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 help build our hosted training platform - the product that lets users launch LoRA and full fine-tuning runs on managed GPU clusters with a single API call or a few clicks. The role spans the developer-facing platform and the underlying Kubernetes-based training infrastructure that runs the jobs.

Core Technical Responsibilities

Hosted Training Infrastructure

Design and operate Kubernetes-based training and inference orchestration across multi-cluster, multi-cloud GPU fleets

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.

AI & GPU Landscape

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)