Member of Technical Staff

Responsibilities

• Design and build a unified, high-performance data layer for GPU training and inference workloads.
• Store, cache, replicate, and move datasets, checkpoints, model weights, and KV cache across clouds and storage tiers.
• Develop performance-critical data-path software in C/C++, Rust, or Go, with Python or Go automation where appropriate.
• Address distributed storage concerns including consistency models, replication, consensus, metadata scaling, caching, compaction, and failure recovery.
• Deploy and operate storage clusters and mount high-performance volumes into ephemeral GPU pods using Kubernetes and CSI.
• Benchmark and profile kernel, network, cache, and storage performance to identify and resolve bottlenecks.
• Support high-performance networking and data movement using RDMA, RoCE v2, InfiniBand, and GPUDirect Storage.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• At least 5 years of experience designing, building, and operating production-scale storage, caching, or data infrastructure.
• Strong software development skills in C/C++, Rust, or Go, including performance-critical data-path code.
• Deep understanding of distributed storage fundamentals, including consistency models, replication, consensus, metadata scaling, caching, and failure recovery.
• Hands-on Kubernetes experience, including CSI, for high-performance volumes and 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, and eBPF.
• Preferred experience contributing to or owning storage, caching, or key-value systems such as Ceph, Alluxio, JuiceFS, RocksDB, FoundationDB, TiKV, Redis, Dragonfly, SeaweedFS, or MinIO.
• Preferred experience with HPC or large-scale AI clusters, Slurm, Kubernetes GPU pools, AI I/O patterns, Linux internals, high-performance networking, GPUDirect Storage, and petabyte-scale cross-cloud data movement.
• Preference for experience with software-defined storage rather than proprietary hardware appliances.

Benefits

• Work on challenging AI infrastructure problems involving low-latency inference and scalable model serving.
• Collaborate with engineers and AI researchers in a fast-growing, inclusive environment.
• The posting describes ownership, direct impact, limited bureaucracy, and access to cutting-edge technology.