This role pays more than
89%
of similar roles. Most pay
$169,050–$254,750
— the shaded band above.
At the midpoint, this role pays about
$290k
versus about
$212k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
867 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 849 roles with salary data.
Senior Developer Relations Lead, AI-Enabled Drug Discovery Science serves as a leader coordinating science streams where RNA therapeutics, experimental data generation, computer-based modeling, mechanization, and translational science converge. The role involves converting scientific therapeutic objectives into executable plans while leading workstreams across data curation, machine learning, automation, and platform engineering. You will manage the operating plan, oversee closed-loop learning for AI-generated hypotheses, and establish governance including decision logs and risk tracking. Key responsibilities include ensuring assay readouts are AI prepared and coordinating with infrastructure teams on model requirements and feature stores. The position requires expertise in high-content and high-throughput data generation, molecular pharmacology, and computational biology. Candidates must navigate the complexities of therapeutic development, target identification, and translating pre-clinical data into candidate decisions within a complex drug discovery landscape involving automated experiments and multiplexed readouts.
Convert scientific therapeutic objectives into executable operating plans across research priorities and program achievements.
Lead workstreams involving data curation, machine learning, automation, and platform engineering across various scales of biology.
Design assay and readout strategies to ensure experimental data is optimized for AI preparation and translation.
Guide closed-loop learning cycles between AI-generated hypotheses, automated experiments, and model updates.
Establish science-stream governance including decision logs, risk tracking, quality thresholds, and resolution paths.
Report scientific progress, critical decisions, and resource needs to program leadership.
Collaborate with AI and infrastructure teams on model requirements, data lineage, and compute planning.
What we're looking for
PhD in life sciences, therapeutic discovery, chemical biology, molecular pharmacology, computational biology, bioengineering, or a related field (or equivalent experience).
12+ years of research or drug-discovery experience in therapeutic discovery and platform biology.
Deep understanding of therapeutic development and translational biology including target identification, efficacy/selectivity tradeoffs, safety considerations, and data quality.
Experience leading large interdisciplinary teams combining experimental science, computational modeling, automation, assay development, and data platforms.
Proven track record leading in matrixed environments with shared scientific direction, program priorities, and execution accountability.
Proficiency with high-content and high-throughput data generation, including molecular, cellular, and functional readouts and model validation.
Ability to collaborate with AI and infrastructure teams on model requirements, feature stores, data lineage, and closed-loop experimentation.
Experience in RNA biology, therapeutic discovery platforms, or connecting experimental datasets to ML models (preferred).
NVIDIA
CUDA
TensorRT-LLM
Triton Inference Server
NVIDIA NeMo
NIM
GPU Acceleration
LLMs
Agent Frameworks
Multimodal AI
Distributed Systems
Machine Learning Infrastructure
Model Optimization
Inference Scaling
RAPIDS
Deep Learning
AI
Robotics
Autonomous Systems
CUDA
NeMo
Nemotron
Isaac Sim
Isaac GROOT
Alpamayo
NVIDIA Software Stack
SDKs
Architectural Design
Code Review