Lead AI Quant Engineer — Multi-Agent Sandbox

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

Benefits offered by this job

Total Rewards package

Discretionary bonus

401(k) match

Learning resources

Job summary

Franklin Templeton is seeking a Lead - AI Quant Engineer to architect multi-agent investment workflows and guide platform choices. You will stay hands-on in prototyping and critical implementations while mentoring engineers and ensuring governance-ready, auditable prototypes with verifiable data.

The role emphasizes evidence-driven practices, risk-aware design, and collaboration with senior leaders across investment, product, data, and AI governance. Hybrid work in CA is supported.

Qualifications

• Master’s or Doctoral degree preferred in a quantitative discipline, Computer Science, Engineering, ML, or related field.
• 8+ years of experience across quantitative engineering, data science, ML, or software engineering, including at least 3 years leading complex AI or quantitative systems.

Responsibilities

• Lead the end-to-end design of multi-agent investment workflows and guide platform and framework choices.
• Demonstrate architecture, evidence standards, and governance for prototypes with auditable decisions.
• Mentor engineers and data scientists, review code and designs, and establish reusable standards.

Skills

Python

Architecture

Leadership

Model risk governance

Education

Master’s or PhD in quantitative field

Tools

LLM frameworks

Kubernetes

Job description

Franklin Templeton is seeking a Lead - AI Quant Engineer to architect multi-agent investment workflows and guide platform choices. You will stay hands-on in prototyping and critical implementations while mentoring engineers and ensuring governance-ready, auditable prototypes with verifiable data.

The role emphasizes evidence-driven practices, risk-aware design, and collaboration with senior leaders across investment, product, data, and AI governance. Hybrid work in CA is supported.