HRT
HireRight Holdings Corporation
stock NYSE

Curious about this statement: "One theory for RenTech's weaker recent returns is that they didn't bet big enough on ML"
Does that refer to the mediocre performance of their public funds (RIEF and RIDA)? I don't think those funds are really at all comparable to what XTX/HRT/etc. do. RIEF in particular is a low-turnover net long fund... I don't know as much about RIDA.
Medallion however *is* comparable of course. Is it well known that Medallion performance has lagged over the last 2-3 years? I haven't heard one way or another.

HRT Algo Dev Internship Interview

2027 New Grad QR CitSec + JS/HRT

One thing I’ve noticed is that Indian quant firms often expect one person to handle alpha research + strategy development, whereas firms like IMC, Citadel, HRT, etc. tend to have much more specialised roles.
So I’m curious about PMs at multi-manager funds like Millennium, BAM, Point72, etc.
If someone spends their career specialising mainly in one area (say, alpha research or trading), how do they eventually become capable of running an entire book?
Do PMs at these firms actually handle alpha, portfolio construction, sizing, risk, execution, etc. themselves? Or do they mainly make the investment decisions while a team of specialised researchers/traders/engineers supports them?
Would be interested to hear from people who have worked at multi-managers or large prop shops.

can u tell ab HRT algo dev + Cit/JS qr if you have them (not sure if u do)

Go all in on HRT like the rest of the sub last week

Hello everyone! I have experiences (many firsthand and others from trusted sources) for the following:
QT - Optiver, SIG, DV, Akuna, Citsec, Cit, Blackedge, OMC, Flow (r1), DRW (r1), 5R(r1), Jump(r1)
QR - HRT(Algo Dev), Akuna a few more
SWE - Most if not all of the Prop firms
Looking to learn about the following: DRW SWE, Headlands Research Developer, Trexquant QR. Hmu about 2027 procs only if you want to discuss them

Curious about this statement: "One theory for RenTech's weaker recent returns is that they didn't bet big enough on ML"
Does that refer to the mediocre performance of their public funds (RIEF and RIDA)? I don't think those funds are really at all comparable to what XTX/HRT/etc. do. RIEF in particular is a low-turnover net long fund... I don't know as much about RIDA.
Medallion however *is* comparable of course. Is it well known that Medallion performance has lagged over the last 2-3 years? I haven't heard one way or another.

HRT Algo Dev Internship Interview

2027 New Grad QR CitSec + JS/HRT

One thing I’ve noticed is that Indian quant firms often expect one person to handle alpha research + strategy development, whereas firms like IMC, Citadel, HRT, etc. tend to have much more specialised roles.
So I’m curious about PMs at multi-manager funds like Millennium, BAM, Point72, etc.
If someone spends their career specialising mainly in one area (say, alpha research or trading), how do they eventually become capable of running an entire book?
Do PMs at these firms actually handle alpha, portfolio construction, sizing, risk, execution, etc. themselves? Or do they mainly make the investment decisions while a team of specialised researchers/traders/engineers supports them?
Would be interested to hear from people who have worked at multi-managers or large prop shops.

can u tell ab HRT algo dev + Cit/JS qr if you have them (not sure if u do)

Go all in on HRT like the rest of the sub last week

Hello everyone! I have experiences (many firsthand and others from trusted sources) for the following:
QT - Optiver, SIG, DV, Akuna, Citsec, Cit, Blackedge, OMC, Flow (r1), DRW (r1), 5R(r1), Jump(r1)
QR - HRT(Algo Dev), Akuna a few more
SWE - Most if not all of the Prop firms
Looking to learn about the following: DRW SWE, Headlands Research Developer, Trexquant QR. Hmu about 2027 procs only if you want to discuss them

Breaking down a few things.
Literal "LLM" isn't that interesting. "LLMs, as a tool, or to extract some features from textual data". This style of NLP on text data is funnily enough approaching old school at this point. A few firms run this in in HFT fashion. Parse known macro events (FOMC, earnings, etc) and just use it to sweep market on news. Or also in mft/lft as yet another alt data signal. Notice in the HFT case, you don't really want to run attention and modern LLMs for obvious reasons...
What is interesting: things like attention, transformers, any modern DL techniques. Independently from models trained on text, if you want to just do modelling on numeric data with modern architectures, yes many people are doing this. Honestly this isn't even a hard conversation and people who aren't doing this are behind. If someone told you to model a function f(x1, x2, ...) ~ future px prediction. You just do whatever it takes that gives you the best results.
Firms like Jane Street, XTX, HRT really advertise and are generally known in the industry for doing a lot of DL modelling. But most other top shops are also doing this too

I'm not really in the HFT business so I'm less up to date with the state of the art there, but my understanding is the faster you go the stupider you have to be, what the fastest auto-traders are running is not even linear regression. People are definitely running ML at HFT firms though.
One of the reasons mid frequency has seen such a switch to ML is that the shorter your time horizon the more capacity constrained you are, so there's been a push for HFTs to get slower and move into more generic quant territory. My understanding (which might be incorrect) is HRT for example makes most of its money in mid freq now. And it goes the other way too, quant models are getting more data hungry and execution is becoming more important, so they're getting faster as well. There was a good FT Alphaville article on the convergence of these two groups (free if you sign up) [https://www.ft.com/content/d5c17e39-0983-4c14-9a7c-92c12cc44641](https://www.ft.com/content/d5c17e39-0983-4c14-9a7c-92c12cc44641)

What did you think of the HRT talk? I was in the audience, and while it was light on actual implementation details (understandably), I thought the notes on what didn’t work/what they didn’t do was really illuminating

I am an incoming sophomore and am still in the midst of preparing for the probability and expected value questions. I was wondering when is the latest I can apply to these firms and still get a fair shot: maven securities, walleye, flow, citadel securities, HRT, Deshaw, etc. Are there some other smaller firms that a sophomore can apply to? I don't know what I should be aiming for but I am also down for a quantitative trading/research role in an investment banking firm or something.

Well, the truth is almost all the big names have some effort on deep learning (models beyond linear/tree models). But how serious they are to do frontier AI Lab type of research (i.e., aiming to build foundational models for market) differ.
Just to name drop some: JS, Jump (not their AI tool team, their core team), Citadel/Citadel Securities, XTX, SIG DL, HRT HAIL, and so on.

Depends what you mean by comparable to a frontier lab, training large LLMs specifically, or large transformer-based models in general. The first is mostly internal tooling where the state of the art is much better, as others have said. The second is the main thing people at the competitive end of the market are doing. Firms that are serious about this are spending (or soon will be) on the order of $1bn/year on GPU compute, with clusters in the 10k to 100k+ range. For context, the estimates floating around for Kimi K3 put its training cluster at roughly 20k GPUs.
GPUs/basic ML in the colo is nothing new. I believe Jump were doing this a long time ago, XTX as well. The shift is the last \~3 years, where mid frequency trading has moved almost entirely from traditional linear regression over to model-based alpha generation.
There's definitely a divide in the market over this. I've talked to people at pod shops who flat out didn't believe me when I described the scale some firms are training at. One theory for RenTech's weaker recent returns is that they didn't bet big enough on ML, and there's a similar theory that the main P&L divide in this year's drawdowns was ML-heavy firms vs everyone else. Structurally, centralised training compute heavily favours collaborative shops, who for the most part seem to be the ones betting big on this.
Worth noting that even at that spend it's still nothing like frontier lab scale, so internal work tends to run months to years behind the labs, and the objectives are different. Nobody is doing months-long pretraining runs, and you're optimising for completely different data scale, model size and inference constraints than an LLM.
Some of this is semi-public. HRT gave a talk (NeurIPS or ICML I can't remember) that goes into a surprising amount of detail on their model stack, and Jane Street has released tours of their data centre on YouTube. I've also heard secondhand that they have a shared research project with Anthropic, who they're supposedly a large investor in.
Source: I work at a firm that does this