Overview In this role you drive interdisciplinary science programs at NVIDIA, coordinating experimental science with automation, AI modeling, and data infrastructure. You will translate research objectives into executable experimental plans, lead cross-functional teams, and shape a closed-loop system linking experimentation with analytics and AI-guided decision-making. Your work accelerates discovery in synthetic chemistry, catalysis, and process chemistry while maintaining scientific rigor. This position offers a meaningful chance to influence large-scale experimental campaigns within a leading technology company.
Compensation / Benefits- equity
- benefits
- base salary range
- inclusive work environment
- career development opportunities
- flexible work arrangements
Responsibilities- Coordinate research priorities, technical execution, and program milestones
- Translate objectives into experimental priorities, parameter development, and success criteria
- Lead initiatives across synthetic chemistry, catalysis, process chemistry, ML, automation, and data processing
- Create standardized experimental traces with outcomes, metadata, QC signals, and summaries
- Guide AI systems for feasibility, outcome prediction, optimization, and campaign orchestration
- Integrate automated experimentation, analytics, data curation, model retraining, and campaign decisions into a closed-loop model
- Establish governance with reviews, logs, risk tracking, quality thresholds, and issue resolution paths
- Prepare regular workstream readouts for leadership detailing progress, decisions, dependencies, resources, and risks
Key requirements- PhD (or equivalent) with 10+ years hands-on experience in experimental science, chemical engineering, material science, robotics, automation, analytical science, platform development, AI-enabled science workflows, or equivalent
- Deep expertise in experimental optimization and principled reasoning including parameter selection, scope, 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 across experimental science, automation, ML, data platforms, analytical science, software engineering, and partner execution
- Ability to operate in matrixed partner governance and translate scientific direction into technical, leadership-ready decisions
- Cross-functional collaboration
- Strategic thinking and planning
- Strong communication and stakeholder management
- Automated experimental platforms
- Analytical data streams and quality control
- Data infrastructure and platform development
