We are building out a new research function at the intersection of artificial intelligence and quantitative trading to improve the efficiency of execution algo models and more, and we are looking for a Junior Quantitative Researcher to be a founding member of this effort. You will work alongside senior quants, engineers, and traders to design AI-driven workflows that generate alpha signals, diagnose model and PnL behavior, and deepen our understanding of market microstructure.

This is a high-ownership role suited to someone who is genuinely excited about markets, has a strong research background, and is already building with modern AI tooling — including LLM-based agents. We are open to hiring at the fresh-PhD level, provided you can demonstrate research depth and a real interest in trading.

• PhD (recently completed or near completion) in a quantitative field — e.g., Computer Science, Machine Learning, Statistics, Physics, Mathematics, Electrical Engineering, Operations Research, or a related discipline.

• Strong programming skills in Python; comfortable with the modern data and ML stack (NumPy, pandas, PyTorch or JAX, etc.).
• Hands-on experience building with AI agents and LLM-based systems — for example, tool-using agents, multi-step reasoning pipelines, retrieval systems, or evaluation frameworks. We want to see that you have actually built things, not just read papers.
• Solid grounding in statistics, probability, and machine learning, with the rigor to know when a result is real and when it isn't.
• Genuine interest in financial markets and trading, demonstrable through coursework, personal projects, competitions, internships, or self-directed study.

• Strong written and verbal communication; able to explain technical work clearly to a mixed audience.

• Prior internship or research experience at a hedge fund, prop trading firm, market maker, bank, or fintech.

• Exposure to market microstructure, limit order books, or high-frequency data.
• Experience with backtesting frameworks, time-series analysis, or causal inference.
• Familiarity with low-latency systems, or large-scale data infrastructure.

• Publications, open-source contributions, or trading competition results.