Developer Relations Manager

This is a fully remote position, open to applicants in California.

📋 Description

• Serve as a reliable technical consultant for research laboratories, focusing on identifying and expediting high-impact workloads by incorporating NVIDIA's frameworks, libraries, and core software stack into research initiatives.

• Continuously map and evaluate the research ecosystem to pinpoint institutional growth opportunities and guide long-term technology strategies.

• Remain updated on research publications within affiliated fields to foresee emerging trends and offer technical guidance on potential collaboration areas.

• Work collaboratively with Research Account Managers, Solution Architects, and Business Development teams to enhance researcher capabilities.

• Build stronger relationships with laboratory personnel, gaining insight into organizational dynamics and the comprehensive range of research being undertaken.

• Participate in domain-specific scientific conferences and promote NVIDIA's involvement alongside other experts in the field.

• Advocate for researchers' needs internally by converting academic feedback into actionable insights that inform product development, educational initiatives, and platform roadmaps.

⛳️ Requirements

• PhD in Computer Science, AI, Machine Learning, Computational Science, Physics, Biology, Bioengineering, or a related technical field; or equivalent experience demonstrating comparable research depth.

• Over 3 years of relevant experience.

• Profound expertise in applying AI to protein design, genomics, transcriptomics, structural biology, molecular simulation, drug discovery, biomedical imaging, or biomedical foundation models.

• Strong command of AI methodologies pertinent to life sciences, including protein language models, sequence models, graph neural networks, diffusion models, multimodal models, and generative models for molecules or proteins.

• Familiarity with bioscience research workflows, encompassing biological datasets, omics pipelines, structure prediction, target discovery, wet-lab collaboration, lab-in-the-loop experimentation, and reproducibility.

• Capability to engage leading bioscience labs on model accuracy, biological validity, scalability, experimental integration, translational impact, and the role of accelerated computing in AI-driven discoveries.

• Research credibility established through publications, open-source scientific software, collaborations with academic or national labs, technical leadership, or practical experience in AI for physics or computational science.

🏝️ Benefits

• Eligibility for equity

• Comprehensive benefits