAl course)

At last, we discussed u/SarvamAI Bulbul models, especially why they use latent space decomposition and how that helps separate speech content from speaker/style representations.

*PS: No tokens were harmed in writing this.
**PS: Please don't dm for guidance, I am not a mentor. But if you want to discuss any specific resource in AI or distributed systems hmu.

X post: https://x.com/ranaharshraj7/status/2065801122494001516?s=46&t=wCYnaDcXpAImKDhlo9yNeA

Base 65LPA + equity + bonus
It was an one hour interview headed by the chief AI officer and CTO in their office.

Started directly with my internship experience where I led the Speech pipeline at a commerce Robotics in tokyo.
We had a deep dive into how I reduced the p95 latency to ~800ms and then we discussed a bit on using spacy to reduce the WER (increase accuracy)

Then we moved onto my ongoing project of training directly speech to speech transformer instead of traditional (speech -> text -> LLM -> text -> speech)
That time i was fine tuning qwen 3 decoder only model, but the loss wasn’t decreasing and he guided me some tricks to try.

Then we deep dived into my current role of data scientist and man he was smart.
We discussed in detail how to model credit risk and also the nitty gritty details of feature selection using PCA instead of traditional recursive elimination, isolation forest and so much more.

After this we went through the networking concepts as why we need queues when we can directly use web sockets for voice bots.
And we also covered a bit of GPU parallelism, more specifically FSDP2 and I coded the architecture of it.

Result : No hire
This was in Jan’26.
yoe (then) : ~6 months

As the title says, received an interview call for builder role.
I was reached by a CXO and I am 5 rounds in, HR hasn’t yet asked for expected comp.
How much should I quote for total compensation and Do the company provide WFH or transportation or food?

If there are any ex-razorpay peeps would love your input.
yoe: 1
Current Comp: ~20L (excluding esops - org isn’t public)