Storage Expert (HPC/AI Data Infrastructure)

Quick Facts

• Role: Storage Expert (HPC/AI Data Infrastructure)

• Workload: 39 hours/week; initially limited until 31 Dec 2029, with long-term cooperation possible

• Focus: Research and applied work in HPC, AI, machine learning, and scientific computing

Description

You will join an interdisciplinary HPC team to design, implement, and optimize high-performance storage solutions for AI and HPC workloads. The position combines research on scalable storage architectures with the development of secure data management strategies for large-scale AI datasets, working closely with SMEs and data scientists.

Responsibilities

• Design, implement, and optimize high-performance storage solutions tailored for AI and HPC workloads

• Conduct research on scalable, cost-effective storage architectures for AI training and HPC simulations in resource-constrained environments

• Develop and deploy data management strategies for large-scale AI datasets, including lifecycle management, versioning, backup, and GDPR-compliant security

• Investigate and prototype AI-optimized storage systems using NVMe, SSD caching, and distributed storage (e.g., Ceph, MinIO, Lustre)

• Support SMEs in evaluating and selecting suitable HPC and AI storage solutions

• Collaborate with data scientists and engineers to ensure storage meets training, inference, and real-time processing requirements

• Develop best-practice guidelines and toolkits for secure, efficient AI/HPC data management

• Provide technical advisory and training on data storage, performance monitoring, and data governance

• Monitor and benchmark storage performance to achieve low latency, high throughput, and cost efficiency

Requirements

• Master’s degree (or equivalent) in Computer Science, Electrical Engineering, Data Science, or a related field

• Experience in HPC systems, storage infrastructure, and AI data engineering

• Proven experience with high-performance storage technologies (Lustre, GPFS, Ceph, NFS, S3-compatible storage)

• Strong knowledge of AI/ML data pipelines (ingestion, preprocessing, model training workflows)

• Hands-on containerization with Docker and Kubernetes, plus orchestration tools in HPC/AI environments

• Familiarity with cloud storage platforms

• Experience with data lifecycle management, data versioning, and metadata management

• Solid understanding of data security, privacy (GDPR), and compliance for AI applications

• Excellent analytical and problem-solving skills

• Excellent communication skills and ability to explain technical concepts to non-technical stakeholders

• Very good German skills (C1) and English skills (C1)

• Willingness to travel on business

Benefits

• Stimulating research environment in Scientific Computing with links to applied computer science, HPC, AI, and machine learning

• Flexible working hours and mobile working

• Modern, diverse workplace with a committed team

• Opportunities for professional development

• Public-sector social benefits

• Possibility of pursuing a doctorate