Member of Technical Staff Data Infrastructure Storage
Fireworks AI operates an AI platform for production inference and training of open-source models.
Funding history
About Fireworks AI
Fireworks AI provides serverless and dedicated model inference, model deployment, and supervised and reinforcement fine-tuning for developers and enterprises building AI applications.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build and operate a unified, high-performance data layer across parallel file systems, object storage, distributed caches, and key-value stores. You will optimize data placement, replication, caching, and movement across clouds so training and inference workloads can access datasets, checkpoints, model weights, and KV cache at scale. You will develop performance-critical data-path code, deploy storage clusters, and diagnose bottlenecks across kernels, networks, caches, and storage backends.
Requirements
- Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
- 5+ years of experience designing, building, and operating production storage, caching, or data infrastructure at scale
- Strong software development skills in C/C++, Rust, or Go and experience writing performance-critical data-path code
- Experience using Python or Go for automation
- Deep understanding of distributed storage fundamentals, including consistency models, replication, consensus, metadata scaling, caching, and failure recovery
- Hands-on Kubernetes experience, including CSI for mounting high-performance volumes into ephemeral GPU pods
- Experience deploying and operating storage clusters such as WEKA, VAST, Lustre, Ceph, or MinIO across clouds using Terraform or Pulumi
- Proficiency with benchmarking and profiling tools such as fio, iperf3, NCCL tests, perf, and eBPF
Responsibilities
- Build a unified high-performance data layer across file systems, object storage, distributed caches, and key-value stores
- Design data placement, replication, caching, and movement across clouds
- Ensure datasets, checkpoints, model weights, and KV cache are available for GPU workloads at scale
- Develop performance-critical storage and data-path code
- Deploy and operate storage clusters across clouds using infrastructure as code
- Benchmark, profile, and resolve bottlenecks across kernels, networks, caches, and storage backends
Benefits
- Equity