Key Skills

Job Description

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. What'll you do 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. 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. Requirements 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

Core Responsibilities

Formalize the trading research lifecycle and build autonomous agent-driven systems to generate, test, and optimize strategies. Develop machine-executable signals and constraints, evaluate alpha and execution characteristics, combine signals into portfolio strategies, and integrate the systems into production trading pipelines.

Requirements

Requires at least two years of MFT/HFT trading experience and a strong understanding of strategy research, validation, deployment, market microstructure, exchange mechanics, and execution constraints. Candidates should have strong Python and machine-learning programming skills, a grounding in probability, statistics, and optimization, and the ability to structure research processes for automation.