Developer Relations Manager

📋 Description

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

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

• Keep abreast of research publications across related fields to foresee emerging trends and offer technical guidance on potential collaboration areas.

• Collaborate across departments with Research Account Managers, Solution Architects, and Business Development teams to enhance researcher capabilities.

• Build stronger connections with laboratory personnel, comprehending organizational dynamics and the comprehensive scope of ongoing research.

• Participate in domain-specific scientific conferences and facilitate NVIDIA's involvement alongside other subject matter experts.

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

⛳️ Requirements

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

• Minimum of 3 years of relevant experience.

• Extensive 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, including biological datasets, omics pipelines, structure prediction, target discovery, wet-lab collaboration, lab-in-the-loop experimentation, and reproducibility.

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

• Established research credibility through publications, open-source scientific software, collaborations with academic or national labs, technical leadership, or hands-on experience in AI for physics or computational science.

🏝️ Benefits

• Eligibility for equity

• Comprehensive benefits