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.
Core Technical Responsibilities
You'll build the distributed control plane and node-level runtime behind our sandbox platform: placing workloads across bare-metal workers, launching secure microVMs, and making the entire lifecycle fast, efficient, and resilient to failure.
Build the control plane that places sandbox workloads efficiently across bare-metal CPU and GPU workers.
Build node-level execution systems that launch and supervise secure microVMs, coordinate storage and networking, and take sandboxes from assignment to ready execution.
Own the sandbox product surface, including its APIs, SDK, and CLI, and evolve it to support reinforcement learning and agentic workflows at scale.
Work closely with research, applied research, customers, and the broader engineering team to take problems from discovery through design, implementation, and production adoption.
Technical Requirements
You have 5+ years of experience building production distributed systems from the ground up.
You have strong systems programming experience. Production Go is a major advantage; Rust, C++, or comparable systems-language experience is also valued.
You are a high-agency engineer who can lead the design and implementation of high-performance, fault-tolerant systems.
You have strong Linux systems fundamentals and depth in at least one of virtualization, networking, storage, or workload isolation.
You enjoy going deep on systems performance, whether the bottleneck is memory behavior, concurrency, networking, or resource utilization.
You have built, deployed, and operated production systems at scale.
What We Offer
Cash compensation of $150-300k with significant equity incentives
Based in our San Francisco office.
Full visa sponsorship and relocation support
A professional development budget for courses and conferences
Regular team off-sites and conference attendance
The opportunity to shape core infrastructure for frontier AI research
Growth Opportunity
You'll work alongside experienced engineers and researchers building core infrastructure for frontier AI research. You'll shape the sandbox platform that powers demanding reinforcement learning, evaluation, and agentic workloads, with opportunities to contribute to open-source infrastructure.
On a small, high-ownership team, you'll help set technical direction and engineering practices while moving quickly from decisions to shipped systems.
We value deep systems expertise and customer obsession. If you want to build foundational infrastructure from the control plane down to the node runtime, we want to talk to you.
Apply now and help build the systems behind the next generation of AI research.