Building Open Superintelligence Infrastructure

• Build and maintain Helm charts that compose trainers, inference servers, environment servers, and supporting services into reproducible “Training stacks”
• Develop the Python control-plane agents that watch pods, report run state to the platform, and keep clusters in sync
• Implement scheduling and autoscaling for heterogeneous hardware (H100/H200/B200) using KEDA, LeaderWorkerSet, taints/tolerations, and gang scheduling
• Run a tight GitOps workflow - every change ships through PRs, Helm values, and CI
• Build node-local model caches, checkpoint pipelines, and shared storage for fast cold starts
• Operate the observability stack (Prometheus, Grafana, Loki, DCGM) and make GPU cluster debugging fast

Platform Development

• Build the developer-facing surfaces for hosted training: job submission, live run monitoring, logs, metrics, model/adapter management, comparisons
• Develop FastAPI backend services and REST APIs that bridge the platform to running clusters
• Build real-time monitoring and debugging tools (streaming logs, step-level metrics, failure analysis)
• Ship product UI in Next.js / React / TypeScript with shadcn, Tailwind, tRPC, and TanStack Query

Research Bridge

• Interface with the RL trainer, inference servers, and environment servers running inside our clusters
• Productize new training capabilities (new model architectures, RL algorithms, modes)

Technical Requirements

We’re looking for engineers who are fluent across three areas - you don’t need to be the world’s best at any one, but you should have real depth in all three and a clear point of view on how they connect., * Strong Kubernetes operations experience - Helm, CRDs, operators, KEDA, gang scheduling, GPU operator

• Comfortable debugging real production clusters (kubectl, pod lifecycle, node issues, networking)
• Cloud platform experience (GCP preferred - GCS, GKE, Cloud Run, Cloud Tasks)
• Infrastructure automation (Helm, Terraform, Ansible) and a GitOps mindset
• Observability: Prometheus, Grafana, Loki, OpenTelemetry, DCGM
• Linux fundamentals: networking, namespaces, performance tuning

Programming & Platform

• Strong Python backend development (FastAPI, async, SQLAlchemy)
• Comfortable building Python control-plane agents that talk to Kubernetes APIs
• Modern frontend development (TypeScript, React/Next.js, Tailwind, shadcn) - enough to ship product surfaces end-to-end
• REST and tRPC API design
• Experience building developer tools, dashboards, and live-monitoring UIs

What We Offer

• Cash compensation $150K-$300K with significant equity
• Flexible work arrangement (remote or San Francisco office)
• Full visa sponsorship and relocation support
• Professional development budget for courses and conferences
• Regular team off-sites and conference attendance
• Opportunity to shape the future of decentralized AI development

Growth Opportunity

You’ll join a team of experienced engineers and researchers working on cutting-edge problems in AI infrastructure. We believe in open development and encourage team members to contribute to the broader AI community through research and open-source work.

We value potential over perfection - if you’re passionate about democratizing AI development and have experience in either platform or infrastructure development (ideally both), we want to talk to you.

• Strong working knowledge of the modern AI stack - open model families, finetuning techniques (LoRA, QLoRA, full FT, RLHF/RLAIF), inference engines (vLLM, SGLang, TensorRT-LLM)
• Familiarity with GPU hardware tradeoffs (H100 / H200 / B200, NVLink, interconnects, memory hierarchy) and what they mean for training and inference workloads
• Understanding of distributed training fundamentals (data/tensor/pipeline/expert parallelism, NCCL, multi-node scheduling)
• Awareness of what’s happening at the frontier - new models, training methods, infra patterns - and the ability to translate that into product decisions

Prime Intellect is building the open superintelligence stack - from frontier agentic models to the infrastructure that lets anyone create, train, and deploy them. We aggregate and orchestrate global compute into a single control plane and pair it with the full RL post-training stack: environments, secure sandboxes, verifiable evals, and our async RL trainer. We enable researchers, startups, and enterprises to run end-to-end reinforcement learning at frontier scale, adapting models to real tools, workflows, and deployment contexts.

We recently raised $15M in funding (taking total funding to $20M), led by Founders Fund with participation from Menlo Ventures and prominent angels including Andrej Karpathy (Eureka Labs, Tesla, OpenAI), Tri Dao (Chief Scientist, Together AI), Dylan Patel (SemiAnalysis), Clem Delangue (Hugging Face), Emad Mostaque (Stability AI), and many others.