Portfolio Manager, Agentic Systems

Portfolio Manager

WorldQuant seeks a Portfolio Manager to manage risk and generate returns while utilizing cutting-edge agentic AI solutions within our Quantitative Trading divisions. This role sits at the intersection of Portfolio Management and Artificial Intelligence, requiring active engagement with autonomous cognitive systems for strategy development.

Responsibilities

• Take risk, manage P&L, and make trading decisions within defined risk parameters while developing expertise in quantitative portfolio management principles
• Deploy and work with cognitive reasoning systems for quantitative modeling problems, leveraging planning, tool use, memory, reflection, and collaboration capabilities
• Utilize and interact with agentic systems including planning algorithms, memory architectures, reflection mechanisms, and collaborative reasoning patterns that support autonomous decision-making in quantitative trading environments
• Adjust hyperparameters of reinforcement learning training processes to optimize system performance and contribute to deep learning model development for the PM model layer and custom agentic workflows
• Contribute to deep learning model building for PM model layer applications specific to portfolio management objectives
• Build and customize agentic workflows and tools tailored to portfolio management needs and specific trading strategies
• Execute human-in-the-loop decisions and checks ensuring that traded strategies meet quant trading acceptance criteria

Requirements

• Advanced degree in a quantitative field (Computer Science, Mathematics, Physics, Statistics, Engineering, or related discipline)
• Minimum of 10 years of experience, PM experience is not required but preferred
• Familiarity with financial markets
• Experience with Python-based deep learning model development
• Willingness to learn portfolio management discipline, including P&L responsibility and risk management
• Hands-on experience with agentic AI frameworks
• Deep knowledge of the core capabilities of agentic systems: planning, tool use, memory, reflection, and collaboration
• Experience applying reinforcement learning methodologies to develop autonomous systems that learn and improve through policy optimization, reward modeling, and outcome-based feedback loops
• Ability to adjust hyperparameters and tune training processes for reinforcement learning systems

Qualifications

• Excellent degree in a quantitative field
• Minimum of 10 years of experience, PM experience is not required but preferred
• Familiarity with financial markets
• Experience with Python-based deep learning model development
• Willingness to learn portfolio management discipline, including P&L responsibility and risk management
• Hands-on experience with agentic AI frameworks
• Deep knowledge of the core capabilities of agentic systems: planning, tool use, memory, reflection, and collaboration
• Experience applying reinforcement learning methodologies to develop autonomous systems that learn and improve through policy optimization, reward modeling, and outcome-based feedback loops
• Ability to adjust hyperparameters and tune training processes for reinforcement learning systems

Skills

• Advanced degree in a quantitative field
• Minimum of 10 years of experience, PM experience is not required but preferred
• Familiarity with financial markets
• Experience with Python-based deep learning model development
• Willingness to learn portfolio management discipline, including P&L responsibility and risk management
• Hands-on experience with agentic AI frameworks
• Deep knowledge of the core capabilities of agentic systems: planning, tool use, memory, reflection, and collaboration
• Experience applying reinforcement learning methodologies to develop autonomous systems that learn and improve through policy optimization, reward modeling, and outcome-based feedback loops
• Ability to adjust hyperparameters and tune training processes for reinforcement learning systems

Benefits

WorldQuant is a total compensation organization where you will be eligible for a base salary, discretionary performance bonus, and benefits. To provide greater transparency to candidates, we share base pay ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on job function and level, benchmarked against similar stage organizations. When finalizing an offer, we will take into consideration an individual’s experience level and the qualifications they bring to the role to formulate a competitive total compensation package. The Base Pay Range For This Position Is 150,000 USD. At WorldQuant, we are committed to providing candidates with all necessary information in compliance with pay transparency laws. If you believe any required details are missing from this job posting, please notify us at [email protected], and we will address your concerns promptly. By submitting this application, you acknowledge and consent to terms of the WorldQuant Privacy Policy. The privacy policy offers an explanation of how and why your data will be collected, how it will be used and disclosed, how it will be retained and secured, and what legal rights are associated with that data (including the rights of access, correction, and deletion). The policy also describes legal and contractual limitations on these rights. The specific rights and obligations of individuals living and working in different areas may vary by jurisdiction. Copyright © 2025 WorldQuant, LLC. All Rights Reserved. WorldQuant is an equal opportunity employer and does not discriminate in hiring on the basis of race, color, creed, religion, sex, sexual orientation or preference, age, marital status, citizenship, national origin, disability, military status, genetic predisposition or carrier status, or any other protected characteristic as established by applicable law.