Quant Engineer

Jobot; San Francisco (CA); On-site; USD 300,000 - 375,000; Full time

Job summary

Jobot in San Francisco seeks a Quant Engineer to build and refine pricing models for illiquid private-market assets and to scale the data infrastructure powering these models and the customer-facing platform.

You will work with proprietary datasets, solve pricing problems, and deliver analyses used by major financial institutions, combining quantitative finance, ML, data engineering, and customer-facing problem-solving in a small, capable team.

Qualifications

• Experience as a Quant in a trading desk, hedge fund, bank, or asset manager.
• Strong foundation in statistics, probability, mathematics, financial modeling, or machine learning.
• Degree in mathematics, statistics, data science, computer science, financial engineering, or another quantitative discipline.
• Master's degree preferred.
• Able to work from our San Francisco office four days per week.

Responsibilities

• Build, maintain, and improve pricing models for illiquid private-market assets.
• Research datasets, features, and signals to boost model performance.
• Design and build pipelines to collect, parse, validate, and store financial data.
• Develop machine-learning systems using structured and unstructured data.
• Experiment with LLMs to automate data ingestion, extraction, and QC workflows.
• Improve scalability and efficiency of data pipelines.
• Produce analyses and data deliverables for institutional clients.
• Explain quantitative methods to technical and non-technical audiences.

Skills

Quantitative finance

Statistics

Machine learning

Financial modeling

Data engineering

Software engineering

SQL

Python

LLMs

Data pipelines

Education

Mathematics/Statistics/CS/Financial engineering

Master's degree preferred

Tools

Python

SQL

ML frameworks

Pandas

Job description

Job details

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

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

Compensation: $300,000 - $375,000

Location: San Francisco, CA

Work Model: Four days per week in office

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

Jobot is an Equal Opportunity Employer. We provide an inclusive work environment that celebrates diversity and all qualified candidates receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, military status, genetic information or any other basis protected by applicable federal, state, or local laws. Jobot also prohibits harassment of applicants or employees based on any of these protected categories. It is Jobot's policy to comply with all applicable federal, state and local laws respecting consideration of unemployment status in making hiring decisions.

Sometimes Jobot is required to perform background checks with your authorization. Jobot will consider qualified candidates with criminal histories in a manner consistent with any applicable federal, state, or local law regarding criminal backgrounds, including but not limited to the Los Angeles Fair Chance Initiative for Hiring and the San Francisco Fair Chance Ordinance.