Fireworks’ state of the art training and inference platform take you beyond the frontier, transforming open models into your specialized intelligence. Backed by Bessemer, Index and Lightspeed.
About 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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
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, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.
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...
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