Founding Quant Researcher

Storm2; San Francisco (CA); On-site; USD 225,000 - 300,000; Full time

Job summary

A stealth startup in WealthTech is seeking a Founding Quant in San Francisco to develop advanced portfolio management tools. Candidates should have a strong background in portfolio construction and asset allocation, with proficiency in Python and AI methodologies. This role involves optimizing investment strategies under real-world constraints, including tax considerations. The compensation ranges from $225,000 to $300,000 annually plus bonuses and equity opportunities.

Qualifications

• Experience with portfolio construction and management in wealth management.
• Proficiency in Python and modern AI/ML frameworks.
• Working knowledge of tax considerations in investment.

Responsibilities

• Developing portfolio construction and optimization methods under real-world constraints.
• Implementing approaches across risk-based and return-based optimization frameworks.
• Building scalable Python systems for rebalancing and risk management.
• Designing risk models and validating portfolio constraints.
• Encoding U.S. tax considerations into portfolio logic.
• Using AI tools in experimentation and research workflows.
• Translating complex quantitative behavior into clear explanations.

Skills

Portfolio construction

Asset allocation

Python

AI tools

Education

Advanced degree in quantitative field (Masters or Ph.D.)

Job description

Overview

Company: Stealth Start Up in the Wealth Tech Space

Compensation: 225-300k + bonus + equity

We are currently partnering with a stealth startup within the WealthTech space who specalizes in building AI Native Portfolio Management tools in search for a Founding Quant to join their organization. We are looking for an individual who has deep experience in portfolio contruction, mangament, and asset allocation in either the retail wealth management space, SMAs, or RIA platforms.

Responsibilities

• Developing portfolio construction and optimization methods that operate effectively under real-world constraints such as taxes, trading frictions, and regulatory requirements
• Implementing and refining approaches across risk-based, return-based, and multi-objective optimization frameworks
• Building scalable, production-ready Python systems to support rebalancing, risk management, and tax-sensitive decision-making
• Designing and validating risk models, covariance estimation techniques, and portfolio constraints used in live portfolios
• Encoding U.S. tax considerations — including wash sale rules, capital gains treatment, and asset location — directly into portfolio logic
• Using modern AI tools and ML frameworks as part of daily experimentation and research workflows
• Translating complex quantitative behavior into clear explanations that can be understood by non-technical stakeholders

Qualifications

• Advanced degree in quantitative field (Masters or Ph.D.)
• Experience with Python.
• Exposure to advisor-led or retail investment platforms (e.g., SMAs, RIA tools)