Key responsibilities include:

• Designing and implementing distributed storage solutions to support scaling data-intensive AI workloads
• Contributing to the development of exabyte-scale, S3-compatible object storage
• Integrating dedicated storage clusters into diverse customer environments
• Working with technologies such as RDMA, GPU Direct Storage, and distributed filesystems protocols such as NFS or FUSE to optimize storage performance and efficiency
• Leading efforts to improve the reliability, durability, security, and observability of the storage stack
• Collaborating with operations teams to monitor, troubleshoot, and improve storage systems in production environments
• Setting the bar for developing metrics and dashboards to provide visibility into storage performance and health
• Analyzing telemetry and system data to drive improvements in throughput, latency, and resilience
• Working cross-functionally with platform, product, and infrastructure teams to deliver seamless storage capabilities across the stack
• Sharing knowledge and mentoring other engineers on best practices in building distributed, high-performance systems

Requirements include:

• Bachelor's, Master's, or PhD degree in Computer Science, Engineering, or a related field
• 8-10+ years of experience working in storage systems engineering or infrastructure
• Strong hands-on experience with object storage or distributed filesystems in production environments
• Experience with one or more storage protocols (e.g. S3, NFS) and file systems such as Ceph, DAOS, or similar
• Proficiency in a systems programming language such as Go, C, or Rust
• Proficiency leveraging AI tools to augment software development
• Familiarity with storage observability tools and telemetry pipelines (e.g., ClickHouse, Prometheus, Grafana)
• Experience working with cloud-native infrastructure, Kubernetes, and scalable system architectures

The base salary range for this role is $188,000 to $275,000.

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The primary responsibilities of this role include designing and implementing distributed storage solutions to support scaling data-intensive AI workloads, contributing to the development of exabyte-scale, S3-compatible object storage, and integrating dedicated storage clusters into diverse customer environments. Additionally, the successful candidate will work with technologies such as RDMA, GPU Direct Storage, and distributed filesystems protocols such as NFS or FUSE to optimize storage performance and efficiency.

Key responsibilities also include leading efforts to improve the reliability, durability, security, and observability of our storage stack, collaborating with operations teams to monitor, troubleshoot, and improve storage systems in production environments, setting the bar for developing metrics and dashboards to provide visibility into storage performance and health, analyzing telemetry and system data to drive improvements in throughput, latency, and resilience, and working cross-functionally with platform, product, and infrastructure teams to deliver seamless storage capabilities across the stack.

A key aspect of this role is sharing knowledge and mentoring other engineers on best practices in building distributed, high-performance systems.

To be successful in this role, the ideal candidate will have a strong background in storage systems engineering or infrastructure, with a minimum of 8-10 years of experience. They will also have hands-on experience with object storage or distributed filesystems in production environments, as well as proficiency in a systems programming language such as Go, C, or Rust. Additionally, they will have experience working with cloud-native infrastructure, Kubernetes, and scalable system architectures, and familiarity with storage observability tools and telemetry pipelines.

If you're a motivated and experienced engineer looking to join a dynamic team and contribute to the development of cutting-edge storage solutions, we encourage you to apply for this exciting opportunity.

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