AI Scientist

Millennium•New York, NY

7d•$175,000 - $250,000•Onsite

About The Position

The Information Technology team is core to the health and growth of Millennium’s business. Supporting the firm’s active, multi-manager model, the team builds and advances flexible, scalable technology and proprietary systems that power next-generation analytical, data, and trading capabilities. Within this environment, the Data Science team applies modern AI and machine learning techniques to develop practical, high-impact solutions that help the business adapt, innovate, and operate at scale.

Requirements

3+ years of industry or applied research experience, along with an MS or PhD in Computer Science, Data Science, Statistics, Operations Research, or a related STEM field.
Strong foundation in machine learning, deep learning, natural language processing, statistical analysis, algorithms, and data structures.
Hands-on experience building and deploying AI and machine learning solutions, ideally including LLMs, generative AI, prompt engineering, RAG, fine-tuning, and agentic systems.
Strong Python programming skills and experience with frameworks such as PyTorch, TensorFlow, JAX, or scikit-learn.
Familiarity with LLM and AI application frameworks such as Hugging Face, LangChain, and LlamaIndex.
Experience working with at least one cloud platform, along with knowledge of vector databases, big data ecosystems, and MLOps or LLMOps tooling.
Exposure to AI-assisted coding tools such as Claude, Codex, GitHub Copilot, or Cursor.
Strong communication skills, sound judgment, and the ability to work independently, manage ambiguity, and learn quickly in a fast-moving environment.

Responsibilities

Build, test, and deploy production-grade AI and machine learning solutions using both classical methods and generative AI approaches, including LLMs, RAG, fine-tuning, prompt optimization, and agentic workflows.
Conduct applied research to evaluate new AI and machine learning techniques for complex business problems and identify practical opportunities for adoption.
Design and implement evaluation frameworks to measure model quality, robustness, reliability, and business impact.
Partner closely with product managers, data engineers, and business stakeholders to translate research findings and business needs into scalable, production-ready solutions.
Contribute to real-time and batch data pipeline workflows in collaboration with data engineering, and support monitoring, logging, metrics, guardrails, and human-in-the-loop review processes.
Communicate technical concepts, research outcomes, and recommendations clearly to both technical and non-technical audiences.
Stay current with emerging AI and machine learning developments and apply relevant advances thoughtfully to ongoing work.
Maintain high standards for code quality, documentation, and review practices to support reliability and long-term maintainability.