How To Do A US Master’s Debt-Free in 2026 & Get Headhunted by Big Tech Recruiters (Meta SWE POV) - w/ Bharath transcript

I never applied to Meta person. The recruiters used her to me. Bharath is an engineer at Meta. Before that, an alphabet moonshot. And before either of those, 1300 job applications. I think I applied to nearly 1300-1400 jobs. I got around 13 calls, converted around 9 offers. So when he tells you what a recruiter reads first, he is not guessing. Your headline itself is super important because based on what you write in your headline, then I think that's your sales pitch that you are doing in few words.

This guy literally planned his whole master's degree experience backwards. School, funding, graduation, date, exit, everything. Whatever it is, I don't want to have a date on my head. That was very clear for me. And on where the hiring is actually moving next. A lot of MS system engineering is required right now, as much as ML development. By the end of this episode, I promise if you are looking to get hired in deep tech roles, you will know exactly where to start and what to do to get yourself started on that journey.

Please join me in welcoming Bharath Vermula to the Ready Set Do podcast. Without any further ado, let's jump in. Bharath, welcome to the show. Hi, Naman. Nice to be here. Yeah, for our listeners, I think it would be remiss for me to not mention how we've met. Actually, I don't know if we've ever met in person. But for our listeners, Bharath was the person I sublet my apartment at Purdue too. I guess it wasn't an apartment. It was like a house on 474 Maple Street, I guess.

And then many years later, I saw Bharath was doing really cool things in the world. So I figured I'd reach out and have him as a guest and he graciously accepted. So me and Bharath go way back, but also not really. And I figured it would be nice to start with that. But jumping to more important matters, obviously you're at meta. Can you talk to us a little bit about your process? It's been fairly recent since you joined.

So very curious around what your hiring process was like and how you applied. Maybe any strategies that you can share that might be relevant for folks applying to big tech, AI roles or related roles. So yeah, take it away. I think that would be a great place to start. Yeah, I'm super glad to be here now. I mean, as you mentioned, I still remember the 474 Maple Street days. I mean, that was by far my favorite house back in Indiana. Same. Actually, 100%. Yeah, it was just so great. Yeah, even the backyard area, right with the big tree. I don't know if you remember.

I had fairly good time with the folks out there coming to the meta process. I never applied to meta person. Okay, the recruiters reached out to me. So I think I was working at a Google X company. I think that was a primary pulling factor for them to reach out to me. I think the process was very straightforward. I mean, at least at the time I got hired, but I think the process has changed a bit now. They have introduced more AI coding based rounds now.

And I think they are more tighter compared to how they used to be. I think meta has tightened its weight a little bit. But I think especially at the time I joined, there were two DSA rounds and one system design round and one behavioral rounds. It was not a tough interview, per se, out of all the interviews I've given. So I think the most toughest interviews I've given was for the con firms. So I think none of the interviews shook me a lot. Apart from the interviews I gave for the con firms. I think it was fairly straightforward. I had a lot of prep during my college days. So I think that helped me a lot. I had to just brush up a little bit. A few days, the most frequent questions. Pretty much that's all I would say.

I mean, if a lead court game is already very good, then I think you could definitely crack it. I think meta is not super tight compared to the interviews that happen in India. So is there still a lead court round or is there just no lead court whatsoever? I think the industry standard is so far something like this. They are definitely going to test the lead court skills. I don't think they will, I don't see any time soon they'll take up the lead court round.

Because optimization and knowing the complexity of the code you write is super important even with the help of AI. If you as an engineer is not able to comprehend the distinction between them. I think if you can just choose a known one, no one would want to hire you. That's my understanding. So there's definitely one DSA round for meta. I think the same is happening with Google as well. That's what I'm calling. So there's going to have one lead court round and one AI coding round, one system design round. That's a technical part of it.

And of course, the manager round is still going to be there. And then can you also speak to a little bit around your role currently that you were doing before meta? And then what role you were joined or what role you were approached for at meta? And I know there's like a lot of diversification right in those roles. So I'm sure when you were preparing for that, you would have done all of that legwork. So if you can share anything around that, I think that would be super helpful.

You know, I was mainly working on privacy and privacy side of things for data governance side of things. When I was working for Verily, which is a Google X company to talk to you about. And if I can jump in, sorry, when you see Google, exactly what does that mean? Because I'm assuming a few of our listeners might not be super familiar. So Google has moonshot projects, right? I think when Google got restructured into Alphabet, so their philosophy was something like this. Google is their, you know, main ship that produces money.

But they wanted few moonshot projects through which they wanted to create more wealth for themselves or more value for the customers as well. So I think they wanted lean ships under Alphabet. You know, Google by far is very slow because of various reasons. It has like a very huge system and a lot of processes set up. And if you're starting a new initiative and if you're adapting the same slower processes among, you know, among the smaller companies, I don't think they thought this is not the way to compete.

We want to try out new things that can run lean and run fast and run as their own companies. Some, some day they would be independent Alphabet, but Alphabet would be just a shareholder in them. But it is one such ship. There are many such companies. You must have heard about Wing. They're doing drone deliveries. So we are also Google X company. It was a moonshot. So very is also a moonshot. Very similar. Got it. That's super helpful. And then just out of curiosity, you mentioned you were head hunted by meta recruiters.

Do you think that was just purely by chance or was that something that you did? Maybe you had your LinkedIn optimizes or you were posting. Was that anything that you did to the best of your knowledge or was that actually purely accidental, so to speak? Fair question. I think I was trying to keep my LinkedIn up to date for most to the best of my knowledge. I always try to keep and I think the reason why I wanted to do that is since I was already working for a Google X company, I wanted to get head hunted rather than me going and applying.

I know that LinkedIn algorithm actually pushes the people who have the right to headline and who tries to keep their profile up to date. Whoever has the most updated profiles, LinkedIn algorithm pushes them to the front of the recruiters. So that is the reason I used to keep refreshing my LinkedIn at periodic times whenever I can. So I think that had in some aspects is what I would definitely say. So that's really interesting and I'm sensing this is a really important signal because it sounded like you were not doing, you know, like posting every day or just going crazy about LinkedIn and trying to keep it optimized.

But you still got it to do the thing that everybody dreams of, right, which is to be head hunted. And in your case, it was literally a big tech, you know, a fang recruiter. So if almost in like a crash course type manner, can you walk us through kind of your process for, let's say somebody listening to this has about 10, 15 minutes that they would want to invest every week. Maybe you can tell me this is done 15 minutes every month.

But yeah, if you can paint that picture for us, what goes into really effectively refreshing once LinkedIn like you just said. So I think there is language, right, I think that your language and is very super important. When recruiters look at your profile, I think that multiple signals that they want to look at. First is, I mean, if you're not from any popular places, okay, then they would want to see relevance for the kind of roles that they're trying to hire.

For example, meta right now is trying to look a lot for a year olds. If you can project some of your a based projects, if you're not from a big tech or any popular companies, that would be a fair strategy. If you already have some decent signals picked up to good places, okay, then projecting your technical work to, you know, metric given language and showing leadership is a very good signal. And your headline itself is like super important because based on what you write in your headline, I think that's your like the sales pitch.

What you're doing in the few words, I think that's the most important thing that you have to do to get right before anyone headhunting you. Like these are the basics, but boring basics, but I think everyone has to follow this. Yeah, and more often than not, this is a pattern we see, right, like it's the boring basics that move the needle the most and people just tend to not worry about as much when this is all that realistically one should be worried about.

And in your case, clearly, it's such a great example of you don't need to, you know, boil the ocean here, just do the basics, but that go from zero to 80, and then the 8200 almost just takes care of itself. So jumping back, and we can almost, you know, go like reverse chronological order here. I'm curious about how you landed your job at barely. I know you were fresh out of college at the time granted it was a slightly different time in the job market and stuff has since deteriorated maybe you would disagree but at least that's my read on that.

But if you can walk us through your experience as a fresh grad entering the job market and what that was like any strategies that you applied that worked for you and even the interview process maybe I think that would be super helpful. Sure, sounds good. I think I came in 2022 January for my master's even before coming to my master's I was back in the day I think there was a very strong signal for doing I mean, being strong at lead code and getting hired there was like direct correlation.

This was back in the John of 2022, where I think this was free chart GBT era. So I think I came with a mindset that I have to really nail my lead code skills. Okay, so I think since I came in spring, I know that I shouldn't be an internship. Okay, so I was free and I was just taking one research course to you know to complete my credits and complete my master's faster. That was more on a lighter note. And along with that, I started my research internship. That was, you know, pure blessing, I would say, because I just have to because of the research internship, I just have to pay one semester worth of fee. Rest of the whole fee was taken care of my body.

And I just put my pedal on the brake and on the gas and I just did my lead code on the whole summer. And I interviewed here and there, but my main goal was to finish off my master's in one and up here. And which I did. Okay, I completed all my courses in one and up here. And in the rest of things, I couldn't, you know, figure out my job search for in this three semesters. Then I took a summer off and then just focused on job search.

And I think the main important thing I discovered in United States was, if you don't have any signals built out, you have to apply a lot. Okay, I think I applied to nearly 1300, 1400 jobs. As my sister used to say, the convert the callback rate in US is 0.5 to 1%. If you don't have any, you know, any this thing, I think the stats exactly worked out for me. I got around 13 calls, converted around nine offers. So I think that's how I got in finally.

But I think lead code applications, I think these are these are like the basics, I would say number of applications. If you don't have any, if you have already a strong profile in India, a niche job profile, I think then your game strategy should be very different. Mine was more generic profile. I built a lot of back and engineering stuff back in India. So I think so I had to put in a lot of applications. But if you already have like some research experience in the specific domain, I think you should go for specific targeted companies.

But if not, you definitely have to put in a lot of applications to get noticed and get your interview calls and convert them and also be still be good at lead code even though I strongly suggest that. So I want to, this is very interesting, right? So I want to double click on this one line that you said that was if you don't have a lot of signal on your resume. That means it's time for spray and pray. And I think if I were to infer what you're trying to say, that would mean that if you do have like a certain signal towards a certain thing, you I'm assuming you mean it should probably probably be a more targeted approach.

That's right, that's right. If you already worked in Brooklyn, India, and you're coming to United States and already going to decent or better college, better university. I'm sure a lot of people would want to head hunt your, you know, once you apply the conversion rates, I mean, out of every hundred applications, you might easily get 10 callbacks, then you should be more careful about whom you're interviewing with. Then interviewing with everyone and wasting your time. If you're from a normal college and, you know, going to a decent school, then you will definitely have to say like hard time, you know, getting like 0.5 to one callback out of every hundred applications. So that's what I mean.

Makes sense. So obviously one aspect of having a strong signal is what you just said where you already have a decent pedigree on your resume. You have like a brand name that folks recognize and, you know, appreciate. Are there any other types of signals? Would it count as a signal if somebody has done, I don't know, like super specific work throughout their career on just VLSI, you know, or just pick any single type niche type of work? Would that does that count or not really? Definitely. I think that definitely strongly counts.

I can go to an example. I have seen one of my friend at my college. He was, I think he did his BSE or something, but I think he was, he was, he didn't realize his potential early on on what to do in life. And then I think during his BSE or he didn't do his beat up, but he did something else. But long story short, he was able to publish to big conferences like CVPR. And he did some computer vision related work where I think one of his GitHub repository went viral during his college days.

Okay, I think it was start by around 2000 people or something like that. I think it was on extracting tables and, you know, this was pre-charged GPTRI again. This was back in like 2017, 2018. That was when he did his work, I believe. So I think people got a lot of traction and his work published to CVPR. When he came to the United States, I mean, first thing is it's really commendable that he was able to pull off to Purdue.

And the second thing when he came to Purdue, when he was applied, I think many people recognize that, oh, you're that guy, you know, who did this work. And many people even headhunted him because of that repository he put online. So even if you go to a college and work under some professor who has like a decent name and produce some, you know, recognizable work, I mean, at the least if you can fill up your resume, better case you have a publication, best case is if you have like a popular work.

Any of these three levels, right, if you can do something for yourself where you're spending your time productively and producing something that's valued, I think definitely people value that. Got it. So I just want to kind of to close the loop on this. The algorithm here, if you will, is if no signal slash generic broad profile, spray and pray, if you're able to identify any semblance of a signal, whatever that looks like and you just also underline the three, you know, major ones at least target that particular thing instead of just broadly applying to anything and everything that's open in the world.

Probably if you can figure out what you want to do, that's like the best case if you can, at least you should be good at anything you pick up. Right. And actually, that was going to be my next question, which was that, do you think there's any value for folks that are more generic slash broad that maybe with a little bit of direct effort can get to a more signal optimized profile maybe in like two, three months. And so is that worth doing or are they better off just probably sticking to the old plan and spraying and praying.

I think there are enough opportunities to open, even if you're good at very good at leading code, especially the time I interviewed. So I think you could easily convert a few jobs. There are a lot of companies which are good at coding. They still want software engineers. If you go somewhere, build a profile, build something good, I think that's a good way to, you know, step yourself up line by line. No one, I think no one figures it out all in the beginning and that's okay.

But I think you should be dedicated at what to do and take it from there. Even that's a valid approach is what I would say. So Bharat, when we were talking, you mentioned that even before you had moved to the US or I think even when you had just started to consider a masters in CS in the US. You mentioned that you had your entire plan baked out in which you knew when you were going to graduate, you knew the ballpark of how much debt you were okay with.

And it just sounded like you just had everything planned. So obviously that's super interesting and for what it's worth, it's the exact opposite of my approach. But we don't need to get into that. I am curious if you can share what that plan was and you know how you were able to execute on that like to the dot without even missing a single hoop. Sure. I think, I mean, everyone's journey is unique. I can tell about my journey here. So I think speaking about my journey, I think I already worked for four years in India before coming to United States.

I used to work as an associate or senior software engineer at Goldman Sachs in India. But I think that job strongly pushed me to you know, go to United States because I felt that the opportunities to work on like really good things in the United States was much better compared to India. That was my strong motivating factor. Coming to my plan, right, I think, I think it was very clear for me, right, I think I had aging parents, and I don't know whether I would want to stay in the United States or I would want to come back.

One thing was very clear for me. Whatever it is, I don't want to have a date on my head. That was very clear for me. I think that that made my picture very clear. As much as possible, there's a way to reduce the data. I think I should execute that. I didn't have everything planned out, but I think I put a few universities. I wanted to go to a good university for say, okay, and I got into Purdue and among others. I think Purdue was offering, when I did some research, Purdue, around 60-70% of the students get RA or TA opportunities, where most of their college fee would be waived off and on top of it, which you get a stipend.

I thought since I have like decent experience back in India and, you know, and I was in the right zone that, you know, I should get hired by a good company and go to some good place. I had some mindset figured out also because of my stage of my life. I only work for four years in India. That motivated me to sketch out things ahead. I think even that, then figuring out as things come. And so your plan was to definitely go to a place that, I'm assuming because you said you didn't want any debt, that can either look like a place where you're all but guaranteed a really good job or where you have a really low tuition. Is that, am I looking at that right then?

I think my primary goal was to go to a good place, okay. So I think out of the universities I got into, I think the top ones were Columbia, Yuma, Samhurst and Purdue. I was trying to make a decision between three of them, okay. I think I was definitely excited for Columbia, but when I looked at the fee and especially at the time I was coming to the States, I think a lot of people are going into big techs without very big brand reputation.

And I think open A and anthropic were out of the equation back in the day. So I think if you're able to go into Google meta and if you're doing the same kind of work, I mean going to a big college, I felt like there's no point having one crore of debt on my head and looking ahead in my life to play that off. Even if I have to come back to India, I thought that it's not a good strategy to attend to my parents. So I thought the best way, the best things I was considering was then my choice broke down between Yuma, Samhurst and Purdue.

I saw that Purdue is a very good school and Purdue does offer like a lot of these assistantships, which I thought in the worst case I'd be able to pull off for one semester, if not for long. So I think from day one I started reaching out to professors and making my intention clear on how I want to do things. And I think few people asked me if I would want to do a PhD if I worked under them. I clearly said no because I don't want to create false hopes and then create a bad reputation for myself.

So I think still I was able to pull off a research internship. I think that just happened. So yeah, I think having some light in your head, I think there's a really good way when you're coming to Masters or first you're doing anything in life, I would say. Because you have a clear objective to hunt for. Totally. And then tell me about why it was so important for you to graduate in one and a half years outside of the tuition.

Was the intention just to save that extra semester of rent then? Because you would still have to pay the same tuition, right? Definitely. I think it's about the time, right? I think you can work a little more. I worked four years back in India. I felt like it's more better for me if I start as early as possible. So I felt that if I can finish it off earlier, I can start my job early. I'm just not wasting my time unless I want to do research, which was not my objective back in the day.

So my goal was to do my Masters in the best way possible and learn the things that I could and move on to the job. That was very clear in my head. So I felt I would be wasting my time if I was in the university rather than going to a job. I see. Yeah, that makes total sense. I think any discussion around this generally, at least in these times, runs into the uncomfortable area of would all of this still work today, right?

Because, you know, like when I put out an episode like this, folks are like, this is great. This is super helpful. But the times have changed and these, you know, plans or strategies or, you know, just ideas that you executed on no longer works. Because of the times being different. Where do you land on that? I realize it's a subjective question by definition. So don't worry about trying to be, you know, all like correct and all, but just curious for your unique perspective around how much has really changed?

Like, how does Bharath do if he comes to the US in 2026, still with four years of work? Versus when he did come in 2022, right? I think, you know, one thing I realized when I came to my master's, it was one of the best times I could go for education. The reason being, the market itself was going through a phase shift, okay, with the advent of AI. I think a lot of things have changed. I think, and I'm glad that chart GPD happened when I was in college, rather than when I was in the workforce.

Because I think people discuss a lot and I think the information sharing that happens or knowledge sharing that happens when you're in university is very different when you're in, you know, when you come out of the college. And I think if you can, I think one opportunity I can see in the market right now is a lot of ML system engineering is required right now, as much as ML development. And if you can go to a college, okay, because a lot of infrastructure build out is happening, I think Microsoft, Meta, Google and Oracle, all the big companies including AWS are is like investing tons and tons of money on infrastructure.

And they would definitely need people who can optimize this infrastructure, because a billion dollars wasted every day is like, save this like $365 billion a year. I think there's a lot of opportunity here. So I would say if you're coming to straight to college, I would rather, I would definitely, you know, invest myself in creating the right skills, associating with professors, okay, I'm probably trying to publish somewhere and create a name for myself.

I've seen a person, okay, who happens to, you know, do some hackathons and going into bastion. Okay, I was just looking at her profile. I think she came out at the right time. Okay. So she came out, I think she was interested in systems and I think I don't remember her name, but I saw her profile in LinkedIn. But I felt that she did the right things in her college, worked on KV cash and some other transformer related ML systems work, and she got into bastion. And the great part was I think she joined bastion when it was a billion dollar company.

In a month, the bastion become a $10 billion company. I think it's just 10x jump for stock values. I think that's that's the kind of opportunity you have when you're working for ML systems companies right now. They're going crazy. So I think there's a great opportunity here, doubling down on ML systems and you're all your lead code work is still gonna definitely pay off is what I can see as opportunity here. I love any answer that doesn't resort to do more is a man is like, Nope, don't come, you're done. So really appreciate you sharing that jumping or shifting gears just a little bit you've been heavily involved with privacy and security in AI agents.

Maybe you can you share a little bit around the work that you're doing on that front and I would love to look at that from the perspective of maybe a day in the life at Meta you know so just you know what you're working on I understand that you probably can't speak to any projects directly but however much you can share about what a general day in the life looks like and also kind of layer in any work or interesting work that you're doing on the privacy and security in agent AI agents front that would be cool.

Sure, I think my work at Meta is on systemic optimizations for training stack. So I think as I was just telling right I think Meta itself is investing a huge amount of money in accelerators and things like that. I'm just trying to give the best I can without revealing any property totally bad. Yeah, so I think as the stack is growing, I think the new challenges come in place where I think every day we spend a lot of money on training but if this whole training fleet is not optimized, the two friends we're losing out, we're not able to run new experiments that we're not able to achieve the new potential.

If the stack itself is not like super optimized for running all the outputs possible all the experiments possible. And the second is if there is somewhere to optimize the whole stack and some of the hardware is underutilized where you're still paying for the power pooling and you know the hardware itself, then you're losing a lot of money on the table. So these are some examples that I can give off for a mass systemic optimizations.

You try to hunt for those opportunities there by saving money for Meta overall for the ads models that we try to train on. And the second question you asked me was the work on like the agents and privacy and security stuff. I think this is some work I'm doing on my own. Oh cool, got it. During my master's, I think I did my research internship in a cyber safety lab where I worked with the US government where we developed some data ingestion pipe where I developed some data ingestion pipelines for the digital forensic group.

And along and the Google X company also I worked on some of the privacy related stuff. And on the side, I'm trying for a better visa. I think that is the reason I'm trying to work on this agent stuff. I think I find this space to be really cool. I think as I'm feeling about this space more and more, I'm getting more intrigue. As you can see right, I think a agentic systems right now are able to hack into other systems.

I think the future is a lot of cyber warfare is going to happen and companies are getting funded crazy in this space. If you see Palo Alto Network 3x the last one year and I think there is a company called as Armageddon which got funded $180 million as a seed fund. These are not at all a joke. Seed fund $180 million. I've never seen such numbers happen in the industry. So I think it's a crazy time that we are in and I'm trying to grow my profile since I have some relevance in this field so that I can get a better visa. So yeah, that's a long story short.

Yeah, so I'm curious. So let's this is very interesting, right? So I use agents a lot basically like every day to do just random work around the channel just, you know, trying to grow on socials and all of that. Where does one start? So I guess can you give any specific examples of what kind of you're working on and what somebody that might be interested in this space can spend their time on? Sure, I think there are multiple friends at which the, you know, there is opportunity. Okay.

First is the, you know, there are two ends of the spectrum rate. Okay. One is on the attacker side. Second is on the defensive side. Okay. I think both the sides play a cat and mouse game. They keep continuing playing a cat and mouse game. So I think the opportunity here is automating the attacks. Okay. So and automating the defenses. Okay. So till now what people were doing, people are most of the systemic things. Okay.

If you see most of the critical attacks happen due to social engineering. Yeah, if you can see agents can social engineer people very well. So, I mean, you can carry out an attack at a massive scale with the help of agents. You can ask me automation was already existing. Then what's different about agents? So the agents can take micro decisions, okay, which can act as a human and continuously work in the loop where they can, you know, automate a lot of manual workflow where if you want 1000 people to be sitting and taking those decisions and to carry out an attack, there you can make sit, you know, make install a small, small LLMs to take those decisions and run like a massive workflows that can run for days

to figure out what the vulnerabilities are in the system, even per se, if there's any person who has some weakness who can be socially exploited, even if that can be figured out, that's a great break through because in the system, you have to find always the weakest link in the chain to break the system. And that becomes super easy with the help of agents and on the on the defensive side, again, the same story happens.

How do you, you know, inference attack at scale, and how do you stop it? How do you contain it? All these are like interesting problems that are evolving on this space. So there's a lot of opportunity here, per se, because of this agent space, I'm sure more and more cyber security companies will be cyber, cyber security millionaires will come out of, you know, this whole opportunities, what my personal feeling is. Hmm. Interesting. So for those in our audience that want to build a stronger research profile, what are, I guess, let's say, you know, they're like standing at the door of the site, they don't fully know exactly what direction they can go in.

How can one ask themself and figure out like the first step that this is what I want to do research on and maybe they're not in college. So do you still have to be in college to do research or like how does all of that work? Not really. I think there are multiple ways you can try to do research. I think doing research is a very time taking and very slow process. I think if you don't know what you're interested in, try to collaborate with, I think the most simplest thing is try to collaborate with someone already doing research and try to assist the research and publish the research. That's what you would do probably doing in college.

But if you know some PSG friend of yours who is already pursuing some research or some master's friend of yours, try to associate yourself with them. They were reducing the burden of finding a problem and they're trying to, you know, get that flavor of things and try to see, you know, where you can land, whether you like the work you're doing or whether you're not. I think that's a good signal. And one other thing is, even if you're working with some big techs, if you reach out to a few professors, I'm sure they're always, you know, short of people.

They would want people, they want good engineers to execute their projects and help them build out more projects. But the main problem is trust. Who can they trust? So if you can do some work or publish some articles and then reach out to them, I'm sure they would want to talk to you. But I think you cannot just pray and see something to happen. I think you have to lay some groundwork and reach out to people, then it becomes a company effort for yourself.

Got it. And then obviously from there, if we were to continue that path, you mentioned that you were seeking a better visa. I know you mentioned EB2-NIW. For those in our audience that are not familiar, exactly what is it? Who is it for? And I guess what are the benefits in a nutshell over your traditional H-1B or L-1 type visas? I think NIW is the main path for a green card. You will still be standing on EB2 line. The main benefit is that you will not go through the PIRM process to get your I-140.

There are certain U.S.A.S. identify certain areas of interest every year and they publish a small article where they say these are the critical fields we need people in. If you are working on the critical fields and if you can show that you are already trying to work on some national interest, that is building a safer economy or protecting some cyber warfare for the people, creating a safer internet. There are many cases where you are trying to develop some drugs which are helpful for an average American.

So you have to show some relevance that you are already an expert in the field and you can take this field forward, thereby helping the national interest that they have published. So the crucial point is you have to write some essays for yourself saying that this is my proposed endeavor and get some letter of recommendations and some artifacts that you have produced, might be blogs or some publications, etc. And then you would put your petition to NIW to the U.S.A.S. officer where you could get the approval or denier.

If you can get your approval, basically you don't need to go through the PIRM. You can get your I-140 in 3-4 months. That's a great... I started this thing as a lay-off protection thing. But since I joined VETA, since we don't have PIRM, I think this has become my full-time thing where I have to get it. So that is the reason I am trying to do my NIW thing. But the broader goal is if I can go through this, the next goal would be going to higher visa such as O1 or KB1. That's a goal.

One of the things that I had heard that might be just fully incorrect is do you need to have a really high salary cap to be eligible for the NIW? I know you mentioned there were some criteria. Do you know for this year what may be one of those criteria is just to paint a richer picture for our audience? I don't think salary is the criteria. I think there are three prongs that you have to get satisfied from. So I think one of them is definitely a salary criteria. I'm not very well aware of the thresholds, but I'm sure that most of the Indians working in big techs should definitely be eligible or in a decent company per se.

And I think the second criteria is having some out-of-your-job. How are you showing your impact that is helping the nation interest is something I'm commonly seeing the RFE reasons for? I don't exactly recall the third prong. I don't want to give any misinformation. Makes sense. Totally. Yeah, yeah. Got it. And so if you kind of have progress in all of those three, you can pretty much apply as you said. You can pretty much make your case and get your NW.

Got it. No, that's super cool. Yeah, this has been really incredible. I really appreciate you sharing all of these cool insights. I think one last question, if I can ask before I let you go here, when it pertains to AI, right? Where exactly on the spectrum do you fall with all of it? I know at the time of recording this, there has been a huge outcry from one of the ex-employees at Anthropic who has been publicly saying that these machines are just going to end humanity and all of that.

If you saw that yet, it's very fresh in the news cycle. So as somebody that interfaces with this technology practically every day and sounds like even after your work day, where do you stand? Do you think it's like a bubble? Do you think it's overpowered? Do you think it's just a big deal? I guess, yeah, what is your take? I think there are multiple angles to your question here. Okay, so I think coming to the AI technology itself, I don't think we can deny it at this point.

Okay, because I've been seeing quite acceleration for my work streams with the help of AI, where if it is correctly configured, it's like a force multiplier for sure. Okay, so I think you cannot deny the existence of AI itself and the power of AI itself. So I'm like an AI supporter in that point of view. Speaking about the economy itself, I'm not very sure whether we are in a bubble or not, because I'm seeing all these massive valuations and massive funding.

I personally feel that things are overvalued at this point and they definitely need correction. I don't know when that would happen, but they are definitely overvalued. I believe from an economy point of view, we could be in a bubble, but that can only pan out in the next one to two years is what I can see, because every year the agents are becoming better and better. I think we haven't hit the plateau yet. At a model point of view, we might have hit the plateau and models might not be going, but the harnesses are still super good that they're solving the crazy problems.

And I think a lot of infrastructure, there's an opportunity for building a lot of infrastructure here that can automate a lot of things. But I don't believe the general intelligence is here. So I mean, I was just reading about the open AI, whole the open AI, solving the Navier stokes, etc. There's a lot of controversy around it doing a brute force search. It tried to copy some work of researchers, things like that. So I think a strong confidence signal could be when it does it at scale and tries to solve multiple important problems, not one marketing thing that can boost a lot of peer among people.

Rather, if it's delivering an algorithm every week and changing the lives of humans, then I would be super scared that the AGI is here. So I think these are all marketing gimmicks that people are trying to do, and it's a repeated pattern. Even the Chargipiti 3 was coming, I think they put a lot of fear in the humans. Like the general audience saying that something big is happening and you guys have to really be scared.

And I think even before Fable came out, they really scared the shit out of the people. I think if it's happening once or twice, I wouldn't call it a pattern, but it's just a mishap that's just happening. It's happening reliably and happening again and again. I think that would be a point when you should be scared. Till that point, I would say your jobs could be affected for sure, because these leaders could take any decisions, but there is still hope and opportunities for people to make progress on important things.

Yeah, as always, as I have for this entire conversation, I really appreciate just how nuanced that was, where all of what you said actually makes complete and total sense to me. Well, Bharat, this has been so awesome and thanks so much for taking the time here today to obviously catch up and more importantly, leave our listeners with such great nuggets around not just job search, but trying to always get ahead of the situation by planning. You don't always have to be a passenger, be it planning your master's degree or your future career.

It's all about looking into the future and doing what you can today to make sure that you get to that point in the future that you want to be at. And I think that is an incredibly powerful takeaway here for our listeners. So thank you so much for taking the time.