Machine learning is a critical pillar of Jane Street's global business, and our ever-changing trading environment serves as a unique, rapid-feedback platform for ML experimentation. Researchers at Jane Street are responsible for building models, strategies, and systems that price and trade a variety of financial instruments. As a mix of the trading and software engineering roles, this work involves many things: analyzing large datasets, building and testing models, creating new trading strategies, and writing the code that implements them. We're looking for people to join the research team with deep ML experience in either an applied or academic context. A good candidate should have a deep understanding of a wide variety of ML techniques, and a passion for tinkering with model architectures, feature transformations, and hyperparameters to generate robust inferences.

Welcome to Signals & Threads, in-depth conversations about every layer of the tech stack, from Jane Street. Today, I'm going to have a conversation with Andrey Mokhov about build systems. Build systems are an important but I think poorly understood and often unloved part of programming. Developers often end up with only a hazy understanding of what's going on with their build system learning just enough to figure out what arcane invocation they need to get the damn thing working and then stop thinking about it at that point, and that's a shame because build systems matter a lot to our experience as developers. A lot of what underlies a good developer experience really comes out of the build system that you use and also there's a lot of beautiful ideas and structure inside of build systems. Sadly, a lot of that beauty is obscured by a complex thicket of messy systems of different kinds and a complicated ecosystem of different build systems for different purposes, and I'm hoping that ...

Trading is a competitive business. You need great people and great technology, of course, but also trading strategies that make money. Where do those strategies come from? In this post we'll discuss how the interplay of data, math and technology informs how we develop and run strategies. Machine learning (ML) at Jane Street begins, unsurprisingly, with data.