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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.
About WorldQuant BRAIN:
BRAIN is a group within WorldQuant similar to a traditional B2C fintech setup. Our mission is to simplify quant finance and provide global remote-work opportunities to participants, while they learn quant finance, AI and ML concepts. WorldQuant Brain provides an advanced crowdsourcing platform for external participants to contribute signals, data, and more.
The Role: WorldQuant is seeking exceptional individuals to join as AI Researchers. In this full-time role, you will work in of Artificial Intelligence (AI) and Large Language Models (LLMs), applying techniques to develop quantitative models for WorldQuant’s BRAIN platform.
As part of this role, you will:
- Conduct research in AI and LLMs, experimenting with advanced architectures such as Transformer models, Reinforcement Learning, and Generative AI.
- Design, train, and fine-tune LLMs and other AI models to extract insights, and generate signals
- Apply AI concepts to create and enhance alphas on the BRAIN platform.
- Analyze large-scale financial datasets to seek to uncover patterns and build models using statistical, mathematical, and machine learning techniques.
- Collaborate with platform development teams to design and test new functionalities and datasets.
- Stay updated on the latest advancements in AI and LLM research, identifying opportunities to integrate them into quantitative finance applications.
What You’ll Bring:
- Technical Expertise: Strong knowledge and hands-on experience in AI, Machine Learning (ML), and LLMs; proficiency in Python and C++; familiarity with PyTorch or TensorFlow.
- Quantitative Skills: Solid foundation in mathematics, statistics, and quantitative modeling; experience with financial datasets is a plus.
- Educational Background: Bachelor’s or advanced degree in Computer Science, AI, Mathematics, Financial Engineering, or related fields from a leading university.
- Research Mindset: Creative problem solver with a passion for experimentation and unsolved challenges; strong interest in financial markets.
- Collaborative Skills: Ability to work in a team-oriented culture; excellent communication and presentation skills in English.
[1] WorldQuant defines alphas as mathematical models that seek to predict the future price movements of various financial instruments
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.
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