Member of Technical Staff - Storage Infrastructure

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.

Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them.

The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.

You'll build and operate the storage systems that feed frontier AI workloads. Own reliable, high-throughput access to datasets, checkpoints, and model artifacts, balancing 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 file systems, 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, file systems, and devices

Implement access controls, tenant separation, quotas, monitoring, and runbooks; collaborate with compute and networking teams

3+ years building or operating production distributed storage systems

Hands-on experience with at least one parallel or distributed file system or object storage platform, such as 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

Block, file, and object storage semantics and their performance tradeoffs

NVMe/SSD performance, file system tuning, I/O profiling, and benchmarking

High-throughput storage networking and distributed client behavior

Capacity forecasting, observability, alerting, and safe maintenance procedures

Authentication, authorization, encryption, and secure data lifecycle management

Experience supporting large GPU training clusters and high-volume checkpoint workloads

S3-compatible object storage, data tiering, or distributed caching

RDMA-enabled storage or GPUDirect Storage experience

Kubernetes storage integrations or SLURM environments

Storage cost optimization and contributions to open-source storage systems

You’ll work directly with customers pushing the boundaries of AI, from startups training foundation models to enterprises deploying massive inference infrastructure. You’ll collaborate with our world-class engineering team while having direct impact on systems powering the next generation of AI breakthroughs.

Cash compensation range of $150,000–$300,000 plus equity incentives.