Quant Engineer

Quant Engineer role in San Francisco with a Series A FinTech startup

This Jobot Job is hosted by: Brandon Bays
Are you a fit? Easy Apply now by clicking the "Apply Now" button and sending us your resume.
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 "Apply Now" button.

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