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.
Apply now and join us in our mission to democratize access to planetary scale computing.
Compensation
Cash compensation range of $150,000–$300,000 plus equity incentives.