Senior Quantitative Researcher

Numerai is building a self-improving hedge fund.

We run an institutional quant fund built from thousands of machine learning models. Increasingly, those models are built by AI, not people. This isn't a thesis we're pitching. It's already how Numerai gets smarter every day (see https://numer.ai).

The next step is to compound it. Numerai is forming a new team with one mandate: build AI that does quant research autonomously, all the time. Discover new features, build better risk models, improve how the fund turns thousands of signals into a portfolio.

Numerai is a small company with an unusual amount of leverage per person. No bureaucracy, no politics. Significant equity.

The Role

Your job is to expand the set of things Numerai 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 exploitable.

You will develop and evaluate alpha across horizons and data types, from slow fundamental structure to faster cross-sectional effects.

You will work on covariance and factor risk estimation: estimator choice, shrinkage, conditioning, missing and stale data, and the difference between a number that looks stable and a number that is actually right out of sample. You will build the diagnostics that catch a degrading risk model before the portfolio does.

You will work on the optimizer: problem formulation, constraint design, objective specification, numerical conditioning, solver behavior, and the sensitivity of the resulting portfolio to every assumption baked into it. You should be the person who notices that a constraint is quietly doing something nobody intended.

You will help decide what data we buy and what we build, and you'll productionize what works.

You will write up what you find. Research reports, derivations, and clear explanations of why the new formulation is better than the old one. Then you will ship it to production and monitor it.

Requirements

PhD in mathematics, statistics, physics, EECS, or a comparably quantitative field — or equivalent demonstrated depth

7+ years in quantitative research, ideally across more than one firm, with strategies that traded real capital and made money

Deep working knowledge of factor models, risk decomposition, portfolio optimization, transaction cost modeling, and execution

Strong statistical judgment: you have internalized how overfitting actually happens and you design research to avoid fooling yourself

Excellent written communication. Your research reports should be able to change someone's mind

Comfortable owning work end to end in Python, including the unglamorous parts

Nice to have

Global equities and cross-sectional stock selection experience

Familiarity with modern portfolio risk and construction literature

Experience with numerical linear algebra at scale, or with conic and quadratic solvers

Experience with machine learning applied to financial prediction, and a clear sense of where it helps and where it doesn't