Storage Architect – AI/HPC Cluster Infrastructure

As a Storage Architect at Cerebras Systems, your primary responsibilities include evaluating and designing storage solutions for our AI and HPC cluster deployments. You'll act as the technical lead in vendor engagement, analyzing performance metrics and ensuring that the storage capabilities align with our workload requirements, such as throughput and inference latency. You'll collaborate with cross-functional teams to validate integrations, and also drive internal storage innovation to meet specific use cases.

To qualify for the Storage Architect position at Cerebras Systems, candidates should have a minimum of 7 years of experience in storage architecture, particularly within data-intensive environments like AI/ML or HPC. A deep understanding of file systems, modern storage technologies, and performance trade-offs is essential. Strong communication skills to express complex storage concepts clearly are also crucial for success in this role.

The work culture at Cerebras Systems is described as simple and non-corporate, emphasizing respect for individual beliefs and a collaborative environment. As a Storage Architect, you'll be part of a team that encourages continuous learning and supports individual growth. This environment fosters innovation, allowing you to make significant contributions to groundbreaking AI technology.

The role of Storage Architect is vital to Cerebras Systems' success because storage solutions are foundational to our AI/HPC clusters. The right architecture can significantly impact our ability to meet customer goals. By ensuring efficient data flow and scalability, the Storage Architect helps drive performance and enhances the overall functionality of our groundbreaking technology, ultimately influencing our leadership position in the industry.

Cerebras Systems offers numerous opportunities for growth and professional development for a Storage Architect. You'll work on innovative projects that challenge your skills and provide exposure to evolving technologies. The company fosters a culture of continuous learning, encouraging you to stay updated on the latest industry trends and best practices. You'll have the chance to collaborate with talented engineers and researchers, facilitating knowledge sharing and enhancing your expertise.

During the interview, share specific projects where you’ve designed or implemented storage solutions relevant to AI or HPC applications. Highlight your understanding of the unique challenges in these environments, such as handling large datasets or ensuring low latency. Discuss the technologies and architecture choices you’ve made to support those workloads and how they contributed to project success.

In your response, reflect on various storage technologies, including NVMe, SCM, and distributed file systems. Explain their performance characteristics and how they impact workloads in an AI/HPC context. You should illustrate your ability to evaluate and select the appropriate technology based on specific performance requirements, such as latency and throughput.

Describe your systematic approach for evaluating vendor storage solutions. Discuss criteria like performance benchmarks, compliance with security standards, and cost efficiency. Illustrate how you've previously tracked emerging technologies to ensure the selected solutions align with both current and future requirements.

Share a detailed example of a storage configuration you’ve designed, including the challenges you faced and the solutions you implemented. Emphasize the impact on overall system performance, user satisfaction, or meeting specific compliance standards. Use metrics or feedback to support your results.

Discuss the frameworks and processes you employ to ensure security and compliance in your storage designs. Talk about staying current with industry standards, performing regular audits, and collaborating with cross-functional teams to implement best practices. Provide examples of how these measures have been successfully applied in your previous roles.

Describe the methodologies you employ for evaluating and benchmarking storage solutions. Highlight any specific tools or metrics, such as IOPS, latency measurements, or bandwidth tests. Discuss how you've customized benchmarking to suit different workloads and the insights gained from these activities.

Explain your approach to managing collaboration between hardware, software, and deployment teams in storage architecture projects. Describe tools or practices that facilitate communication and documentation of storage variations, ensuring that all teams are aligned toward common goals.

Present your insights on current challenges in storage architecture for AI applications, such as data scalability, real-time processing demands, or evolving compliance regulations. Discuss how your experience prepares you to address these challenges effectively and drive innovative solutions.

Illustrate how you would assess and prioritize storage requirements based on the specific needs of different tenants in a multi-tenant environment. Discuss strategies for balancing performance, security, and operational goals while optimizing resource allocation.

Share any unique experiences where you’ve contributed to developing custom internal storage solutions. Describe the use cases you focused on and the outcomes. Discuss the collaboration involved with software engineering teams and any roadblocks you overcame to deliver effective solutions.