Job Purpose
As a Senior Machine Learning Engineer in NYSE’s Data Science, ML, and AI team, you will design, build, and deploy machine learning systems to power intelligent decision-making across the exchange. You will work on a diverse set of problems – from structured prediction and anomaly detection to retrieval-augmented generation – using both classical and modern ML approaches. You will be expected to leverage AI-assisted development tools (e.g. ChatGPT, GitHub Copilot) to accelerate iteration, improve reproducibility, and enhance team productivity.
Responsibilities
- Develop and deploy ML models for structured and unstructured data, including supervised, unsupervised, and generative tasks
- Build production-grade pipelines for model training, evaluation, and deployment, integrating with NYSE's data infrastructure
- Design and operationalize RAG pipelines using LangChain and vector databases where appropriate
- Build and optimize ML microservices using FastAPI, Docker, and Kubernetes; ensure observability, scalability, and cost-efficiency
- Track and manage ML experiments and artifacts using tools such as MLflow
- Define, monitor, and improve performance metrics for deployed ML systems (e.g. accuracy, latency, coverage, user satisfaction)
- Use AI assistants for code generation, testing, and documentation
- Collaborate with stakeholders across engineering, data, and product teams to align technical efforts with business goals
- Stay current with research and industry trends in machine learning and AI, and evaluate applicability to NYSE use cases
Knowledge and Experience
- M.S. or Ph.D. in ML, AI, or related field
- 5+ years of experience in software engineering, including 3+ years building and deploying machine learning models
- Proficiency in Python, including experience with FastAPI and asynchronous programming
- Strong understanding of ML fundamentals (e.g. model evaluation, feature engineering, regularization, bias/variance tradeoffs)
- Experience with modern ML libraries (e.g. scikit-learn, XGBoost, PyTorch, TensorFlow) and model lifecycle tools (e.g. MLflow)
- Familiarity with GenAI/NLP stacks including Hugging Face, OpenAI APIs, and LangChain
- Experience with vector databases (e.g. Pinecone, FAISS) and unstructured data pipelines
- Comfort using GenAI tools (e.g. ChatGPT, GitHub Copilot) to enhance development productivity
- Strong communication skills, with the ability to explain technical concepts to diverse audiences
Preferred Knowledge and Experience
- Exposure to financial market data and its unique properties (e.g. order books, time-series behavior, microstructure)
- Peer-reviewed publications in related fields
