About the role

What you’ll do

• Coordinate science streams across experimental science, AI modeling, laboratory automation, and data infrastructure
• Translate research priorities into experimental campaigns
• Guide AI-enabled optimization
• Integrate automated experimentation and data workflows
• Provide program leadership with progress, decisions, dependencies, and risks

What you’ll need

• PhD or equivalent experience with 10+ years of hands-on experience in experimental science, chemical engineering, material science, robotics, automation, analytical science, platform development, or AI-enabled science workflows
• Deep expertise in experimental optimization and reasoning based on underlying principles, including parameter selection, scope, conditions, tradeoffs, failure modes, and transferability
• Proven understanding of automated experimental platforms, analytical readouts, metadata, quality control, and detailed unsuccessful-data capture
• Experience leading large interdisciplinary teams and operating in matrixed partner governance, translating scientific direction into technical execution
• Experience owning an experimental optimization platform, automated lab effort, AI-enabled science initiative, or high-impact applied research program and building datasets with unsuccessful outcomes and standardized metadata
• Applied optimization methods, predictive modeling, analytical data analysis, or active learning to real-world scientific campaigns; familiarity with GPU-accelerated scientific computing, simulation, AI agents, or large-scale data infrastructure

Original job description