Member Of Technical Staff

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