Data Infrastructure Architect: Storage & Caching for GPUs

Fireworks AI; San Mateo; On-site; PHP 1,200,000 - 2,400,000; Full time

Job summary

Fireworks AI is seeking a senior storage infrastructure engineer to design, build, and operate scalable data storage, caching, and movement across clouds for GPU workloads. You will optimize data placement and manage datasets and KV stores to ensure low latency, high reliability for training and inference at scale.

You will collaborate with AI teams, implement storage clusters using modern tools, and apply performance profiling to maintain peak system throughput in a fast-moving environment.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field.
• 5+ years designing, building and operating storage, caching or data infrastructure at scale.
• Strong software development in C/C++, Rust or Go; Python or Go for automation.
• Deep understanding of distributed storage fundamentals: consistency models, replication and consensus.
• Hands-on experience with Kubernetes, including CSI.
• Experience deploying and operating storage clusters across clouds using Terraform or Pulumi.
• Proficiency with benchmarking and profiling tools (fio, iperf3, NCCL tests, perf, eBPF).

Responsibilities

• Design, build and operate scalable storage, caching and data infrastructure for AI workloads.
• Architect data movement across clouds, optimizing placement for GPU jobs.
• Deploy and operate storage clusters (WEKA, VAST, Lustre, Ceph, MinIO) across clouds with IaC tools.
• Benchmark and profile performance across kernel, network, cache and storage backends.

Skills

C/C++

Rust

Go

Python

Education

Bachelor’s degree in Computer Science, Engineering, or related field

Tools

Kubernetes

CSI

Terraform

Pulumi

Ceph

MinIO

Lustre

WEKA

VAST

RocksDB

FoundationDB

TiKV

Redis

JuiceFS

GPUDirect Storage

Job description

Fireworks AI is seeking a senior storage infrastructure engineer to design, build, and operate scalable data storage, caching, and movement across clouds for GPU workloads. You will optimize data placement and manage datasets and KV stores to ensure low latency, high reliability for training and inference at scale.

You will collaborate with AI teams, implement storage clusters using modern tools, and apply performance profiling to maintain peak system throughput in a fast-moving environment.