Member of Technical Staff - Agents

Building Open Superintelligence Infrastructure
Prime Intellect is building the open superintelligence stack - from frontier agentic models to the infra that enables anyone to 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 $15mm in funding (total of $20mm raised) led by Founders Fund, with participation from Menlo Ventures and prominent angels including Andrej Karpathy (Eureka AI, Tesla, OpenAI), Tri Dao (Chief Scientific Officer of Together AI), Dylan Patel (SemiAnalysis), Clem Delangue (Huggingface), Emad Mostaque (Stability AI) and many others.

Role Impact

This is a hybrid position spanning the quick and iterative development of AI agents and frameworks and advancing the underlying infrastructure. You’ll focus on:

1. Building the next-generation AI agents to handle workload management and automation.

2. Building out the underlying agent infrastructure that power different kinds of agents.

3. DevRel + Content: Create technical content to drive adoption, engage communities for collaboration and feedback, and coordinate across teams to align technical and product goals.

Core Technical Responsibilities

Rapid AI Agent Development

• Prototype & Iterate: Build and refine AI agents for workload management, automation, and real-time decision-making.

• Framework Integration: Contribute to and extend agent frameworks to handle evolving feature requests and performance needs.

• Experimental Features: Quickly explore new agent capabilities (e.g., multi-agent collaboration, memory architectures) to guide design choices.

Agent Infrastructure & Backbones

• Scalable Deployments: Architect and maintain systems that support distributed AI agent execution at scale.

• Networking & Coordination: Implement robust protocols for data exchange and coordination across distributed agents.

• System Observability: Leverage monitoring tools (Prometheus, Grafana) to track agent performance and resource utilization.

• Resource Orchestration: Manage containerized environments (Kubernetes, Docker) and automate infrastructure (Terraform, Ansible) for seamless agent operations.

Collaboration & Community Engagement

• Cross-Team Alignment: Work closely with product, research, and other engineering teams to identify key improvements and ensure agent features match real-world needs.

• DevRel & Content: Produce clear technical documentation, guides, and demos, driving open-source adoption and helping new users onboard quickly.

• Open-Source Contributions: Engage with the community by pushing code, merging pull requests, and resolving issues, championing decentralized AI best practices.

Technical Requirements

Agent & Platform Skills

• Python (FastAPI, Async): Proficiency in building agent logic, REST APIs, and backend services.

• Pytorch

• TypeScript/React/Next.js: Comfortable creating dashboards or other visualizations for agent monitoring.

• Real-time & WebSocket Systems: Experience building streaming or live-updating UIs to visualize agent activity.

• API Design: Ability to craft intuitive and efficient interfaces for agent communication.

Infrastructure Skills

• Rust: Systems programming for high-performance agent components.

• Kubernetes, Docker: Container orchestration and production-ready deployments.

• Infrastructure automation and config management.

• Cloud Experience: Familiarity with scalable and cost-effective infrastructure.

Nice to Have

• GPU/ML Infrastructure: Understanding how to optimize agent training or inference on GPUs.

• Advanced AI/ML Knowledge: Familiarity with popular model architectures and training workflows.