Quant Engineer

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

Benefits offered by this job

Equal Opportunity Employer

Four days in-office per week

Job summary

Jobot is seeking a Quant Engineer in San Francisco to own pricing models for illiquid private-market assets and to build the data infrastructure powering our core platform. You will work with proprietary datasets, solve pricing problems, and deliver analyses used by institutional clients.

The role requires ownership in an early-stage environment, strong quantitative background, and the ability to operate from our San Francisco office four days per week.

Qualifications

• Master’s degree preferred in mathematics, statistics, data science, CS, financial engineering, or similar.
• Experience as Quant with institutional finance background preferred.
• Strong foundation in statistics, probability, applied mathematics, or ML.

Responsibilities

• Build, maintain, and improve pricing models for illiquid private-market assets.
• Develop data pipelines to support models and client-facing platform.
• Experiment with ML and LLMs to automate data ingestion and quality control workflows.
• Collaborate with engineering, product, sales, and leadership to scale infrastructure.

Education

Master’s degree preferred

Degree in quantitative discipline

Job description

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

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.

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