Overview
We are looking for a Quantitative AI Researcher to design and develop AI-driven systems and agentic workflows that support trading and research.
This role focuses on building intelligent systems that extend beyond static models — integrating machine learning, automation, and decision-support agents to operate in complex, data-rich environments. You will work across financial, event-driven, and probability-based markets, where data is noisy, outcomes are uncertain, and adaptability is critical.
The objective is to develop AI systems that enhance decision-making, accelerate research, and improve trading performance.
What You’ll Do
Design and build agentic AI systems to support research, analysis, and trading workflows
Develop and refine machine learning models for prediction, classification, and probabilistic estimation
Integrate AI agents with data pipelines, models, and execution systems
Work with structured and unstructured data across multiple markets
Develop tools that automate data analysis, signal generation, and decision support
Evaluate system and model performance, iterating based on real-world outcomes
Collaborate with traders and engineers to deploy systems in live environments
What We’re Looking For
Strong foundation in machine learning, AI systems, and statistical modelling
Experience building applied ML models and working with real-world data
Familiarity with LLMs, agent frameworks, or autonomous systems is beneficial
Proficiency in Python and relevant AI/ML tooling
Ability to think in terms of systems, workflows, and end-to-end solutions
Strong problem-solving skills and attention to detail
We value individuals who can build intelligent systems that operate effectively under uncertainty, not just isolated models.
What You’ll Gain
Experience developing AI systems applied to real trading and research problems
Exposure to environments where automation, data, and decision-making intersect
The opportunity to build systems that directly influence strategy and performance
A high level of ownership and autonomy in both research and implementation
Environment
Small, focused team
Close collaboration across trading, research, and engineering
Emphasis on practical outcomes and system performance
Performance-driven, but collaborative
