Senior Data Infrastructure Engineer (GPU/AI)

Fireworks AI; San Mateo (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Fireworks AI in California is seeking a senior storage/data infrastructure engineer to own the data plane for GPU workloads. You will design, deploy and operate a unified data layer spanning parallel file systems, object stores and distributed caches across multiple clouds to feed training and inference pipelines.

You will ensure datasets, checkpoints and model weights are colocated with workloads, optimize data placement, and automate storage clusters with IaC tools like Terraform or Pulumi.

Qualifications

• Bachelor's degree in CS/Engineering or equivalent practical experience.
• 5+ years of experience designing, building and operating storage, caching or data infrastructure in production at scale.
• Strong software development skills in C/C++, Rust or Go, with experience writing performance-critical data path code. Python or Go for automation.
• Deep understanding of distributed storage fundamentals: consistency models, replication and consensus (e.g., Raft/Paxos), metadata scaling, caching, and failure recovery.
• Hands-on experience with Kubernetes, including CSI, to mount high-performance volumes into ephemeral GPU pods.
• Experience deploying and operating storage clusters (WEKA, VAST, Lustre, Ceph, MinIO) across clouds using Terraform or Pulumi.
• Proficiency with benchmarking and profiling tools (fio, iperf3, NCCL tests, perf, eBPF) to identify bottlenecks across kernel, network, cache and storage backend.

Responsibilities

• Make data a solved problem for every GPU workload at Fireworks. We own how data is stored, cached and moved across clouds, so training and inference teams never have to think about where their data lives or how it gets to them, and no GPU ever waits on it.
• Build a unified, high-performance data layer that spans parallel file systems, object storage, distributed caches and key-value stores across multiple clouds.
• Datasets, checkpoints, model weights and KV cache should be in the right place at the right time, at whatever scale the GPUs need.
• Training jobs start without a data-staging step, inference replicas cold-start without waiting on weight downloads, and the system handles placement, replication and movement on its own.

Skills

C/C++

Rust

Go

Python

Kubernetes

Performance profiling

Distributed storage

Education

Bachelor's degree in Computer Science or Engineering

Tools

WEKA

VAST

Lustre

Ceph

MinIO

Terraform

Pulumi

Job description

Fireworks AI in California is seeking a senior storage/data infrastructure engineer to own the data plane for GPU workloads. You will design, deploy and operate a unified data layer spanning parallel file systems, object stores and distributed caches across multiple clouds to feed training and inference pipelines.

You will ensure datasets, checkpoints and model weights are colocated with workloads, optimize data placement, and automate storage clusters with IaC tools like Terraform or Pulumi.