Quant Engineer

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Job details:

Quant Engineer role in San Francisco with a Series A FinTech startup This Jobot Job is hosted by: Brandon Bays

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Salary: $300,000 - $375,000 per year

A bit about us: Quant Engineer

Location: San Francisco, CA

Work Model: Four days per week in office

Compensation: $300,000–$375,000

About Us

We are an early-stage financial technology company building data, pricing, and research infrastructure for the private markets.

Our platform transforms complex and fragmented market information into actionable pricing and investment intelligence for leading asset managers, investment banks, venture funds, and other sophisticated financial institutions.

Following a recently completed Series A financing, we are expanding our quantitative and data capabilities as we build foundational infrastructure for a rapidly growing asset class.

Why join us? Private assets do not have the transparent exchanges, continuous pricing, or standardized datasets available in the public markets. Solving that problem requires sophisticated quantitative modeling, creative feature development, and robust data infrastructure.

As our Quant Engineer, you will have substantial ownership over the models and systems powering our core products. You will work with proprietary financial datasets, solve complex pricing problems, and see your work used directly by major financial institutions.

This is an opportunity to combine quantitative finance, machine learning, data engineering, and customer-facing problem-solving within a small and highly capable team.

Job Details As a Quant Engineer, you will build and improve pricing models for illiquid private-market assets while developing the data infrastructure that supports our models and customer-facing platform.

Responsibilities:

Build, maintain, and improve quantitative pricing models for illiquid assets

Research new datasets, features, and market signals that can improve model performance

Design and build pipelines that collect, parse, validate, and store financial data

Develop machine-learning systems using structured and unstructured datasets

Experiment with LLMs to automate data ingestion, extraction, and quality-control workflows

Improve the scalability and efficiency of existing data pipelines

Produce custom analyses and data deliverables for institutional clients

Explain quantitative methodologies and data-collection strategies during select client conversations

Collaborate closely with engineering, product, sales, and company leadership

Qualifications:

Professional experience as a Quant at a trading desk, hedge fund, bank, asset manager, or comparable institutional financial environment

Strong foundation in statistics, probability, applied mathematics, financial modeling, or machine learning

Degree in mathematics, statistics, data science, computer science, financial engineering, or another quantitative discipline

Master’s degree preferred

Strong software-engineering and data-engineering capabilities

Experience working with large, complex, or imperfect financial datasets

Ability to translate technical concepts for both quantitative and non-technical audiences

Comfortable operating with significant ownership in an early-stage environment

Able to work from our San Francisco office four days per week

Interested in hearing more? Easy Apply now by clicking the "Easy Apply" button.

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