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.
Signal research and construction. Develop, test, and productionize predictive signals across asset classes using a combination of statistical methods, machine learning, and AI agent–driven research workflows. Take ideas from hypothesis through backtest, validation, and deployment.
Root cause analysis (RCA). Investigate model behavior, signal decay, PnL attribution, and unexpected trading outcomes. Build tools — including agentic ones — that accelerate diagnosis and shorten the loop between observation and fix.
Market microstructure research. Study order book dynamics, execution costs, liquidity, and venue behavior to inform both signal design and execution strategy.
AI agent infrastructure for research. Help design and extend internal agentic systems that automate parts of the research pipeline — data exploration, hypothesis generation, backtest configuration, results summarization, and report drafting.
Collaborate broadly. Work closely with traders, engineers, and other researchers to turn ideas into live, monitored strategies.
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.