Senior Developer Relations, Automated Synthetic Chemistry Science Lead

Nvidia

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $224,000–$356,500 / yr
Posted: 2 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $208k

This role $290k

$137k most similar roles pay here $380k

This role pays more than 93% of similar roles. Most pay $176,562–$238,650 — the shaded band above. At the midpoint, this role pays about $290k versus about $208k 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 879 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 859 roles with salary data.

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At a glance

TL;DR · Senior Developer Relations, Automated Synthetic Chemistry Science Lead

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As the Senior Developer Relations, Automated Synthetic Chemistry Science Lead, you will coordinate science streams at the intersection of experimental science, optimization, analytical characterization, AI modeling, laboratory automation, and data infrastructure. You will manage operating plans by converting research objectives into experimental priorities, campaign planning, and success criteria while leading initiatives in synthetic chemistry, catalysis, process chemistry, machine learning, and automated systems. Your daily work involves building standardized experimental traces, guiding AI systems for outcome prediction and optimization, and integrating automated experimentation into a closed-loop operating model. You will establish science-stream governance and provide readouts on scientific progress to leadership. Required expertise includes experimental optimization, automated experimental platforms, and data curation. The role addresses the challenge of accelerating experimentation while maintaining quality through advanced tools like GPU-accelerated scientific computing and active learning.

Skills

Machine Learning AI Modeling Automated Systems Robotics Data Infrastructure Synthetic Chemistry Process Chemistry Analytical Science Active Learning Predictive Modeling Data Curation Laboratory Automation Simulation Experimental Optimization

What you'll do

Convert research objectives into experimental priorities, parameter-space development, and campaign planning.
Lead interdisciplinary research initiatives across synthetic chemistry, catalysis, machine learning, and automated systems.
Build standardized experimental traces that capture successful outcomes, metadata, and quality-control signals.
Guide AI systems for feasibility assessment, outcome prediction, and campaign orchestration.
Integrate automated experimentation and data streams into a closed-loop operating model.
Establish science-stream governance including decision logs, risk tracking, and quality thresholds.
Prepare recurring workstream readouts regarding scientific progress, resource needs, and unresolved risks for leadership.

What we're looking for

PhD or equivalent experience in experimental science, chemical engineering, material science, robotics, automation, analytical science, platform development, or AI enabled science workflows.
10+ years of hands-on experience in experimental science, chemical engineering, material science, robotics, automation, analytical science, platform development, or AI enabled science workflows.
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.
Owned an experimental optimization platform, automated lab effort, AI-enabled science initiative, or high-impact applied research program (preferred).
Familiarity with GPU-accelerated scientific computing, simulation, AI agents, or large-scale data infrastructure (preferred).

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