Requirements
- Advanced Scientific Background: A post-graduate degree in a technical field such as computer science, biology, climate, astrophysics, or a related computational science
- Computing Expertise: 8+ years of combined experience spanning AI/ML infrastructure and research-facing technical advisory; experience building networks across academic, nonprofit, or government research is strongly preferred
- Exceptional ability to communicate complex technical and scientific concepts to both high-impact research scientists and executive leadership.
- Hands-on experience with cluster workload management using Slurm and Kubernetes.
- Successful track record of accelerating time from grant approval to first result for computationally intensive projects.
- History of collaborative impact in high-intensity, team-based environments.
- Sense of controlled urgency in driving work to completion.
- The highest integrity and ability to maintain confidentiality.
- Understanding of the tech stack needed to design, train, deploy, and maintain state-of-the-art AI models at a production scale.
- Experience producing technical writing for expert and general audiences.
Nice to Have
- PhD in a technical field such as computer science, biology, climate, astrophysics, or a related computational science
- Grant & Funding Acumen: Proven track record of supporting large-scale, federally funded, or private scientific grant proposals.
- In-depth knowledge of data center storage and networking technologies and solutions.
- Proficiency with modern machine-learning hosting software frameworks, such as NVIDIA Dynamo, TensorFlow Serving, Ray, etc.
- Prior leadership of data center infrastructure initiatives and projects, such as evaluating hardware scalability, securing data, or executing large-scale upgrades.
- Expertise in relevant technical focus areas, e.g., AI model performance monitoring or network and storage optimization, etc.
- Expert-level experience and industry credentials in the software and hardware frameworks that drive modern AI, and competence in at least one, and preferably multiple, fields of science impacted by modern AI.
- Ability to work with and effectively translate technical concepts across multiple scientific disciplines.
- Ability to critically evaluate scientific and technical publications and emerging methods in related disciplines.
- Experience working with science-focused institutions such as philanthropic organizations or academic/government research institutions.
