Lead – AI Quant Engineer - Project Sandbox

At Franklin Templeton, we believe success is built through powerful partnerships. As a forward thinking asset manager, we build dynamic relationships with clients, understand their goals, and navigate complex markets together. We leverage cutting edge strategies and deep insights to unlock opportunities for long term wealth creation. Our talented, global teams bring expertise that is both broad and unique.

From our welcoming, inclusive, and supportive culture to our globally diverse business, we offer opportunities not only to help you reach your potential, but also to contribute to our clients’ success.

• Project Sandbox sits within Franklin Templeton's AI Platform and AI Investment Group. The team turns high-value investment ideas into governed, end-to-end prototypes that combine quantitative analytics, agentic AI, deterministic optimization, and live monitoring.
• The team works closely with investment professionals, product, data, engineering, Legal, Risk and Compliance, Global Investment Solutions, and AI governance stakeholders to ensure that prototypes are useful, explainable, auditable, and aligned with enterprise controls.
• The initial team is a focused, hands-on pod of software engineers, data scientists and LLM specialists, and a technical lead. The culture is evidence-driven and collaborative: team members challenge assumptions, document limitations, verify AI outputs against source data, and advance work only when the evidence and controls support the next stage.

• This role is the principal technical leader for Project Sandbox, accountable for architecture and evidence standards across quantitative research, agentic AI, portfolio construction, and risk engineering.
• The Lead - AI Quant Engineer will lead the end-to-end design of multi-agent investment workflows and guide platform and framework choices while remaining hands-on in prototyping, critical implementation, and review.
• Success means Sandbox can demonstrate technically sound, economically defensible, and governance-ready prototypes with verified data, reproducible research, auditable decisions, explicit limitations, and clear readiness gates.

• Set the technical architecture, engineering roadmap, and design principles for Project Sandbox across quantitative research, agentic AI, portfolio construction, and risk.
• Lead multi-agent investment workflows spanning research agents, portfolio decision support, independent risk challenge, deterministic optimization, and live monitoring.
• Keep risk arithmetic, limits, exposures, and other verifiable quantities deterministic and authoritative; require automated reconciliation, audit trails, and human review for agent decisions.
• Define research and evaluation standards covering point-in-time data, walk-forward and out-of-sample testing, robustness, transaction costs, leakage, overfitting, LLM groundedness, and failure analysis.
• Provide technical leadership for portfolio and risk engineering, including factor exposures, beta and hedging, liquidity, VaR, constraints, optimization, attribution, and scenario analysis.
• Make and document architecture, model, framework, build-versus-buy, integration, cost, latency, reliability, security, and operating-model decisions.
• Partner across investment, product, data, engineering, architecture, Legal, Risk and Compliance, Global Investment Solutions, and AI governance; present evidence, risks, limitations, and readiness recommendations to senior leaders.
• Mentor engineers and data scientists, resolve technical impasses, lead code, design, and research reviews, and establish reusable standards and best practices.

• We expect the candidate to go beyond personal AI use and apply AI to improve system design and scale how teams work in practice.
• Approaches system design, architecture, or technical decisions by considering where AI can meaningfully improve the solution, not just the development process.
• Evaluates trade-offs between AI-assisted and traditional approaches with practical judgement.
• Identifies repetitive, manual, or error-prone processes in the team's work and introduces AI-powered improvements, including code reviews, AI-assisted testing, intelligent alerting, or document generation.
• Applies advanced prompting techniques to tackle ambiguous, multi-step, or domain-specific problems.
• Thinks through how AI-assisted solutions will be tested, deployed, monitored, and maintained.