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Quantitative Engineer; Private Markets

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Jobot
Full Time position
Listed on 2026-07-21
Job specializations:
  • Software Development
    Data Scientist, Data Engineering, Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 300000 - 360000 USD Yearly USD 300000.00 360000.00 YEAR
Job Description & How to Apply Below
Position: Quantitative Engineer (Private Markets)
Quantitative Engineer (Private Markets

This Jobot Job is hosted by:
Jim Quirk
Are you a fit? Easy Apply now by clicking the "Apply Now" button and sending us your resume.
Salary: $300,000 - $360,000 per year

A bit about us:

We are an elite, well-funded fintech startup building the foundational data, pricing, and transaction infrastructure for the global private markets. Backed by a $16M Series A from the world's largest asset managers and investment banks, our platform tracks over $300B in proprietary transaction data and $4T in funding signals for 100,000+ companies. Based in San Francisco, our lean team thrives on technical rigor, deep engineering ownership, and solving complex, multi-trillion-dollar valuation challenges for the world's most sophisticated institutional investors.

Why join us?
  • Competitive Base Salary!
  • Extremely Competitive Benefits!
  • Flexible Work Schedules!
  • Accelerated Career Growth!
Job Details

Location:

San Francisco, CA (In-Office 4 Days/Week)
Compensation: $300,000 - $350,000 + Equity About the Company We are a rapidly growing, recently funded Series A fintech company building the data, pricing, and transaction infrastructure for the global private markets. Unlike public equities, private company shares do not trade continuously on centralized exchanges, making accurate pricing and market intelligence extremely difficult to establish. We are solving this challenge by aggregating vast amounts of proprietary secondary-market activity, funding data, and investor signals into a platform used by the world's leading financial institutions.

Backed by a $16M Series A round from major global asset managers and investment banks, our platform currently tracks:
Over $300 billion in proprietary transaction data Approximately $4 trillion in historical funding-round data Comprehensive coverage of more than 100,000 private companies and investors An elite institutional client base that collectively manages over $52 trillion in assets The Opportunity This is not a traditional internal quantitative role. It is a rare opportunity to apply institutional-quality modeling to proprietary, highly unique private-market data.

You will blend rigorous quantitative research with production data engineering and direct customer exposure, enjoying massive ownership within a lean, high-caliber team. As a Quantitative Engineer, you will take full ownership of the models and data infrastructure powering our flagship private-market intelligence products. A primary focus will be our predictive daily pricing engine for actively traded private companies. Because these are illiquid assets without clean, public price discovery, this work requires exceptional creativity and the ability to extract highly accurate signals from complex, imperfect datasets.

Responsibilities Model Development:
Build, maintain, and continuously improve complex pricing and valuation models for illiquid private-market assets. Feature Engineering:
Identify, test, and integrate new financial features and data signals to drive model accuracy. Production Pipelines:
Develop and optimize scalable production data pipelines that support machine learning models and client-facing products.

AI

Innovation: Experiment with Large Language Models (LLMs) to automate and scale data ingestion, entity classification, and automated quality assurance. Client Advisory:
Analyze massive proprietary datasets to deliver clear insights and join select technical conversations to explain methodology directly to sophisticated financial clients. Product Ownership:
Collaborate closely with a tight-knit engineering team to materially shape the technical architecture and product roadmap. Key Requirements Financial Rigor:
Previous quantitative experience at a trading desk, hedge fund, investment bank, or a similarly rigorous, data-driven financial environment. Mathematical Foundation:
Strong background in statistics, applied mathematics, financial modeling, machine learning, or quantitative research. Technical Stack & Data Engineering:
Robust programming skills with hands-on experience building and maintaining production-grade data pipelines. Communication

Skills:

Ability to…
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