The Role
As a Quantitative Researcher / Quant Engineer (AI-Focused), you will design, test, and deploy AI-driven quantitative models that enable our agents to predict about markets, manage risk, and optimize outcomes.
You’ll work at the frontier of AI x Finance, bridging statistical modeling, agentic reasoning, and data engineering to create adaptive, data-informed intelligence.
Key Responsibilities
• Quantitative Modeling & Research
• Develop quantitative models for financial forecasting, strategy optimization, and portfolio analytics.
• Incorporate LLM-based reasoning into quantitative pipelines to augment decision-making and contextual understanding.
• Research market microstructure, trading patterns, and fund flow dynamics relevant to agent-driven financial systems.
• Design and backtest AI-augmented trading or fundraising strategies using real and synthetic data.
• AI Integration
• Integrate LLMs and autonomous agents with quantitative models for dynamic hypothesis generation and adaptive strategy refinement.
• Use multi-agent frameworks (e.g., LangGraph, CrewAI, AutoGen) to simulate collaborative decision-making between quant agents.
• Explore reinforcement learning and LLM-based policy learning for optimization under uncertainty.
• Data Engineering & Infrastructure
• Build scalable data pipelines for market, sentiment, and alternative datasets using Python, PySpark, or SQL.
• Deploy models in cloud-native environments (AWS, GCP) using Docker/Kubernetes.
• Evaluation & Governance
• Establish robust evaluation frameworks for agent-driven trading, forecasting, and decision systems.
• Monitor model performance, bias, and explainability, ensuring alignment with regulatory and ethical standards.
• Collaborate with AI and compliance teams to design transparent, auditable quant processes.
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Qualifications
• 5+ years in quantitative research, trading, or applied data science.
• Deep understanding of statistical modeling, time-series analysis, and optimization techniques.
• Proficiency in Python (NumPy, Pandas, PyTorch, or TensorFlow); experience with TypeScript is a plus.
• Familiarity with LLM frameworks (LangChain, AutoGen, CrewAI) or AI-driven agentic systems.
• Strong background in mathematical reasoning, probability theory, and stochastic processes.
• Ability to translate research insights into production-grade systems.
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Preferred Qualifications
• Advanced degree (MS/PhD) in Applied Mathematics, Computer Science, Statistics, or Quantitative Finance.
• Experience with reinforcement learning, meta-learning, or agentic decision frameworks.
• Background in crypto markets, DeFi, or alternative asset analytics.
• Contributions to open-source AI or quant research projects.
• Experience integrating AI models into live financial systems or strategy engines.
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