Member of Technical Staff - Storage Infrastructure

Prime Intellect•San Francisco, CA

7d•$150,000 - $300,000

About The Position

Prime Intellect is building the open superintelligence stack, providing infrastructure for AI labs. Their platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. They are developing open-source models for long-horizon tasks and the platform to build them. Prime Intellect has raised $150M from prominent investors and individuals in the AI and infrastructure space. They are seeking individuals passionate about building at the intersection of frontier research, real infrastructure, and go-to-market for a nascent category.

Requirements

3+ years building or operating production distributed storage systems.
Hands-on experience with at least one parallel or distributed filesystem or object storage platform (e.g., Lustre, BeeGFS, Ceph, or GPFS).
Strong Linux administration and performance troubleshooting skills.
Experience automating infrastructure operations in 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 their performance tradeoffs.
Familiarity with NVMe/SSD performance, filesystem tuning, I/O profiling, and benchmarking.
Understanding of high-throughput storage networking and distributed client behavior.
Experience with capacity forecasting, observability, alerting, and safe maintenance procedures.
Knowledge of authentication, authorization, encryption, and secure data lifecycle management.

Nice To Haves

Experience supporting large GPU training clusters and high-volume checkpoint workloads.
Experience with S3-compatible object storage, data tiering, or distributed caching.
RDMA-enabled storage or GPUDirect Storage experience.
Kubernetes storage integrations or SLURM environments.
Experience with storage cost optimization.
Contributions to open-source storage systems.

Responsibilities

Build and operate storage systems for frontier AI workloads, ensuring reliable, high-throughput access to datasets, checkpoints, and model artifacts.
Balance performance, durability, availability, and cost as GPU clusters scale.
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 with 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 with explicit 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.
Collaborate with compute and networking teams.