Ex-Citadel Quant and AI Researcher On Breaking In, Tech vs Finance Careers

0:00Nimit Sohoni:90 % of the battle in research is actually finding the right problems. This is Nimit Sohoni, Stanford PhD, AI researcher, and previously a quant at Citadel, and I asked him about both AI research and quant careers. Do you have any advice for someone who wants to move into AI research? If you want to sort of switch from a SWE track to AI track, there's got to be something behind it, right? We compared and contrasted these roles, which had some surprising insights. Yeah, I think, you know, quant actually probably has a better work-life balance than AI. Unlike in tech, you know, one thing I was surprised by, you know, culturally is how tight-lipped people are in finance, even within a firm.

0:39We also went deep into what he's actually working on now. The main challenge of Transformers is that... Here's the full episode.

0:50when you think about the opportunities that are not available to you without a phd what comes to

0:57Nimit Sohoni:mind yeah so i think you know there's not really too many opportunities that are you know actually unavailable to to people without a phd but some of them just get a lot easier with phds i mean And so academia is an obvious one that does require a PhD. That was never something I was super interested in for a lot of reasons. But I think some roles that definitely a PhD opens a lot of doors to are kind of the two that I've had experience with. One is sort of doing industry research in AI like I'm doing now. Or, you know, back in the day, there were a few different, like industry research in computer science or mathematics was a little bit more diverse, but more and more people are converging towards AI.

1:49Nimit Sohoni:So I'll just say like AI research is one of them where having a PhD helps or not that you know, a lot of people do AI research without a PhD, but you know, the type and shape of the role can look kind of different. and another one is quantitative finance so again a lot of people go into quant um you know out of undergrad but certainly having a phd um like uh opens you up to you know some some sort of different opportunities and it can be a lot easier to get your foot in the door there so if if we think concretely let's say i was going for an ai researcher role or something like that are you saying the phd helps you in that first step of filtering or does it help somewhere else in the process and getting one of those roles?

2:32Nimit Sohoni:Yeah. So I think it's, it's both. So certainly it's a lot easier to get an interview if you've differentiated yourself from the pack in some way. You know, just applying for AI research role at a, you know, top firm can be, can be difficult, you know, if you don't have, you know, whatever the, you know, right schools, quote unquote, on your resume, the right internships, the right connections, whatever. but it's certainly doable and then I guess like I think like more less transactionally having a or like doing the PhD can like develop like a key critical skill set that can help you as as along your path towards becoming a great AI researcher but of course you know there is an argument for being thrown into the fire as well and just like kind of learning on the job and that's certainly an option that works for many people.

3:26Nimit Sohoni:I think, you know, there are some things that are harder to do in industry than in academia, like kind of the more exploratory first principles, like fundamental research without necessarily like a direct application. You know, in an industry definitely skews a lot heavier towards the applied side of things. But I think like having that fundamental background can be very valuable depending on what kind of research you're targeting. You mentioned the type and the shape of the role could be different if you had a PhD versus not. Could you give an example of what you mean? If you're working on more like engineering heavy stuff in AI, so, you know, building, you know, training or evaluation infrastructure, you know, working on like, you know, data processing, things like that.

4:12Nimit Sohoni:Those are not things like a PhD is really necessary for at all. I'd say if you want to do more like sort of pie in the sky type stuff like uh you know architecture design things like that um a phd can be you know can be helpful there because um you know you have more time to kind of explore directions that may not pay off in the short term um but you know again there are examples of people being successful in um you know without a phd with or without a phd in both both domains um so I think if your only goal is to be an AI researcher and you're not super tied to the particular type of work you do, you just really want to get into the field, a PhD is definitely not necessary.

4:58Nimit Sohoni:But I think if you're still in the exploration phase of your career and you want to find a problem that really draws your interest, then a PhD can be a good way to do that. You mentioned the PhD skill set or something that you kind of develop when you get a PhD. What is that skill set? I would say like 90 % of the battle in research is actually finding the right problems. So you have to find a problem that is interesting, it's meaningful, that people are actually going to care if you solve it. You have to sometimes convince people it's interesting because they might not have thought about it the same way.

5:41Nimit Sohoni:And then you have to execute. and you need to make sure it's a problem that's appropriately scoped that is actually tractable for you to make progress on. So I think like all of those things were not skills that I had developed. I was more just like execution was my strength. And so definitely that was a big learning process for me during the PhD is like that sort of like research taste and problem selection. And this is something that, you know, just like being immersed in the field, you know, really helps with, you know, once you've read enough papers, you know, talk to enough people, you kind of get a sense of the patterns and trends that are going on in the field.

6:25You mentioned the research taste and finding the right problems. If you could kind of condense what you learned in your PhD, is there maybe some top tips that kind of lead to you finding the right problems?

6:38Nimit Sohoni:I would say the main things that I find useful are just, you know, keeping abreast of the current literature, just reading, you know, as many as many papers as you can. And it doesn't have to be reading them like end to end, just like skimming abstracts, you know, seeing what's going on, what are people thinking about. And yeah, I think the other thing is just working your way up. So, you know, initially earlier in your career, you want to attack like, you know, very small sub problems that are, you know, you're reasonably likely to make progress on. Right. So one example, you know, example of this can be like you take a, you know, method and you try to extend it to some like, you know, special case or some something like slightly different from the original application.

7:24Nimit Sohoni:And then as you as you go on and as you mature as a researcher, you can start tackling like bigger and bigger problems. So, you know, not just like kind of extending previous work, but maybe coming up with like totally new ideas, things like that. So I think there is, you know, a gradual stage of maturation as a researcher. And I think, you know, some people do try to skip those steps. And I think that's generally, you know, inadvisable, I think. You mentioned keeping abreast of the literature. What's the go-to spot for you to kind of get your feet of the hot research paper to read? honestly uh twitter is uh one of the probably the main way that i uh keep up with papers i think if you um follow enough good people on uh on x uh i guess your your feed becomes like pretty curated to that so that's usually the first way i find out about stuff um obviously you know just like talking to people you know co-workers and so on but yeah i think x is my go-to so i try to I try to curate my feed in such a way that it's mostly machine learning papers and pictures of cute animals.

8:30Nimit Sohoni:Do you have a good starting point for someone who just wants to plug in? I pretty much started with following people I knew from Stanford and elsewhere. Professors whose papers I'd read, prominent people at big labs and so on. And then anytime they tweet a paper, they like a paper or something like that. But if it's interesting to me, I just click on that and I follow all the people tagged in or associated with that work. And that's, yeah, so I sort of grew my follow list organically via that. I understand after your PhD, you became a quantitative researcher at Citadel. Where were you in your career and why did you decide to become a quant?

9:13Nimit Sohoni:Yeah, I joined Citadel Securities after graduation for my PhD. um and so there yeah so basically i had actually interned there um right before the summer right before graduating um and i liked it a lot the reason i decided to intern um just kind of i wanted to see what else was out there um i had a few friends who had interned at sodel uh at sodel securities and you know enjoyed it or at other quant firms um and i was uh just kind of curious, you know, by that point, I'd been working in AI research for like four to five years. And yeah, like I said, I was generally, you know, when I entered my PhD, I was interested in careers in which I could apply my interests in mathematics and computation.

10:00Nimit Sohoni:And so I think the three major careers of that form at the time were, you know, machine learning research, which I already had experience with, you know, quantitative finance. And then the last one, maybe quantum computing, but, you know, it was a much smaller sort of domain and one I had no experience with, although that one's also kind of blowing up these days. So yeah, quant finance, I was just, just kind of curious and, you know, I'd heard good things. And so I decided to intern and I ended up liking it a lot. I think it was, you know, refreshing in some ways. You know, like I said, the PhD is a grind, you know, you can burn out at various points.

10:40Nimit Sohoni:And it was kind of a fresh, fresh set of problems, totally different environment. Well, it's funny that you say that the grind of the PhD, you kind of took a break to become a closet at all, because I've heard that the work culture is pretty intense at these finance companies. Is that the case? Yeah, so that is the reputation. But I think that definitely varies a lot based on the team you're on the firm you're at um yeah i personally i had a pretty great work-life balance uh as funny as that might sound um as a quant um you know i think one reason is that um you know traders typically uh will work you know trading hours or whatever locale they're in you know of course there are markets all over the world you know apac uh you know um europe and so on but uh you're in the u.s US traders are typically working around US trading hours and, of course, a little bit before and after just to prepare and stuff.

11:41Nimit Sohoni:And so I think that generally has a sort of trickle down effect on the culture where most people are just kind of really clustered working around trading hours and then don't take their work home all too much. So even though the work can be done at any time, I think that is sort of how the office culture operates. Yeah, when it comes to quantitative finance or quant work, how would you describe the work? It really, really depends a lot on both the team you're on, like the sector you focus on, whether you're at a hedge fund or a market maker, whether you're like front office or back office, quant, and of course the company.

12:23Nimit Sohoni:And so some quants will spend all their time just like on alpha generation so you know generating new um you know trading strategies and uh you know backtesting them and and so on and then putting them into practice and monetizing them um you know i mean well some people will focus just on the alpha some people will focus on the you know monetization or you can be you know like a risk quant so you're basically um not necessarily generating strategies at all but just like um you know trying to uh come up with metrics to capture uh risk and you know avoid that uh reduce risk without reducing you know cutting into profits um you might be um you might be doing like data analysis so uh you might have like a ton of like historical like trade data and stuff and analyzing them in various ways and so on so um yeah i think uh yeah um yeah like i said like you know hedge fund versus market making they're actually very different problems so i think uh the thing that unifies all of them is really um you know having a strong math background.

13:25Like in the day-to-day, let's say, you know, your project that you're currently working on, like what would that look like? What would the shape of that problem look like? And how does math concretely play a role?

13:36Nimit Sohoni:You know, depending on what sector you're in, you know, there's a lot of different math that will come into play. I mean, you know, sort of the backbone of finance is stochastic calculus. So I think that comes up almost everywhere. But then there are other things like, you know, numerics, numerical optimization, numerical interpolation, things like that. You know, machine learning, of course, is, you know, now more and more firms are like getting into getting really deep into the deep learning space, even like establishing their own research, research arms that do like LLM type research and stuff like that.

14:12Nimit Sohoni:Yeah, numerical linear algebra. Yeah, so there's a lot of different math and it's actually very like, it's actually very diverse in terms of what people are always trying to come up with ways to apply like different fields of math to quant. I think some of it is just for fun kind of you know, because quants are such a mathy intellectual bunch, but there is actually a lot that, you know, underpins the entire field. So yeah, I mean, I think, yeah, stochastic calculus is probably the most unifying part. Like that's kind of the, you know, finance 101 type math. So you mentioned coding a lot as a quant.

14:55And I have had some friends who were Sweez at Citadel and these various companies understand the roles are quite different. How do quants and Sweez typically collaborate at these companies?

15:08Nimit Sohoni:Really depends a lot on the company. You know, some companies like, you know, Jane Street, for instance, like the number of people who are called quants is actually very small. And, you know, traders themselves are quite technical and like implement a lot of stuff. And then, of course, they have like software engineers as well. Whereas Citadel, I think, is a more quant forward firm. So I think, you know, you know, a quants might be, if not the largest like percentage of employees, like it might be like about equal in terms of the technical staff um and so yeah i think um you know there can be a lot of overlap in what a quant uh quantitative researcher and what a you know software engineer does and also you know between a quant and a trader um so um yeah it kind of just depends like at some firms i think it's more divorced where quants are really doing you know the the strategy work uh and then it's like kind of handed off to software engineers to implement but at other firms I think you might do a bit of both because you know of course like the the person like if if they have the implementational skills the person best posed to like actually implement something is the person who like understands all the you know reasons and you know edge cases and things like that and so yeah like I said you know I did a ton of coding mostly in C++, also some Python.

16:28If you were to compare and contrast finance and tech generally across these roles, what comes to mind?

16:37Nimit Sohoni:So I think a lot of the skill set, first of all, is actually quite similar. Yeah, like I said, math and computer science were my main interests, and I wanted a job that would leverage both of them. And I think that's been the case in quant, and that's also been the case in AI research that I've done. And so, you know, I knew nothing about finance before I joined Citadel Securities, but, you know, I read a few textbooks that were recommended by people. And, you know, that was really all I needed. And, yeah, from there, I just, you know, drew upon my sort of technical skills. And I think like AI research is a lot of the same way.

17:17Nimit Sohoni:I think if you have really strong fundamentals, you can pick up, you know, pick up the rest. so um yeah in terms of technical skills i don't think it was you know really a rough transition um either going you know going either way um i think uh you know obviously the you know culture is different uh you know sf versus new york uh those kind of things um yeah work work hours i would say yeah i think uh you know quant actually probably has a better work-life balance than ai um you know particularly because like um level of competition in ai right now um like uh you know it's it's just a very competitive space and so one of the ways you can gain a comparative advantage is just like by outworking your competition and that kind of is you know what happens uh in in practice a lot of places i know a lot of people who are just like working around the clock i've heard insane stories about the comp structures at quantitative finance firms is that all true like is it heavily bonus weighted and i've also heard stuff about garden leave so yeah in terms of comp yeah i think one thing is like you know there's not really standardized levels like there are in tech you know you can't just sit you know it's not like someone is just like ic5 and you kind of know like what you know kind of pay bands they're they um what they're making uh it's it's uh yeah i think comp is really driven by a few things um you know how the company does that year how your team does that year.

18:46Nimit Sohoni:If you are really on like the alpha side of things, like, you know, how you how your particular strategies did that year. And then, of course, there's like other things that play into it, like seniority, both in terms of, you know, hierarchy, if you're at one of the firms that you does have a kind of explicit hierarchy, or in terms of like, you know, just like tenure at the firm, or years of experience, things like that. And so, yeah, I think, you know, quant firms i think are more secretive and you know partly because of the you know relative lack of standardization so um it is kind of opaque in terms of like how those factors actually combine for your final comp but um yeah i think it you know it can be very bonus driven if you're really on the alpha side of things and that attracts some people to that kind of thing where they really would just want like as they want to be as exposed as possible to i guess like the fruits of their labor but it is you know the downside is it can be much riskier business as well um so yeah it's just more variable but like if you're you know more back office type thing i think the comp is you know probably a little bit more deterministic if you're not you know directly tied to alpha generation yeah it's interesting i mean because we were talking about ai research versus quants and obviously being a quant is uh famous for earning a lot if you have generated a lot of alpha i hear compensation like easily in the millions for for a lot of these people um but at the same time ai research also popped off too you know if you're the top one percent of either of these firms you're going to do very well yeah i mean yeah they're kind of crazy um yeah i mean these these things really do exist where people are making like nba player salaries and stuff i think you know for the for the median case uh yeah it's still it's still very good but I think, yeah, it's not exactly that outlandish.

20:42Nimit Sohoni:Yeah, sorry, you also mentioned stuff about NDAs and stuff and garden leave. So, or sorry, non-competes, I guess. So, yeah, I think, you know, so finance firms are very, you know, they're very serious about this sort of thing, you know, unlike in tech. You know, one thing I was surprised by, you know, culturally is how tight-lipped people are in finance even within a firm you know there's things that you can and cannot share across teams and or people might just want to be more secretive because you know they're protective of their alphas and so if you like know kind of what they're doing you can sort of re-implement a similar thing and like capture take over some of their alpha right because uh it what what makes it alpha is that it's you know secret if if more the more people know who know about it like the less profitable it's going to be for any individual and so um yeah i think it's you know quite secretive uh you know even even the firms that are you know have a reputation for being more open are actually quite secretive versus in tech you know people talk about things all the time and so it was a bit jarring for me returning it to tech and like hearing like people you know talk about what they're doing in like a you know very open way i was like wow like you're just going to tell me that for free so yeah a non-compete is like yeah probably the you know most notorious part of this is yes a lot of firms will um have a clause in your you know in the contract you sign at the beginning stating that you cannot work for a competitor for a period of time after you um after you leave the firm uh and this period of time is typically decided by the company and when you when you leave but it can be um anywhere from well it can be zero uh up to like two years uh i think i've heard like even up to three years for for some places but i think that's rare.

22:26Nimit Sohoni:I think the norm is, I would say the norm is like, you know, six months to two years. And so, yeah, during this period, you're basically just paid to not work. Yeah, it's called garden leave because I guess, you know, you sit at home and garden or whatever. And it's actually like, I mean, it's actually a quite interesting thing, you know, it creates interesting incentives for some people because you are typically compensated quite well during this garden leave period. So So it's not necessarily a downside for some people. And yeah, basically the idea is you won't leak ideas to your competitors.

23:03Nimit Sohoni:And by the time your garden leave is over, if you have some special alphas or trading strategies, two years down the line, they're probably not even relevant anymore. So it doesn't even matter. You mentioned the secrecy within quantitative finance. And I see a natural incentive here to kind of be hostile or I guess competing within the firm because my alpha is my alpha. I'm not going to help you. Did you ever feel that or see stories of that? Yeah, no, it's definitely a thing. You know, people are, yeah, I think a lot of people are reluctant or even forbidden to talk about any details basically of what they do.

23:47Nimit Sohoni:you know some people that's you know some people you know won't even don't even like say what sector you know they work on you know at least across companies and stuff like that so yeah I think that that's definitely a thing you know some firms are set up where it's like basically pods so you know one pod is just responsible for basically all of their p &l and then the firm takes a cut and so you know different pods might be working on you know very similar things unknowingly right but they're not sharing any of the information um and there is some logic behind this uh because uh the idea is you want to have uncorrelated uh you know um uncorrelated returns so if all the pods are like you know talking to each other sharing ideas you know chances are they're gonna start doing very similar things and then you know that exposes you to risk where you know what if the thing you're doing is actually wrong and uh you know you can wipe out not just one pod but entire team of them whereas if people are working independently um then you know that's that's not that's less of a risk earlier you mentioned a the top one percent of ai researchers and quants are going to do extraordinarily well i'm curious what sets the top one percent of ai researchers and quants apart from the rest there are a lot of things uh i think there are different ways to get to that point as well maybe raw technical skill like some people are just really really good at what they do able to you know the prototypical like 10x engineer that kind of thing and they just have you know a better a higher level of intuition and or execution speed um stuff like that um you know of course there's politics involved like uh you know people who are better at playing the political game can um you know rise up in the ranks i think in quant you know one thing is that it is a bit you know it's harder to game the system because there are kind of hard metrics that it's easier to evaluate how someone does especially you know if you're an alpha quant you know it's quite clear right like if you implement a strategy um and you make the firm a ton of money like that's obviously going to be recognized um i think you know in in ai you know it can be a little bit harder but of course i mean the analog might be like you publish like a seminal paper like you make a true breakthrough in the field um you know you you make you know the models much better than um yeah that sort of thing so um yeah i think it's yeah i guess it's probably similar to you know other domains it's just a combination of skill and like um you know playing playing the game i think being in the right place at the right time has a lot to do with it you know both in quant in terms of uh seeing something before other people do and then like making taking advantage capitalizing on market trends and and turning that into a profit or an AI, you know, like having the right idea at the right time.

26:39When it comes to quant firms, I'm kind of curious. There's all these tier lists out there. What are the top firms and why?

26:47Nimit Sohoni:Rentech, like we talked about, is one of the sort of mythical firms in this space. You know, you can't really argue with their returns, the historical returns over like a 20 year, 30 year period. It's pretty insane. So, you know, Rentech is maybe, you know, the gold standard, depending on who you ask. Then there are other firms like some of the slightly bigger ones like Jane Street, Citadel, Jump Trading, Hudson River. I think those are generally very well-regarded firms. And having that kind of thing on your resume can definitely be an asset to future quant rules and things like that. So yeah, very good firms, I think.

27:26Nimit Sohoni:Great technical talent, great returns, obviously. And then there are some like elite smaller ones, you know, similar to Rentech, like, you know, smaller, more secretive, less well known, but still very, very excellent returns. So yeah, TGS is one it's in Southern California. Yeah, XTX is another one, one of the newer firms. I think Radix is another newer firm that's, yeah, in that boat. So yeah. Are there any stories you working in the space that you think might be interesting? You know, finance firms do not mess around. So you hear stories about like, you know, people are just doing, you know, dumb things like, you know, traders or quants having like an internal WhatsApp group where they, you know, talk about strategies and they're like, like, so as a quant, you have like trading restrictions.

28:18Nimit Sohoni:You have to get all trades pre-approved. And so, of course, if you're, you know, someone who works in equities or something, you know, you probably are not going to be able to trade those tickers at all. But, you know, people try to get around it with their little WhatsApp groups or whatever, like telling their friends to, you know, buy these stocks or something, split the profits or whatever. If that happens and you get found out, you know, they're going to go after you. You'll get fired. Obviously, there'll be lawsuits. You can even go to jail. Yeah, there's like a few stories about this because it is against the law.

28:52Nimit Sohoni:and so um yeah heard heard more stories about this that's one of the things they tell you about in training actually is like uh yeah do not do this um similar with like non-competes you know people going to competitors or starting their own thing or something and like uh getting accused of taking strategies and stuff like that um yeah the all these firms have like elite legal teams and yeah just not something you want to mess with i've heard that in quantitative finance that it's kind of intense sometimes or rather people may get fired uh very often did you ever have like you just were working with someone that kind of disappeared yeah yeah i mean yes that that does happen um yeah i think it's interesting in quant because um like i said yeah a comp is a function of many things among uh among which is seniority and so i think your job security can actually be kind of u-shaped because senior quants like you know even if they're very good they just get very expensive after a while because that's sort of what the you know market rate is for for senior quants and so you know even even a good quant you know can stop being worth it after a while whereas like earlier career quants you know um you can uh you know they might be very good and also not command you know as high of a salary so yeah the job shape is a great not not usually like kind of like an inverse parabola almost um and yeah i mean people certainly get fired it's a quant in general i think has a culture of uh you know um well one is like up or out and two is like you know just trimming the low performers um and again i think this can become you know especially easy if you're like more like on the alpha side like if you're just not making money like it can be pretty clear but uh in in general like even for like you know engineers um yeah i think there is this kind of culture.

30:44Nimit Sohoni:Yeah, traders, of course, it's yeah, since they're, you know, making trades and stuff. Again, it's like very easy to monitor. So I think that can be even more, more brutal. Why did you leave Citadel to join Cartesia? When I joined Citadel, it was partly because I was just interested in learning about a new problem domain and like, you know, learning some, learning some new stuff, you know, learning about finance in general, I think was also like kind of interesting to me. And yeah, I think, you know, I became more financially literate as a result of things and stuff like that. Like it was, it was, it was a great learning experience for me and I was kind of optimizing for like growth potential partially as well.

31:21Nimit Sohoni:But yeah, I mean, by that point, you know, I'd been at Cidel for, you know, a couple of years and was, yeah, I think, you know, like it's, it's sort of like your, your, your growth, you know, at most places will kind of like accelerate for a bit and then like sort of taper off. But I think there was still a lot more to, to be learned had I decided to continue on that path. But I saw what was going on in the field of AI. When I graduated, actually, it was right before ChatGPT came out. And so I think a lot had changed even since I joined Citadel. And I heard that the founders of Cartesia were starting this company.

31:57Nimit Sohoni:And for context, I knew all of them from my PhD at Stanford. They were actually all in Chris Ray's lab with me. I knew Albert pretty well. And so, yeah, I had tons of respect for them. They're great researchers. I worked pretty closely with some of them. Albert was a good friend of mine, knew the other guys. And so it just seemed like a great opportunity and a great time to get back into the field of AI when things were sort of taking off. And I thought it would be great in terms of personal and technical growth. also the opportunity to join a small startup um was definitely something that was interested in me and uh kind of like shape the company and the culture uh you know as one of the earlier employees and so um yeah it was really um yeah i think uh yeah it was all about all about growth getting back into ai and um and i think like you know um there is like a definitely a different risk profile.