below marketmost similar roles pay hereabove market
This role pays more than
77%
of similar roles. Most pay
$160,875–$204,225
— the blue band above.
At the midpoint, this role pays about
$212k
versus about
$183k
for comparable roles.
Based on 240 similar postings.
Employer
About Franklin Templeton
Franklin Templeton is a global investment management organization offering a wide range of mutual funds, ETFs, and alternative investment solutions to retail and institutional investors worldwide. Industry: Investment Management & Asset Management
Franklin Templeton currently has
16 open roles
on FindRole.
Listed pay typically runs
$137,250–$174,500
across 16 roles with salary data.
JOB TITLE: Lead – AI Quant Engineer - Project Sandbox
The Lead – AI Quant Engineer joins Project Sandbox within the AI Platform and AI Investment Group. This role serves as the principal technical leader, accountable for architecture and evidence standards across quantitative research, agentic AI, portfolio construction, and risk engineering. You will lead the end-to-end design of multi-agent investment workflows, ensuring risk arithmetic and limits remain deterministic and authoritative. Daily responsibilities include prototyping, setting engineering roadmaps, and defining evaluation standards for LLM groundedness and transaction costs. You will utilize Python, SQL, numerical optimization libraries, and agent orchestration platforms within a cloud or hybrid environment. The role solves the challenge of turning high-value investment ideas into governed, auditable, and reproducible prototypes that integrate quantitative analytics with live monitoring and enterprise governance controls.
Set the technical architecture, engineering roadmap, and design principles for Project Sandbox across quantitative research and agentic AI.
Lead the end-to-end design and prototyping of multi-agent investment workflows and portfolio construction systems.
Ensure risk arithmetic, limits, and exposures remain deterministic through automated reconciliation and audit trails.
Define research and evaluation standards for data robustness, transaction costs, LLM groundedness, and failure analysis.
Provide technical leadership for portfolio and risk engineering, including factor exposures, liquidity, and scenario analysis.
Make and document critical decisions regarding architecture, model selection, build-versus-buy, and system security.
Mentor engineers and data scientists while leading code, design, and research reviews to establish best practices.
Present evidence, risks, and readiness recommendations to senior leaders across investment, legal, and risk departments.
What we're looking for
Master's or Doctoral degree in a quantitative discipline, Computer Science, Engineering, Machine Learning, or a related field (preferred).
8 or more years of experience across quantitative engineering, data science, machine learning, or software engineering.
At least three years of experience leading complex AI or quantitative systems.
4+ years of experience applying AI to improve system design and scale team workflows.
Expert-level Python skills and strong command of numerical methods, optimization, statistics, time-series analysis, portfolio construction, and risk analytics.
Experience building production LLM or agent-based systems, including tool orchestration, retrieval, evaluation, groundedness controls, and reliability engineering.
Strong architecture and distributed systems experience with a track record of moving research concepts into secure, scalable platforms.
Experience in asset management, quantitative equity, factor models, or investment-risk technology (preferred).
C++
Java
Python
kdb+/q
SQL
CUDA
OpenCL
Boost
QuantLib
NumPy
Pandas
Linux
Git
CI/CD
Jira
Monte Carlo Simulations
Stochastic Calculus
Distributed Grid Computing