5 hours ago
Remote, WorldwideMid Level
Responsibilities
- Create technical educational content demonstrating workloads using virtual machines, GPU clusters, Kubernetes, SLURM, and Soperator.
- Develop sample code, tutorials, reference architectures, video tutorials, and live coding sessions for cloud computing and ML infrastructure.
- Collaborate with academic partners to understand requirements and develop aligned solution architectures.
- Design and document Infrastructure as Code solutions, technical documentation, and how-to guides with the Solutions Architect team.
- Advise academic partners on GPU cloud technologies, infrastructure, and best practices.
Requirements
- Strong understanding of cloud infrastructure and distributed computing principles, including virtual machines, containerization, compute resources, networking, storage optimization, and resource management.
- Experience with Infrastructure as Code, preferably Terraform, and prior work with containerization and cloud environments.
- Knowledge of GPU clusters and techniques for optimizing diverse ML and cloud workloads.
- Working knowledge of Kubernetes and SLURM.
- Strong programming skills in Python and familiarity with the PyTorch ecosystem.
- Excellent written communication skills and the ability to express technical ideas clearly in text.
- At least 2 years of experience in software development, cloud engineering, DevOps, or a similar technical role.
- Experience with cloud technologies and infrastructure is required.
- Bonus qualifications include MLflow, Apache Airflow, Kubeflow, AWS, GCP, Azure ML, NVIDIA NGC, hybrid or on-premises GPU infrastructure, technology partnerships, third-party integrations, and public presentations.
Benefits
- Competitive compensation.
- Career growth and learning opportunities.
- Flexibility and ownership.
- Collaborative and innovative culture.
- Opportunity to work on impactful AI projects.
- International environment with talented teams.
Tech Stack
Categories
Solutions Engineering
About Nebius
The Nebius AI Cloud brings powerful full-stack infrastructure for AI developers and practitioners across startups, enterprises and science institutes to build and deploy generative AI applications and rapidly deliver scientific breakthroughs by training and running ML models within a secure, high-performance, and cost-optimized cloud environment.