Member of Technical Staff, Data InfrastructureNew$175K–$220K

Fireworks AI
Apply now

The opportunity

Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed,…

What you'll do

  • We expect strong candidates to have gone deep in at least one of these areas: and to be comfortable in the others:

  • The Storage Systems Builder: Has designed and shipped a storage system: a distributed file system, a blob/object store, a KV store, or a storage engine. They can explain the tradeoffs they made in consistency models, replication, erasure coding, metadata scaling, compaction and failure recovery, and why they made them.

  • The Caching Architect: Has built a distributed or tiered cache across memory, NVMe and remote storage. They think in hit rates, eviction policies, cache coherence, prefetching and tail latency. Bonus if they've worked on caching for model weights, datasets, checkpoints, or LLM KV cache offload.

  • The Linux Power User: Knows the system under the hood: kernel I/O paths, POSIX semantics, page cache behavior, io_uring/SPDK, and OS-level tuning (sysctl, I/O schedulers, NUMA).

  • The Network-As-Code Expert: Understands RDMA, RoCE v2, InfiniBand and GPUDirect Storage from a software and configuration perspective, because neoclouds often need manual plumbing.

  • Bachelor's degree in Computer Science, Engineering, or a related technical: field (or equivalent practical experience).

What they're looking for

  • + 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.