Quantitative Research Intern

About the role

Qsentia.com is developing a modern hedge fund technology platform that combines reinforcement learning and large language models to support an advanced portfolio management system. The company applies quantitative research and scalable AI techniques to live financial markets, with a focus on improving risk-adjusted performance, especially when markets are under pressure or highly volatile. This role places you in a highly technical environment where finance, machine learning, and software engineering intersect, giving early-career candidates hands-on exposure to advanced tools, research workflows, and production-grade systems used in quantitative finance.

What you will do

In this remote Quantitative Developer I internship, you will contribute to the creation, implementation, and validation of quantitative models and trading strategies, including approaches based on reinforcement learning and large language models. Your work will include building research prototypes in code, preparing and transforming financial datasets, and conducting simulations and backtests to measure strategy performance. You will also assist in bringing models into production, tracking their outputs, and reviewing performance indicators. The role involves close collaboration with quantitative researchers, data scientists, and engineers, along with participation in code reviews and the documentation of methods and findings.

What we are looking for

We are seeking candidates with a strong base in mathematics and statistics who can apply quantitative concepts to practical financial data. A solid understanding of quantitative finance and analytics is important, including exposure to portfolio theory, factor models, or risk modelling. Interest in trading and systematic markets, such as market microstructure, order execution, and strategy design, is also valuable. You should be comfortable programming in at least one commonly used language for quantitative work, such as Python, C++, or Julia, and familiar with numerical or scientific computing libraries. Experience working with data pipelines, databases, and version control tools like Git in team-based development settings will be useful. Applicants should be currently pursuing or have recently completed a degree in Mathematics, Statistics, Computer Science, Physics, Engineering, Finance, or a related discipline. Strong analytical ability, careful attention to detail, and clear writing skills are needed for sharing model logic and results. Interest in machine learning or reinforcement learning for finance is a strong advantage, and prior project or research exposure in these areas is a plus.

Additional information

This position is remote and part-time. No stipend or salary amount was provided. The vacancy count, start date, and application deadline were not specified.