The interesting problems are the ones that don't round off.

You'll form and test hypotheses about market structure, build systematic strategies from the survivors, and run them in live markets. We care how you think — about data, about uncertainty, about when to stop. PhD in mathematics, physics, statistics, or a related field, or equivalent depth of self-directed study.

You have experience taking a signal from idea to live capital. You're comfortable with the full cycle — ideation, rigorous testing under realistic cost assumptions, implementation, and ongoing monitoring. You'll lead a small research agenda and mentor more junior researchers. 3+ years in systematic trading research required.

A focused role for someone with a deep understanding of how orders interact with market structure at intraday timescales. You'll work closely with both the research and execution teams. Familiarity with limit order book dynamics, adverse selection, and transaction cost modeling is essential.

You own live strategies in production — monitoring their behavior, managing risk in real time, and feeding what you see at the screen back into research. You think in distributions, stay calm when the market doesn't, and know the difference between a model breaking and a regime changing. Experience trading systematic or quantitative strategies required.

You care about the last basis point. You'll model and minimize transaction costs, design and tune execution algorithms, and work at the seam between research signals and the live order book. A deep understanding of market microstructure and a real feel for how orders actually fill.

You build the tools that researchers actually use — fast, reliable, and transparent. You care about correctness and hate surprises in production. Strong Python and/or C++ required; experience with high-frequency data pipelines, vectorized computation, or backtesting infrastructure is a strong signal.

You live at the boundary between research and production — turning a researcher's prototype into clean, fast, correct code that can hold capital. Strong software engineering plus enough mathematical literacy to argue with the research, not just translate it. Python and C++; numerical computing a plus.

You build and own the pipelines that turn raw market and alternative data into something researchers can trust — clean, point-in-time correct, and fast to query. You treat data quality as a first-class problem, not a chore. Experience with large time-series datasets and modern data infrastructure.

You'll own the low-latency execution infrastructure, market data systems, and risk controls that the firm runs on. We value deep expertise in systems programming and an adversarial mindset — you should be able to enumerate failure modes faster than you can name features. C++ and Linux internals are the foundation.