About Millennium

Millennium is a global, diversified alternative investment firm, founded in 1989. Defined by evolution, innovation and focus, Millenniums mission is to deliver results for our investors.

Our people are empowered with both independence and support: the autonomy to pursue ideas with conviction and the backing of a global network committed to collaboration, disciplined risk management and continuous learning. With opportunities to deepen expertise and accelerate development, talent at Millennium is equipped to adapt, evolve and build lasting impact over time. Discover how transformative growth accelerates impact.

Meet the Team

The Volatility Alpha Development team is the core quantitative and strategy group supporting Millenniums global volatility business. The team builds and maintains systematic options datasets, backtesting infrastructure, event-volatility models, and live systematic volatility-fitting frameworks that directly support portfolio managers across global volatility strategies.

What You'll Do

Research, develop, and productionize AI/ML models for volatility forecasting, options pricing, signal generation, event analysis, and systematic hypothesis testing.

Build high-performance quantitative research workflows using JAX, PyTorch, and hardware acceleration.

Design agentic research and operational workflows using LangGraph, with evaluation, tracing, and observability through LangSmith.

Partner with researchers, engineers, and portfolio managers to integrate AI-assisted research, paper-trading, and decision-support tools into investment workflows.

Apply LLM application-development techniques, including prompting, tool use, structured outputs, embeddings, vector databases, and model evaluation.

Support production ML practices, including experiment tracking, feature and data versioning, model monitoring, drift detection, and reproducibility.

What You Bring

Bachelors, Masters, or PhD degree in computer science, engineering, mathematics, physics, or a related quantitative field.

At least three years of experience in a quantitative, engineering, or data-driven financial-services environment.

Strong Python programming skills; experience with C++, Java, Rust, Go, or C# is a plus.

Hands-on experience with Kubernetes, Docker, Airflow, and CI/CD practices.

Working knowledge of financial markets, particularly options and derivatives.

Excellent problem-solving, communication, and cross-functional collaboration skills.