Portfolio Manager, Agentic Systems

Reposted Yesterday
Be an Early Applicant
4 Locations
In-Office
150K-150K Annually
Expert/Leader
Financial Services
The Role
Manage a live trading book while utilizing cutting-edge agentic AI solutions. Responsible for risk management, strategy development, and model building within a quantitative trading environment.
Summary Generated by Built In

WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a balanced, global investment platform.

WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement.

Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it.

We are seeking 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.

As a Portfolio Manager focused on Agentic Systems, you will manage a live trading book while working with cognitive reasoning architectures that enable autonomous systems to solve complex financial problems and reason through multi-step solutions. You will 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. You will 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.

  • Portfolio Management: Take risk, manage P&L, and make trading decisions within defined risk parameters while developing expertise in quantitative portfolio management principles
  • Agentic Systems Utilization: Deploy and work with cognitive reasoning systems for quantitative modeling problems, leveraging planning, tool use, memory, reflection, and collaboration capabilities
  • Reinforcement Learning Tuning: Adjust hyperparameters of reinforcement learning training processes to improve autonomous system performance and decision-making quality
  • Model Development: Contribute to deep learning model building for PM model layer applications specific to portfolio management objectives
  • Custom Agentic Development: Build and customize agentic workflows and tools tailored to portfolio management needs and specific trading strategies
  • Human-in-the-Loop Oversight: Execute human-in-the-loop decisions and checks ensuring that traded strategies meet quant trading acceptance criteria

This position combines portfolio management with cutting-edge agentic AI technology. Your work will directly impact:

  • Research-to-production cycles for quantitative strategies
  • Complex, multi-step financial workflows through autonomous systems
  • Enhanced decision-making through human-AI collaboration

This role offers the unique opportunity to develop as a portfolio manager while shaping the future of quantitative finance through the strategic utilization of agentic AI systems that solve complex financial problems and drive measurable business value.

What You’ll Bring:

  • 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

Pay Transparency:

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.





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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.

Skills Required

  • Advanced degree in a quantitative field (Computer Science, Mathematics, Physics, Statistics, Engineering, or related discipline)
  • Minimum of 10 years of experience
  • Familiarity with financial markets
  • Experience with python-based deep learning model development
  • Hands-on experience with agentic AI frameworks
  • Deep knowledge of planning, tool use, memory, reflection, and collaboration in agentic systems
  • Experience applying reinforcement learning methodologies
  • Ability to adjust hyperparameters and tune training processes for reinforcement learning systems

WorldQuant Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about WorldQuant and has not been reviewed or approved by WorldQuant.

  • Healthcare Strength Feedback suggests medical and dental coverage is fully paid for employees and dependents, indicating strong health benefits support. This breadth and cost coverage positions healthcare as a notable strength.
  • Leave & Time Off Breadth Feedback suggests paid time off is generous, with substantial vacation, personal days, and sick time described. The overall time‑off structure is portrayed as comprehensive.
  • Parental & Family Support Feedback suggests fully paid parental leave is provided. Family-oriented provisions complement core benefits to support caregiving needs.

WorldQuant Insights

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The Company
HQ: Old Greenwich, CT
2,008 Employees
Year Founded: 2007

What We Do

WorldQuant is a global quantitative asset management firm with over $7 billion in assets under management. Founded in 2007 by Igor Tulchinsky with the belief that talent is global, but opportunity is not, WorldQuant has more than 1,000 employees spread among 26 global offices. WorldQuant seeks to get to the future faster, guided by the principle that there are an infinite number of insights to discover. The firm develops and deploys investment strategies across a variety of asset classes in global markets. For more information on WorldQuant’s philosophy and culture, please visit www.worldquant.com.

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