Fireworks Member of Technical Staff, Data Platform at Fireworks to build a unified, high-performance data layer for GPU workloads spanning multiple clouds. Own data storage, caching, and movement to optimize training and inference.
Responsibilities
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.
Key Technical Archetypes:
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.
Qualification
Bachelor's degree in Computer Science5+ years of experience designingHands-on experience with KubernetesFamiliarity with Linux internalsExperience movingSolve Hard Problems
Required
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
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.
Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
