Portfolio Manager, Agentic Systems

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 WQHR_US@WorldQuant.com, and we will address your concerns promptly. #LI-JB1 By submitting this application, you acknowledge and consent to terms of the WorldQuant Privacy Policy. 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