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Senior Developer Relations, Automated Synthetic Chemistry Science Lead

NVIDIA· Account Mgr, Dev Relations
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Santa Clara, CA$224k - $356.5kFull-timePosted 4w ago

About the role

Senior-level role coordinating science streams at the intersection of experimental science, optimization, analytical characterization, AI modeling, laboratory automation, and data infrastructure. Leads interdisciplinary science teams to accelerate experimentation while maintaining scientific quality.

Responsibilities

  • 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.

Qualifications

  • 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.
  • Experience 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.

Skills

Required:Experimentation leadershipExperimental optimizationAutomated experimentation platforms

Benefits

  • Equity eligibility
  • Benefits (see company page)
Last updated Sep 25, 2026. Interested? Apply on the company site.

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