WorldQuant

Software Engineer Intern, AI/LLM Initiative

Singapore

Not SpecifiedCompensation
InternshipExperience Level
Full TimeJob Type
UnknownVisa
Financial Services, BiotechnologyIndustries

Intern Software Engineer - AI/LLM Initiative

Company Overview

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.

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.

Qualifications

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.

Domain-Specific Knowledge (Beneficial but not required, strong willingness to learn is key)

  • Interest in finance, quantitative trading, or financial data.
  • Basic understanding of APIs and data formats like JSON or CSV.

Technical Environment Familiarity (Helpful)

  • Experience with Linux or a similar Unix-like operating system.
  • Familiarity with version control systems (e.g., Git).

Employment Type:

  • [Employment Type not specified]

Location Type:

  • [Location Type not specified]

Salary:

  • [Salary not specified]

Skills

Software Development
Testing
Documentation
Data Analysis
AI
LLM
Python
Data Pipelines
Infrastructure

WorldQuant

Quantitative asset management using algorithms

About WorldQuant

WorldQuant is a quantitative asset management firm that focuses on managing investments for institutional clients like pension funds and sovereign wealth funds. The firm uses data and predictive algorithms to analyze financial markets and identify investment opportunities. Its approach involves algorithmic trading, where mathematical models guide investment decisions. Unlike many competitors, WorldQuant encourages a culture of experimentation and innovation among its employees, allowing everyone to contribute ideas regardless of their position. The company's goal is to generate returns for its clients while maintaining a commitment to equal opportunity in the workplace.

Greenwich, ConnecticutHeadquarters
2007Year Founded
$148.5MTotal Funding
N/ACompany Stage
Quantitative Finance, Financial ServicesIndustries
1,001-5,000Employees

Benefits

Performance Bonus
Flexible Work Hours

Risks

Increased competition from AI-driven investment firms like ADIA.
Regulatory scrutiny on algorithmic trading practices is increasing globally.
Market volatility challenges the performance of algorithmic trading models.

Differentiation

WorldQuant employs over 1,000 professionals across 27 global offices.
The firm uses predictive algorithms to manage assets and generate client returns.
WorldQuant emphasizes equal opportunity, allowing all employees to contribute meaningfully.

Upsides

Increased focus on alternative data sources is gaining traction in quantitative finance.
Machine learning integration in portfolio management allows better market trend predictions.
Quantum computing offers potential for faster, complex calculations in algorithmic trading.

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