Quantitative Researcher - $900k TC $600k base

Sharpe Search; San Francisco (CA); On-site; USD 250,000 - 600,000; Full time

Benefits offered by this job

Significant equity

Job summary

Sharpe Search seeks a senior quantitative researcher to build an autonomous, self-improving hedge fund system driven by thousands of ML models. You will own research end-to-end, from signal development to production, emphasizing factor models, risk decomposition and execution.

Deep equity experience and proven profitability across real capital are highly valued. The role demands rigorous statistical thinking, clear written communication, and the ability to act with extreme ownership within a

Qualifications

• PhD in a quantitative field or equivalent demonstrated depth.
• 7+ years of quantitative research across multiple firms.
• Strong Python implementation and end-to-end ownership of work.

Responsibilities

• Develop and evaluate alphas across horizons and data types.
• Manage covariance and factor risk estimation and diagnostics.
• Own the optimizer from problem formulation to numerical conditioning and solver behavior.
• Communicate research results clearly and productionize successful ideas.

Skills

PhD in quantitative field

7+ years of quantitative research

Python

Factor models

Portfolio optimization

Risk decomposition

Transaction cost modeling

Machine learning for finance

Global equities experience

Cross-sectional stock selection

Education

PhD in mathematics/statistics/physics/EECS

Tools

Quadratic/conic solvers

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 -