AI/ML Quant Scientist – Volatility & Options

Millennium; Hong Kong; On-site; HKD 700,000 - 1,100,000; Full time

Job summary

Millennium in Hong Kong seeks a quantitative software engineer to research, develop, and productionize AI/ML models for volatility forecasting and options pricing. You will build high‑performance research workflows using Python (JAX, PyTorch) and integrate with LangGraph and LangSmith in a collaborative team.

The role requires a quantitative degree and 3+ years in finance or data science, strong Python skills, and knowledge of Kubernetes, Docker, Airflow, and CI/CD.

Qualifications

• Bachelor’s/Master’s/PhD in a quantitative field (CS, engineering, math, physics).
• 3+ years in a quantitative/financial-services environment.
• Strong Python; knowledge of C++, Java, Rust, Go, or C# is a plus.
• Hands-on experience with Kubernetes, Docker, Airflow, and CI/CD practices.

Responsibilities

• Research, develop, and productionize AI/ML models for volatility forecasting and options pricing.
• 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 and tools into investment workflows.
• Apply LLM techniques, including prompting, tool use, embeddings, vector databases, and model evaluation.
• Support production ML practices, including experiment tracking, feature/data versioning, and drift detection.

Skills

Python

Quant Finance

Communication

Teamwork

Education

Quantitative degree

Tools

Kubernetes

Docker

Airflow

CI/CD

Job description

Millennium in Hong Kong seeks a quantitative software engineer to research, develop, and productionize AI/ML models for volatility forecasting and options pricing. You will build high‑performance research workflows using Python (JAX, PyTorch) and integrate with LangGraph and LangSmith in a collaborative team.

The role requires a quantitative degree and 3+ years in finance or data science, strong Python skills, and knowledge of Kubernetes, Docker, Airflow, and CI/CD.