Member of Technical Staff

Responsibilities

• Design and operate storage architectures for training datasets, checkpointing, inference artifacts, and shared research workflows.
• Deploy and tune parallel filesystems, object storage, and local NVMe caching for demanding AI workloads.
• Benchmark throughput, latency, metadata performance, and concurrent access using representative training and checkpoint workloads.
• Build provisioning, capacity planning, lifecycle management, and operational automation for storage services.
• Design and test replication, recovery, backup, and failure-handling procedures against durability and availability targets.
• Diagnose performance and reliability issues across applications, clients, networks, filesystems, and devices.
• Implement access controls, tenant separation, quotas, monitoring, and runbooks while collaborating with compute and networking teams.
• Work directly with customers and engineering teams supporting large-scale AI infrastructure deployments.

Requirements

• At least 3 years of experience building or operating production distributed storage systems.
• Hands-on experience with at least one parallel or distributed filesystem or object storage platform, such as Lustre, BeeGFS, Ceph, or GPFS.
• Strong Linux administration and performance troubleshooting skills.
• Experience automating infrastructure operations with Python, Go, Bash, or similar languages.
• Understanding of storage failure modes, data integrity, consistency, replication, and recovery.
• Knowledge of block, file, and object storage semantics and performance tradeoffs.
• Experience with NVMe/SSD performance, filesystem tuning, I/O profiling, and benchmarking.
• Knowledge of high-throughput storage networking, distributed client behavior, capacity forecasting, observability, alerting, and safe maintenance procedures.
• Knowledge of authentication, authorization, encryption, and secure data lifecycle management.
• Preferred experience supporting large GPU training clusters and high-volume checkpoint workloads; S3-compatible object storage, data tiering, distributed caching, RDMA-enabled storage, GPUDirect Storage, Kubernetes storage integrations, SLURM environments, storage cost optimization, or open-source storage contributions.

Benefits

• Cash compensation is $150,000–$300,000 plus equity incentives.