About the Role
As an AI Researcher, you will join our research team and shift the paradigm of algo systematic trading — from manual strategy research to scalable, agent-driven research systems. Instead of directly building trading strategies, you will focus on encoding your expertise into autonomous agents that generate, test, and optimize strategies at scale. You will operate at the intersection of market microstructure, modeling, and system design, transforming research into a continuously learning pipeline.
- Formalize the research process for trading by defining hypothesis spaces, validation logic, and the full lifecycle from idea to evaluation
- Design and build agent-driven research systems that autonomously generate, test, and optimize trading strategies:
- Translate market microstructure intuition into machine-executable features, signals, and constraints that guide agent behavior
- Generate novel alpha hypotheses, evaluate alpha decay, turnover, capacity, and execution sensitivity.
- Combine individual signals into portfolio-level trading strategies.
- Continuously improve existing strategies through automated experimentation.
- Validate hypothesis using backtesting and simulation frameworks to support large-scale, autonomous experimentation under realistic execution conditions
- Optimize research scalability by increasing throughput of hypothesis generation, balancing exploration vs exploitation, and ensuring statistical robustness
- Collaborate with engineering to integrate agent-based research systems into production trading pipelines and continuously improve their performance
You’ll work at the frontier of real-world ML, with freedom to define problems, test ideas, and push them into live trading systems.
You might thrive in this role if you have
- 2+ years of experience in trading (MFT/HFT) with a clear understanding of how strategies are researched, validated, and deployed
- Motivation to shift from manual research to building and scaling agent-driven research systems
- Strong understanding of market microstructure, exchange mechanics, execution constraints and strategies
- Solid grounding in probability, statistics, and optimization, with the ability to apply them in noisy, real-world settings
- Strong programming skills in Python and ML frameworks, with the ability to write efficient, clean, and scalable code
- Ability to formalize and decompose the research process into structured, repeatable components suitable for automation
About the Team
We are building a proprietary hedge fund where the core advantage is not individual strategies, but the system that discovers them. Our goal is to rethink systematic trading by combining: autonomous research agents, large-scale experimentation, realistic simulation and execution, modern AI and learning systems
The fund operates in close integration with an internal AI research lab. Together, we design and own the full stack — from hypothesis generation to live trading.