Quantitative Developer I internship

Job Description

Quantitative Research Intern ll | Qsentia.com

The Tone:
This is an internship at Qsentia.com, located remotely. Qsentia.com is building a next-generation hedge fund platform that integrates reinforcement learning with large language models to power a state-of-the-art portfolio management system. This role contributes to applying advanced quantitative research and scalable AI to real-world financial markets. It offers early-career talent exposure to cutting-edge tools, research, and production-scale systems in quantitative finance.

The TL;DR
• Role: Internship
• Location: Remote

• Mission: Support the design, implementation, and testing of quantitative models and trading strategies, including those using reinforcement learning and large language models.
• Tech Stack: Python, C++, Julia, Git, numerical/scientific computing libraries, databases

What You’ll Actually Do
• Design: Support the design, implementation, and testing of quantitative models and trading strategies.
• Develop: Code research prototypes and help integrate models into production systems.
• Analyze: Clean and transform financial datasets, run simulations or backtests, and analyze strategy performance metrics.
• Monitor: Monitor the behavior of integrated models within production systems.
• Collaborate: Participate in code reviews, document methods and results, and collaborate with quantitative researchers, data scientists, and engineers.

The Must-Haves
• Background: Currently pursuing or recently completed a degree in Mathematics, Statistics, Computer Science, Physics, Engineering, or Finance, with a strong foundation in quantitative disciplines.
• Experience: Knowledge of Quantitative Finance and Quantitative Analytics, including familiarity with portfolio theory, factor models, or risk modeling; comfort working with data pipelines, databases, and version control tools (e.g., Git).
• Skills: Proficiency in at least one programming language commonly used in quantitative research (e.g., Python, C++, or Julia) and experience with numerical or scientific computing libraries; strong analytical thinking, attention to detail, and clear written communication skills.
• Bonus: Interest in machine learning or reinforcement learning methods applied to finance; prior project or research experience in these areas.