Data Infrastructure Engineer (AI)

Location: San Mateo, United States; hybrid

Salary: $175K–$220K annually, plus equity

Company

Fireworks AI provides a platform for building, training, and serving specialized AI models across text, image, embedding, audio, and multimodal workloads.

What you will do

• Build a unified, high-performance data layer spanning parallel file systems, object storage, distributed caches, and key-value stores across multiple clouds.
• Ensure datasets, checkpoints, model weights, and KV cache are placed, replicated, and moved to the right location for GPU workloads.
• Eliminate data-staging delays for training jobs and weight-download delays for inference replicas.
• Design and operate storage, caching, and data infrastructure that handles production-scale workloads and failure recovery.
• Identify and resolve bottlenecks across the kernel, network, cache, and storage backend using benchmarking and profiling tools.

Requirements

• 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, including performance-critical data path code; Python or Go for automation.
• Deep understanding of distributed storage fundamentals, including consistency models, replication, consensus such as Raft or Paxos, metadata scaling, caching, and failure recovery.
• Hands-on Kubernetes experience, including CSI, to mount high-performance volumes into ephemeral GPU pods.
• Experience deploying and operating storage clusters such as WEKA, VAST, Lustre, Ceph, or MinIO across clouds with Terraform or Pulumi.
• Proficiency with performance benchmarking and profiling tools such as fio, iperf3, NCCL tests, perf, or eBPF.

Nice to have

• Ownership or core-contributor experience with storage, caching, or KV systems such as Ceph, Alluxio, JuiceFS, RocksDB, FoundationDB, TiKV, Redis, Dragonfly, SeaweedFS, or MinIO.
• Experience with high-performance computing or large-scale AI clusters, including Slurm or Kubernetes GPU pools, and 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, sysctl, I/O schedulers, and NUMA.
• Familiarity with high-performance networking, including RDMA, RoCE v2, InfiniBand, and GPUDirect Storage.
• Experience moving, replicating, or caching petabyte-scale datasets across AWS, GCP, and specialized neoclouds, with attention to egress cost and consistency.
• Preference for software-defined storage over proprietary hardware appliances.
• Depth in at least one storage, caching, or KV infrastructure area; experience across every listed area is not required.

Culture & Benefits

• Work on challenging AI infrastructure problems, including low-latency inference and scalable model serving.
• Use emerging technologies that affect how businesses and developers build with AI.
• High ownership and direct impact in a fast-growing environment with limited bureaucracy.
• Collaborate with engineers and AI researchers focused on curiosity and innovation.
• Equity is offered as part of compensation.