We are seeking a
Cloud Engineer with deep expertise in distributed storage systems, specifically Ceph and WEKA, to architect, deploy, and maintain scalable storage infrastructures supporting AI and HPC workloads. This role is critical to ensure performance, resiliency, and data integrity across our customer environments.
You will play a key role in supporting large-scale GPU infrastructure deployments, collaborating with engineering and operations teams to deliver best-in-class storage solutions tailored to the demanding requirements of AI and ML workloads.
Key Responsibilities- Architect, deploy, and manage high-performance, scalable storage solutions based on Ceph and WEKA for AI and deep learning workloads.
- Optimize IOPS and throughput across distributed systems supporting hundreds of GPUs per cluster.
- Develop and maintain infrastructure-as-code templates for automated storage deployments.
- Monitor system performance and implement improvements to ensure low latency, high bandwidth, and data integrity.
- Lead incident response and root cause analysis for storage-related issues across production environments.
- Collaborate with system engineers, network teams, and customer success to tailor storage performance to specific workload needs.
- Evaluate and integrate new storage technologies and NVMe architectures into the AI stack.
- Write and maintain detailed technical documentation and runbooks.
- Contribute to strategic infrastructure planning and scaling initiatives.
Required Qualifications- 7+ years of experience managing and scaling enterprise storage systems.
- 3+ years of hands-on experience with Ceph and/or WEKA in production environments.
- Deep knowledge of storage architectures: object, block, and parallel file systems.
- Strong understanding of RDMA, InfiniBand, NVMe-oF, and distributed metadata systems.
- Proficiency in Linux (preferably Ubuntu/CentOS) and scripting (Bash, Python).
- Experience with performance tuning for AI/ML workloads using storage-intensive frameworks like TensorFlow, PyTorch, etc.
- Familiarity with containerized and virtualized environments: Docker, Kubernetes, KVM, etc.
- Strong troubleshooting and diagnostic skills in large-scale, multi-tenant environments.
Preferred Qualifications- Experience with AI model training workflows, particularly storage IO patterns in multi-node GPU clusters.
- Familiarity with storage solutions from NetApp, DDN, VAST Data, and cloud-native offerings.
- Experience with monitoring tools (Prometheus, Grafana), and configuration management (Ansible, Terraform).
- Knowledge of data governance and compliance standards in AI environments.
Compensation and BenefitsWe are committed to supporting the people who help us grow. We offer a competitive compensation package that includes base salary, performance bonuses and stock option opportunities.
The base salary range for the Cloud Engineer is $121,500 to $178,750. This pay range reflects the broad, minimum to maximum, pay range for this job for the location for which it has been posted. Compensation decisions are dependent on several factors including, but not limited to, an individual's qualifications, location where the role is to be performed, internal equity, and alignment with market data.
Our benefits package includes comprehensive medical, dental, vision coverage, 401(k) with company match, generous paid time-off and professional development support.
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