This job is closed
Applications are no longer available for this announcement. Explore current related opportunities below.
Job description
An elite, high-leverage quant firm is building a self-improving hedge fund powered by thousands of machine learning models—increasingly designed and refined by AI itself. This is already how the fund compounds edge every day.
They are forming a small team with one mandate: build AI that conducts quantitative research autonomously and continuously. No bureaucracy. No politics. Significant equity. Extreme ownership.
The ideal candidate holds a PhD in mathematics, statistics, physics, EECS or a comparably quantitative field (or equivalent demonstrated depth), with 7+ years of quantitative research across more than one firm and strategies that traded real capital and made money. They bring deep working knowledge of factor models, risk decomposition, portfolio optimization, transaction cost modeling and execution, strong statistical judgment that prevents self-deception through overfitting, excellent written communication that can change someone’s mind, and the ability to own work end-to-end in Python including the unglamorous parts. Experience in global equities and cross-sectional stock selection, modern portfolio risk literature, numerical linear algebra or quadratic/conic solvers, or machine learning applied to financial prediction with a clear sense of its limits is a plus.
This role expands the set of things the fund knows how to do: which signals are worth pursuing, which risk structures are worth taking, which ideas look good in backtest and reliably die in production, and where the market is genuinely exploitable. You will develop and evaluate alpha across horizons and data types, work on covariance and factor risk estimation—including estimator choice, shrinkage, conditioning and the diagnostics that catch a degrading risk model before the portfolio does—and own the optimizer, from problem formulation and constraint design to numerical conditioning, solver behavior and the sensitivity of every resulting portfolio. You will help decide what data to buy versus build, productionize what works, write clear research reports and derivations, ship them, and monitor them in live markets.
This is the rarest quant research seat available: expand what a self-improving hedge fund can know and do, with extreme ownership, significant equity, and nothing standing between you and the frontier -
#J-18808-Ljbffr
Worksite address
san francisco, CA, 94199, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.