Quantitative Research Engineer

2+ years experience

About Galacticflow Research

Galacticflow Research is an HFT firm trading digital assets across global markets. We research, develop, and operate our own trading strategies and technology.

A few things about us:

Independent by choice. No VCs on our cap table. We have reserved that space for our employees instead.
We are self-taught. We learn, experiment, and build from first principles.
We compete to win. We set a high bar, care deeply about performance, and constantly push ourselves to do better.
Trust over micromanagement. We trust people to do what is right, deliver results, and uphold their integrity.
Great work deserves great rewards. Top-tier compensation and generous performance-linked bonuses.

About the Role

At GalacticFlow Research, a proprietary high-frequency trading (HFT) firm, we develop and execute sophisticated trading strategies powered by cutting-edge technology and quantitative research. We are looking for a Quantitative Research Engineer to help design, implement, and optimize trading algorithms that operate at ultra-low latencies.

Responsibilities

Develop, test, and optimize quantitative trading strategies using real-time and historical market data.
Conduct data-driven research and statistical analysis to identify alpha signals and improve execution efficiency.
Implement low-latency and high-performance trading models in Python and C++
Collaborate with traders, quants, and engineers to deploy and enhance trading strategies in live markets.
Design and maintain market data pipelines for tick-data replay, backtesting, and analytics.
Optimize order execution by analyzing slippage, market impact, and order book dynamics.
Work on portfolio risk management, strategy PnL evaluation, and execution performance metrics.

Requirements

2+ years of experience in quantitative research, trading, or financial engineering.
Strong proficiency in Python and C++, with a focus on performance optimization.
Deep understanding of probability, statistics, and machine learning for financial modeling.
Experience with market microstructure, order book dynamics, and execution algorithms.
Familiarity with SQL and time-series databases for analyzing large datasets.
Strong problem-solving skills and algorithmic thinking, with the ability to work in a fast-paced, high-stakes environment.
Bachelor's, Master's, or PhD in a quantitative field (Computer Science, Mathematics, Statistics, Physics, or Engineering). Candidates with backgrounds from IITs are highly preferred.
Strong competitive coding profile on platforms like Codeforces or AtCoder is a plus.

Bonus