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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.
Project Overview
Join our AI/LLM initiative, a pioneering project focused on integrating Large Language Models (LLMs) with WorldQuant's strategy management tools. As an intern, you will contribute to building a platform that offers AI-assisted insights and automates tasks for Portfolio Managers. This is an opportunity to work on foundational infrastructure, data pipelines, and user-facing AI applications.
Role Summary
We are looking for an enthusiastic Intern Software Engineer to support the AI/LLM initiative team. You will assist in various aspects of the software development lifecycle, including development, testing, documentation, and data analysis. A key aspect of this role will involve supporting the deployment of new tools and providing assistance to end-users, particularly Portfolio Managers, during the installation and initial usage phases. This role offers a unique opportunity to gain hands-on experience with Python, API development, financial data systems, tool deployment processes, and cutting-edge AI technologies within a leading quantitative investment firm.
Key Responsibilities
- Assist senior developers in the design, development, and testing of MCP server components and tools using Python.
- Contribute to the enhancement of existing tools, potentially including our internal command-line data access tools, by addressing smaller bugs or implementing specific features under guidance.
- Support the development of new MCPs by working on well-defined modules or tasks.
- Facilitate the deployment of newly developed tools and software packages to user environments.
- Collaborate with Portfolio Managers and other end-users to guide them through tool installation processes and provide initial user support.
- Participate in unit testing and integration testing efforts, helping to ensure software quality.
- Assist in data validation and reconciliation tasks for strategy metrics and features.
- Help create and update technical documentation for MCPs, related systems, and user guides.
- Support the investigation of data sources and assist in finalizing data specifications.
- Collaborate with the team on debugging and troubleshooting technical issues
- Gain exposure to financial data concepts (PnL, risk) and quantitative trading systems.
Core Skills
- Currently pursuing a Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field.
- Basic to intermediate proficiency in Python programming.
- Understanding of software development principles and methodologies.
- Familiarity with data structures and algorithms.
- Interest in finance, quantitative trading, or financial data.
- Basic understanding of APIs and data formats like JSON or CSV.
- Experience with Linux or a similar Unix-like operating system.
- Familiarity with version control systems (e.g., Git).
- Basic understanding of software deployment concepts.
- Strong analytical and problem-solving aptitude.
- Eagerness to learn and a proactive attitude.
- Excellent communication and interpersonal skills, with an ability to assist technical and non-technical users.
- Good teamwork skills.
- Attention to detail.
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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.