Lead AI Quant Engineer for Multi-Agent Investing

Franklin Templeton; San Mateo (CA); Hybrid; USD 189,000 - 234,000; Full time

Benefits offered by this job

Paid Time Off

Health Coverage

401(k) match

Employee Stock Investment Plan (ESIP)

LEAP Education Assistance

Job summary

Franklin Templeton in San Mateo, CA, seeks a Lead - AI Quant Engineer to own Project Sandbox architecture and governance for multi-agent investment workflows. You will prototype, review, and lead critical implementation with a hands-on approach.

We look for 8+ years in quantitative engineering or related fields, expert Python, and strong skills in portfolio construction, risk analytics, and distributed systems.

Qualifications

• Master's or Doctoral degree in a quantitative field or equivalent experience.
• 8+ years of experience in quantitative engineering, data science, ML, or software engineering.
• Expert-level Python skills with portfolio construction and risk analytics.
• Experience building production LLM or agent-based systems.
• Strong architecture and distributed systems experience.
• Ability to explain technical trade-offs to senior stakeholders.

Responsibilities

• Set technical architecture and engineering roadmap for Project Sandbox.
• Lead multi-agent investment workflows and platform choices.
• Ensure deterministic risk services, audit trails, and governance.
• Mentor engineers and data scientists; lead reviews.
• Present evidence and readiness recommendations to senior leaders.

Skills

Python

Optimization

Risk analytics

Portfolio construction

Leadership

Communication

Agent-based systems

Kubernetes

Education

Master's or PhD in quantitative field

Tools

Kubernetes

Vector databases

CI/CD

Job description

Franklin Templeton in San Mateo, CA, seeks a Lead - AI Quant Engineer to own Project Sandbox architecture and governance for multi-agent investment workflows. You will prototype, review, and lead critical implementation with a hands-on approach.

We look for 8+ years in quantitative engineering or related fields, expert Python, and strong skills in portfolio construction, risk analytics, and distributed systems.