Staff Infrastructure Engineer – Storage Platform at Tensorwave

About TensorWave Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute at scale. We've built a versatile cloud platform that eliminates infrastructure barriers, empowering builders to focus on innovation instead of fighting their stack. Because breakthrough AI should move at the speed of ideas, not infrastructure. About the Role We are building and operating large-scale infrastructure platforms to support high-performance AI and machine learning workloads across multiple data centers. Our environment includes GPU-intensive systems, high-throughput networking, and distributed storage platforms that must deliver consistent performance at scale. We are looking for a Staff Infrastructure Engineer – Storage Platform to own the design, operation, and evolution of our storage systems. This role combines architecture and hands-on operational ownership, ensuring that storage platforms are both well-designed and reliably executed in production. You will be responsible for defining how storage works across the organization while remaining deeply involved in real-world system behavior, performance tuning, and incident response. What You’ll Do ARCHITECTURE & PLATFORM DESIGN - Design and evolve storage architectures supporting Kubernetes (block, file, object storage), AI/ML and high-performance compute workloads - Evaluate and select storage technologies based on performance (IOPS, throughput, latency), scalability and fault tolerance, operational complexity and maintainability - Define storage standards, best practices, and reference architectures - Design for resilience over traditional HA, including failure-domain awareness PLATFORM OWNERSHIP & OPERATIONS - Own production storage platforms, including Ceph (RBD, CephFS, RGW), High-performance NAS (Weka, VAST, or similar) - Lead lifecycle operations - Cluster design and deployment, expansion and scaling, upgrades and migrations - Perform and guide capacity planning, performance tuning, failure analysis PERFORMANCE & RELIABILITY - Analyze storage performance across IOPS, throughput, latency, and tail latency - Identify and resolve bottlenecks across disk subsystems, network paths (including RDMA), client access patterns - Lead root cause analysis for storage-related incidents - Ensure storage platforms meet the demands of GPU and Kubernetes workloads KUBERNETES STORAGE INTEGRATION - Define and implement Kubernetes storage patterns - CSI drivers, StorageClasses, persistent storage design - Troubleshoot complex Kubernetes storage issues involving stateful workloads, provisioning failures, performance anomalies - Partner with platform teams to align storage with workload requirements AUTOMATION & TOOLING - Design and implement automation for storage deployment and configuration, cluster lifecycle management - Leverage tools such as Ansible, Terraform, Kubernetes manifests / Helm - Integrate storage platforms into observability stacks (Prometheus, Grafana, etc.) TECHNICAL LEADERSHIP - Serve as the technical authority for storage across the organization - Mentor engineers on storage systems, performance, and troubleshooting - Establish operational standards and best practices - Drive continuous improvement of storage reliability and performance Who You Are Required Qualifications - 7+ years of experience in infrastructure, storage, or distributed systems - Deep hands-on experience with distributed storage systems in production - Strong experience with Ceph (RBD, CephFS, and/or RGW) - Experience with high-performance storage platforms such as: - Weka, VAST Data, or similar - Strong understanding of: - Storage performance characteristics - Data replication and failure domains - Distributed system design principles - Strong Linux systems expertise - Ability to troubleshoot across: - Storage, network, and compute layers Preferred Qualifications - Experience supporting AI/ML or HPC workloads - Familiarity with: - NVMe-based architectures - RDMA or high-throughput Ethernet - Experience integrating storage with Kubernetes at scale - Experience operating across multiple data centers - Exposure to object storage and S3-compatible APIs What We Offer - Stock Options - 100% paid Medical, Dental, and Vision insurance for Employees - Company Health Savings Account Contributions - 100% paid Short Term and Long Term Disability Insurance for Employees - Life and Voluntary Supplemental Insurance Options - Other Insurance Options, such as Pet & Legal Insurance - Various Supplementary Health Benefits, such as discounted Virtual Healthcare Appointments and Serious Illness Support - Flexible Spending Account - 401(k) - Employee Assistance Program - Flexible PTO - Paid Holidays - Parental Leave - Other In-Office Perks Equal Employment Opportunity TensorWave is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of any protected status under applicable law. Reasonable Accommodations TensorWave provides reasonable accommodations in accordance with applicable laws. If you require accommodation during the hiring process, please contact [Upgrade to PRO to see contact]. Employment Eligibility All offers of employment are contingent upon verification of identity and authorization to work in United States, as required by law. Background Checks Where permitted by law, employment may be contingent upon the successful completion of a job-related background check. Data Privacy Notice By submitting an application, you acknowledge that TensorWave may collect, use, and retain your personal information for recruiting and employment-related purposes in accordance with applicable data privacy laws.