Member of Technical Staff - Storage Infrastructure

Prime-Intellect; San Francisco (CA); On-site; USD 150,000 - 300,000; Full time

Job summary

Prime Intellect is building the open superintelligence stack, delivering a full‑stack platform for frontier AI workloads. You will own and operate the storage systems feeding datasets, checkpoints, and model artifacts, balancing performance, durability, and cost as GPU clusters scale.

The role covers designing architectures, deploying parallel storage, and optimizing data paths for large-scale AI training and inference, with opportunities to influence end-to-end data lifecycles and security

Qualifications

• 3+ years building or operating production distributed storage systems.
• Hands-on experience with parallel or distributed filesystem or object storage platforms (Lustre/BeeGFS/Ceph/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, replication and recovery.

Responsibilities

• Design and operate storage architectures for training datasets, checkpointing and inference artifacts.
• Deploy and tune parallel filesystems, object storage, and NVMe caching for AI workloads.
• Benchmark throughput, latency, and metadata performance for training workloads.
• Build provisioning, capacity planning, lifecycle management, and automation for storage services.
• Test replication, recovery, backup, and durability targets.
• Diagnose performance and reliability issues across components.

Skills

Distributed storage

Linux administration

Scripting (Python/Go)

Performance tuning

Tools

Lustre

BeeGFS

Ceph

GPFS

Job description

Own Your Intelligence

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.

Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.

Role Impact

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.

Core Technical 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 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

Technical Requirements

Required Experience

• 3+ years 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 in Python, Go, Bash, or similar languages

• Understanding of storage failure modes, data integrity, consistency, replication, and recovery

Infrastructure Skills

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

• NVMe/SSD performance, filesystem 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

Nice to Have

• 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

Growth Opportunity

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.

We value expertise and customer obsession - if you're passionate about building reliable, high-performance GPU infrastructure and have a track record of successful large-scale deployments, we want to talk to you.

Compensation

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