Lead Software Scientist Interactive AI Systems in Materials Science

Position: Lead Software Scientist for Interactive AI Systems in Materials Science
Location: New York

Lead Software Scientist for Interactive AI Systems in Materials Science The Role

Cornell Research & Innovation seeks a highly experienced researcher-engineer to conceive, lead, and build an interactive, LLM-based AI system for materials science research. This is a unique position that sits at the intersection of artificial intelligence, materials science, human-computer interaction, and research leadership.

Unlike a traditional software engineering role, this position requires deep scientific engagement, technical leadership, and hands‑on system building, from early design through a fully usable, deployed research tool. The successful candidate will embed directly within materials science research environments to ensure that the resulting system is scientifically powerful, intuitive to use, and tightly aligned with real research workflows.

This position will serve as a key contributor to the U.S. NSF‑sponsored Artificial Intelligence Materials Institute (AI‑MI). AI‑MI will accelerate and transform the discovery of new materials to be used in sustainable energy, advanced electronics, environmental stewardship and quantum technologies by integrating human scientific expertise with AI methods. Researchers from Cornell University make up the leadership team, joined by researchers from Princeton University, the City University of New York, and Boston University.

The goal of NSF AI‑MI is to harness the rising tide of materials data, using AI to enable scientists to develop new materials based on prediction, while also developing trustworthy AI and deepening our fundamental understanding of AI.

Core Responsibilities Scientific AI System Design and Leadership

• Lead the end‑to‑end design and development of an interactive, LLM‑based AI system tailored to materials science research.
• Define the system's technical and scientific vision, balancing cutting‑edge AI methods with usability, robustness, and long‑term sustainability.
• Make architectural decisions spanning model integration, data representations, interaction paradigms, and deployment strategies.
• Translate open‑ended scientific goals into concrete system requirements and deliverables.

Integration of Research into a Production‑Quality System

• Work closely with PhD students and postdoctoral researchers in materials science, physics, and computer science to incorporate latest research results—including new models, representations, and scientific insights—into the evolving system.
• Bridge the gap between research prototypes and a cohesive, production‑quality platform, ensuring reliability, reproducibility, and extensibility.
• Evaluate when and how new research ideas should be integrated, refined, or redesigned to meet real‑world research needs.

Human‑Centered Design for Scientific Workflows

• Embed within a materials science research lab to observe, understand, and analyze how researchers actually work—including how they explore data, generate hypotheses, run experiments, and interpret results.
• Lead the design of interaction models, interfaces, and workflows that align with these practices.
• Ensure the system is usable, discoverable, and adoptable by materials scientists—not just technically impressive.
• Continuously assess and refine the system based on researcher feedback, usage patterns, and evolving scientific practices.

Collaboration and Mentorship

• Serve as a technical and scientific leader for interdisciplinary teams of PhD students and postdocs.
• Coordinate contributions across AI, materials science, and physics researchers, aligning individual research efforts with the system's broader goals.
• Mentor junior researchers on system design, scientific software development, and translating research ideas into usable tools.
• Foster a collaborative environment that values both scientific innovation and practical impact.

Project and Research Leadership

• Lead complex, multi‑year projects involving multiple stakeholders, disciplines, and evolving research directions.
• Set milestones, prioritize work, and manage technical risk in a research‑driven environment.
• Communicate progress and design decisions clearly to both technical and non‑technical audiences.
• Contribute to long‑term strategy…