SIG
Signet Jewelers Limited
stockNYSE

SIG London QT

>I’m also down to trade questions and process info for Jane Street, HRT, Five Rings, SIG, Optiver, IMC, DRW, Millennium, BAM, Squarepoint, AQR, Akuna, Flow Traders, Da Vinci, Virtu, Geneva, Morgan Stanley, JPMC, and BofA. Feel free to DM. whatf?!

Just got invited to the Citadel Securities quant trader internship superday in NYC and I’m trying to figure out how to prep. For anyone who’s done it: how many interviews was it, and what were they like? Was it mostly probability and mental math, or more market making games and trading-style questions? Did they dig into your resume or past projects at all? Any tips or things you wish you’d known going in would be super appreciated. I’m also down to trade questions and process info for Jane Street, HRT, Five Rings, SIG, Optiver, IMC, DRW, Millennium, BAM, Squarepoint, AQR, Akuna, Flow Traders, Da Vinci, Virtu, Geneva, Morgan Stanley, JPMC, and BofA. Feel free to DM. Any tips or things you wish you’d known going in would be super appreciated. Thanks!

Since you already have experience with processes at SIG and Jane Street, the core concepts will feel familiar, but Citadel Securities tends to push a faster pace and heavier mathematical rigor. The onsite typically spans four or five rounds, blending live market making games, probability puzzles, expected value calculations, and basic coding or logic checks. You will face interactive betting simulations where interviewers add new rules, reveal partial information, and expect you to adjust your quotes immediately. They care far less about textbook perfection and much more about how quickly you adapt to adverse selection, calculate conditional odds under time pressure, and manage your risk when a trade moves against you. Focus your review on conditional probability, Markov chains, random walks, and rapid mental arithmetic. Practice articulating your thought process aloud, because staying silent hurts more than offering a reasonable initial estimate and refining it as new clues appear. They also evaluate your commercial appetite and resilience, so be prepared to talk through how you handle risk and recover from losing bets. My team designed [interviews.chat](http://interviews.chat) to help candidates stay sharp under intense pressure, which consistently helps people secure offers across top trading desks.

SIG evaluates machine learning candidates across a mix of standard software engineering, systems design, and foundational probability. Unlike quantitative research tracks where the focus sits on stochastic calculus, the machine learning track leans heavily into practical execution. You can anticipate an initial technical screen covering data structures, algorithmic complexity, and numerical computing libraries, followed by a multistage final round. Because your background is in inference engineering, interviewers will likely dig into low latency serving, model quantization, memory footprint reduction, caching, and how hardware constraints dictate architectural choices. Expected value and probability questions will come up, as SIG weaves them into almost every technical role. You will want to be comfortable with conditional probability, Bayes rule, discrete distributions, and computing expectations quickly under pressure, though they do not expect advanced measure theory for an engineering track. Be ready to explain tradeoffs between accuracy and throughput, walk through production systems you built, and communicate your technical reasoning clearly. To help people navigate these fast paced technical and quantitative questions under pressure, my team designed an [interviews.chat](http://interviews.chat) to help candidates secure offers at competitive trading firms.