Senior Developer Relations Lead, AI-Enabled Drug Discovery Science
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What you'll do
- Coordinate the operating plan across research priorities, execution, and program achievements.
- Convert scientific therapeutic objectives into executable plans.
- Lead NVIDIA workstreams spanning data curation & analysis, machine learning, automation, and platform engineering across scales of biology.
- Collaborate on assay and readout strategies so efficacy, selectivity, adaptability, translatability, and safety data are AI prepared.
- Guide closed-loop learning across AI-generated hypotheses, automated experiments, multiplexed readouts, model updates, and science decisions.
- Review science validity for key biology applications.
- Establish science-stream governance: build reviews, decision logs, risk and dependency tracking, quality thresholds, and paths for addressing blocking issues.
- Accounting of workstream through readouts for program leadership, including scientific progress, critical decisions, cross-team dependencies, resource needs, and unresolved risks.
What they're looking for
- PhD experience in research within 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, platform biology.
- Deep understanding of therapeutic development and translational biology, including target identification, experimental context, efficacy/selectivity tradeoffs, safety considerations, and data quality.
- Experience leading large interdisciplinary teams that combine experimental science, computational modeling, automation, assay development, data analysis, data platforms, and working alongside external partners.
- Proven track record to lead in matrixed environments where scientific direction, program priorities, and execution accountability are shared across organizations.
- Proficiency with high-content and high-throughput data generation, including molecular, cellular, and functional readouts, assay quality control, label definition, experimental composition, and model validation.
- Ability to collaborate deeply with AI and infrastructure teams on model requirements, feature stores, data lineage, compute planning, evaluation metrics, and closed-loop experimentation.
Nice to have
- Experience in RNA biology and involvement with advancing RNA therapeutics from pre-clinical to clinical phases (e.g. siRNAs, ASOs, mRNA vaccines).
- Prior leadership of a therapeutic discovery platform, data-rich experimental platform, translational program, or high-throughput scientific data effort.
- Experience connecting experimental datasets to ML models, active learning, foundation models, simulation, or build systems.
- Track record translating pre-clinical data into candidate decisions or product profile predictions.
- Familiarity with NVIDIA AI platforms, BioNeMo, GPU-accelerated computational biology, or large-scale scientific data infrastructure and experience building operating models across pharma, technology, engineering, automation, or external research partners.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
NVIDIA seeks an AI enabled drug discovery science leader who can coordinate science streams.
The role exists where RNA therapeutics, experimental data generation, computer-based modeling, mechanization, and translational science converge. It requires a hands-on researcher with prior experience who can guide science teams, make well-considered technical tradeoffs, and keep experimental, data, model, and compute activities coordinated.
What you'll be doing:
Coordinate the operating plan across research priorities, execution, and program achievements.
Convert scientific therapeutic objectives into executable plans.
Lead NVIDIA workstreams spanning data curation & analysis, machine learning, automation, and platform engineering across scales of biology.
Collaborate on assay and readout strategies so efficacy, selectivity, adaptability, translatability, and safety data are AI prepared.
Guide closed-loop learning across AI-generated hypotheses, automated experiments, multiplexed readouts, model updates, and science decisions.
Review science validity for key biology applications.
Establish science-stream governance: build reviews, decision logs, risk and dependency tracking, quality thresholds, and paths for addressing blocking issues.
Accounting of workstream through readouts for program leadership, including scientific progress, critical decisions, cross-team dependencies, resource needs, and unresolved risks.
What we need to see:
PhD experience in research within 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, platform biology.
Deep understanding of therapeutic development and translational biology, including target identification, experimental context, efficacy/selectivity tradeoffs, safety considerations, and data quality.
Experience leading large interdisciplinary teams that combine experimental science, computational modeling, automation, assay development, data analysis, data platforms, and working alongside external partners.
Proven track record to lead in matrixed environments where scientific direction, program priorities, and execution accountability are shared across organizations.
Proficiency with high-content and high-throughput data generation, including molecular, cellular, and functional readouts, assay quality control, label definition, experimental composition, and model validation.
Ability to collaborate deeply with AI and infrastructure teams on model requirements, feature stores, data lineage, compute planning, evaluation metrics, and closed-loop experimentation.
Ways to stand out from the crowd:
Experience in RNA biology and involvement with advancing RNA therapeutics from pre-clinical to clinical phases (e.g. siRNAs, ASOs, mRNA vaccines).
Prior leadership of a therapeutic discovery platform, data-rich experimental platform, translational program, or high-throughput scientific data effort.
Experience connecting experimental datasets to ML models, active learning, foundation models, simulation, or build systems.
Track record translating pre-clinical data into candidate decisions or product profile predictions.
Familiarity with NVIDIA AI platforms, BioNeMo, GPU-accelerated computational biology, or large-scale scientific data infrastructure and experience building operating models across pharma, technology, engineering, automation, or external research partners.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Company
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