Key Responsibilities:

Architect and implement distributed storage solutions tailored for massive AI workloads, ensuring seamless integration with compute infrastructure.
Optimize storage performance for high-throughput and low-latency access, leveraging cutting-edge hardware (NVMe, SSD) and software (Ceph, ZFS).
Collaborate with infrastructure teams to enhance deployment pipelines using Infrastructure-as-Code (IaC) best practices, ensuring robust and secure storage delivery.
Monitor and maintain storage systems across on-premise clusters and cloud environments, proactively addressing bottlenecks and failures.
Contribute to capacity planning and data durability strategies to support xAI’s growing computational needs.

Requirements:

Designing and operating distributed storage systems (e.g., Ceph, Lustre, or ZFS) at scale for high-performance computing (HPC) or AI workloads.
Hands-on experience with storage hardware (NVMe, SSD, HDD) and tuning I/O performance in compute-intensive environments.
Writing scalable, high-performance code in Rust or Go for storage-related applications or tooling.
Managing storage infrastructure with IaC tools like Pulumi, Terraform, or Ansible.
Familiarity with Kubernetes storage primitives (e.g., Persistent Volumes, CSI drivers) and integrating storage with containerized workloads.
Bonus: Experience with AI/ML data pipelines, including handling large datasets for training and inference.

Tech Stack

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