Member of Technical Staff, Data Infrastructure

The Role

Make data a solved problem for every GPU workload at Fireworks. We own how data is stored, cached and moved across clouds, so that 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.

In practice, that means building 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.

Minimum qualifications

• Bachelor's degree in Computer Science, Engineering, or a related technical field (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 (e.g., WEKA, VAST, Lustre, Ceph, MinIO) across clouds using infrastructure-as-code tools such as Terraform or Pulumi.

• Proficiency with performance benchmarking and profiling tools (e.g., fio, iperf3, NCCL tests, perf, eBPF) to identify and resolve bottlenecks across the kernel, network, cache and storage backend.

Preferred qualifications

• Experience as a core contributor or owner of a storage, caching or KV system, either in-house or open source (e.g., Ceph, Alluxio, JuiceFS, RocksDB, FoundationDB, TiKV, Redis/Dragonfly, SeaweedFS, MinIO).

• Experience with high-performance computing or large-scale AI clusters (e.g., Slurm, Kubernetes GPU pools) and familiarity with AI I/O patterns such as checkpoint bursts, random dataset reads, and model-weight loading for inference cold starts.

• Familiarity with Linux internals, including kernel I/O paths, POSIX semantics, page cache behavior, io_uring/SPDK, and OS-level tuning (sysctl, I/O schedulers, NUMA).

• Familiarity with high-performance networking (RDMA, RoCE v2, InfiniBand) and GPUDirect Storage.

• Experience moving, replicating or caching petabyte-scale datasets across clouds (e.g., AWS, GCP and specialized neoclouds), with attention to egress cost and consistency.

• Preference for software-defined storage over proprietary hardware appliances.

• We value depth in one area over a checklist of all of them. If you've built serious storage, caching or KV infrastructure and want to apply it to AI at scale, we'd like to hear from you.