Senior Developer Relations, Automated Synthetic Chemistry Science Lead

We at NVIDIA seek an Senior Developer Relations, Automated Synthetic Chemistry Science Lead who can coordinate science streams.

This role exists at the crossroads of experimental science, optimization, analytical characterization, AI modeling, laboratory automation, and data infrastructure.

It demands a practitioner with a background in research who can lead interdisciplinary science teams and accelerate experimentation without compromising scientific quality.

What you'll be doing:

Coordinate the operating plan across research priorities, technical execution, and program achievements.

Convert research objectives into experimental priorities, parameter-space development, campaign planning, and success criteria.

Lead research initiatives encompassing synthetic chemistry, catalysis, process chemistry, machine learning, automated systems, and data processing.

Build standardized experimental traces capturing successful and unsuccessful outcomes, metadata, quality-control signals, analytical summaries.

Guide AI systems for feasibility assessment, outcome prediction, optimization, scope exploration, and campaign orchestration.

Integrate automated experimentation, analytical data streams, data curation, model retraining, and campaign decisions into a closed-loop operating model.

Establish science-stream governance: build reviews, decision logs, risk and dependency tracking, quality thresholds, and paths for addressing blocking issues.

Prepare recurring workstream readouts for program leadership, including scientific progress, critical decisions, cross-team dependencies, resource needs, and unresolved risks.

What we need to see:

PhD (or equivalent experience) with 10+ years of hands-on experience in experimental science, chemical engineering, material science, robotics, automation, analytical science, platform development, AI enabled science workflows, or equivalent experience.

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.

leading large interdisciplinary teams across experimental science, automation, machine learning, data platforms, analytical science, software engineering, and partner execution.

Ability to operate in matrixed partner governance, translating scientific direction into technical execution and leadership-ready decisions.

Ways to stand out from the crowd:

Owned an experimental optimization platform, automated lab effort, AI-enabled science initiative, or high-impact applied research program.

Built datasets with detailed unsuccessful outcomes and standardized analytical metadata across multiple experimental contexts.

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.

building joint operating models across life sciences, technology, academic, 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!

You will also be eligible for equity and benefits.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.