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Trading on the world’s electronic markets is highly competitive, and that challenge has led us to pursue cutting-edge research in machine learning, programmable hardware, applied mathematics, and compiler design, among other fields. An important part of how we’ve approached this work is by engaging and collaborating with academic researchers.

The Jane Street Visiting Researcher Program was designed to further facilitate that collaboration. The program is open to all active researchers with a doctorate. We welcome both current faculty and junior researchers fresh out of graduate school. We wish to learn more about visitors’ research while giving them the ability to evaluate their ideas in an industrial setting. Researchers come to work at Jane Street, lending their expertise to problems we care about, and working to communicate some of those insights to the broader academic community.

Researchers can join us as a visitor for as little as three months or up to a year, and while we enjoy working in person, remote opportunities are also available.

Alongside this open call for collaboration, we are seeking to fill a specific position within our type systems team for a junior visitor.

Potential research areas

Below is a list of areas that we think are especially well-suited to this program.

• Programming languages. Most of our software is written in OCaml, and we work on improving many aspects of the language and the compiler that supports it. You can read more about our efforts here.
• Graph-structured and incremental computation. This comes up in a variety of contexts, from evaluating financial models to designing build systems for compiling our code. We’ve built several systems in this space, and continue to work on improving the performance, scalability, and reliability of these systems.
• Networking. Trading makes a lot of unusual demands on the network, and this has led us to work on better approaches and abstractions for configuring and analyzing our network, all in the context of network designs that differ in important ways from the mainstream approaches to designing cloud-computing networks.
• Hardware synthesis. Reconfigurable hardware like FPGAs are an effective tool for building powerful accelerators for all sorts of applications, from packet processing to machine learning. The downside is that building hardware designs is slow and exacting work, and so we’ve done a lot of work to build tools and abstractions that simplify and accelerate the process.

That said, the list is not exhaustive, and we’re happy to consider collaborations in other areas.

If you’re interested, let us know

Please fill out this form to express interest. Don’t worry too much about every little detail. It’s an informal process, and we expect to tailor the program to each individual. We’ll be in touch if it sounds like there could be a good fit.