Quant Engineer: Illiquid Asset Pricing & ML Pipelines

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

Job summary

LeoForce in San Francisco, CA, seeks a Quant Engineer to build pricing models for illiquid private-market assets and to develop supporting data infrastructure. You will contribute to model development, data pipelines, and customer-facing platforms in an early-stage fintech team.

The role requires deep quantitative skills, strong software and data engineering capabilities, and the ability to translate complex concepts for diverse audiences.

Qualifications

• Strong foundation in statistics, probability, applied mathematics, financial modeling, or machine learning.
• Master’s degree preferred for advanced roles.
• Strong software-engineering and data-engineering capabilities.
• Experience with large, complex financial datasets.

Responsibilities

• Build, maintain, and improve quantitative pricing models for illiquid assets.
• Research datasets, features, and market signals to improve model performance.
• Design and build pipelines to collect, parse, validate, and store financial data.
• Develop ML systems using structured and unstructured data.
• Experiment with LLMs to automate data ingestion and QC workflows.
• Improve scalability and efficiency of data pipelines.
• Produce analyses and data deliverables for institutional clients.
• Explain methodologies during client conversations.
• Collaborate with engineering, product, sales, and leadership.

Skills

Statistics foundation

Machine learning

Software engineering

Data engineering

Ability to communicate technical ideas

Education

Bachelor's degree in mathematics, statistics, data science, computer science, financial engineering, or similar

Master’s degree preferred

Job description

LeoForce in San Francisco, CA, seeks a Quant Engineer to build pricing models for illiquid private-market assets and to develop supporting data infrastructure. You will contribute to model development, data pipelines, and customer-facing platforms in an early-stage fintech team.

The role requires deep quantitative skills, strong software and data engineering capabilities, and the ability to translate complex concepts for diverse audiences.