NVIDIA
Senior Developer Relations Lead, AI-Enabled Drug Discovery Science
$224K–$431K
US, CA, Santa Clara · Senior · Full-time · Posted Sep 18, 2026
- Experience
- 12+ years of research or drug-discovery experience in therapeutic discovery or platform biology.
- Education
- PhD in research within life sciences, therapeutic discovery, chemical biology, molecular pharmacology, computational biology, bioengineering, or a related field, or equivalent experience.
About the job
Leads and coordinates cross-functional scientific workstreams that connect RNA therapeutics, experimental data, computational modeling, automation, and translational science for AI-enabled drug discovery.
What you'll do
- Coordinate research priorities, execution plans, and program reporting across scientific workstreams.
- Lead work spanning data curation and analysis, machine learning, automation, and platform engineering; guide closed-loop experimentation and model updates.
- Review scientific validity and establish governance, quality thresholds, decision tracking, and risk management.
What they ask for
- PhD in a relevant life-sciences or related research field, or equivalent experience.
- 12+ years of research or drug-discovery experience in therapeutic discovery or platform biology.
- Deep understanding of therapeutic development and translational biology, with experience leading large interdisciplinary teams and matrixed programs.
- Proficiency with high-content and high-throughput data generation, assay quality control, data analysis, and model validation; ability to collaborate with AI and infrastructure teams.
Nice to have
- Experience in RNA biology and advancing RNA therapeutics from pre-clinical to clinical phases.
- Prior leadership of a discovery or data-rich experimental platform, or experience connecting experimental datasets to ML models, active learning, foundation models, or simulation.
Pay
224,000 USD - 356,500 USD base salary; 272,000 USD - 431,250 USD base salary
Tools
machine learning, AI, ML models, active learning, foundation models, simulation, BioNeMo, GPU-accelerated computational biology
AI focus
Leads AI-enabled drug discovery, including AI-prepared experimental data, machine-learning models, closed-loop experimentation, and collaboration on model requirements, evaluation, and compute planning.
Model-written summary · the original posting is authoritative