Join Us in Shaping the Future of AIAt Prime Intellect, we are pioneering the development of an open superintelligence infrastructure. Our focus is on creating a comprehensive stack that spans from advanced agentic models to the foundational infrastructure required for anyone to create, train, and deploy these models. We are aggregating and orchestrating global computing resources into a unified control plane, complemented by a complete post-training reinforcement learning (RL) ecosystem that includes environments, secure sandboxes, verifiable evaluations, and an asynchronous RL trainer. Our mission is to empower researchers, startups, and enterprises to implement end-to-end reinforcement learning at the cutting edge, adapting models to real-world tools, workflows, and deployment contexts.Recently, we secured $15 million in funding (bringing our total to $20 million) led by Founders Fund, with support from Menlo Ventures and notable contributors such as Andrej Karpathy (Eureka AI, Tesla, OpenAI), Tri Dao (Chief Scientific Officer of Together AI), Dylan Patel (SemiAnalysis), Clem Delangue (Hugging Face), Emad Mostaque (Stability AI), among others.Your Role and ImpactAs a key player in this customer-focused role, you will operate at the intersection of cutting-edge RL/post-training techniques, applied data, and autonomous systems. Your contributions will directly influence how advanced models are aligned, evaluated, deployed, and utilized in practical applications by:Enhancing Agent Capabilities: Innovate and iterate on next-generation AI agents designed to handle real workloads, including workflow automation, complex reasoning tasks, and large-scale decision-making. Utilize applied data from real deployments to continuously improve policies, enhance reasoning, and bolster reliability and safety.Developing Robust Infrastructure: Create distributed systems, evaluation pipelines, and coordination frameworks that ensure agents operate reliably, efficiently, and at scale. Design workflows for data capture, processing, and versioning that facilitate feedback, model traces, and reward signals.Acting as a Bridge Between Customers and Research: Transform customer insights and needs derived from applied data into precise technical specifications that guide both product development and research priorities. Collaborate closely with RL and evaluation teams to ensure real-world signals inform model alignment and reward shaping.Prototyping in Real-World Scenarios: Rapidly design and deploy agents, evaluations, and harnesses alongside clients to validate solutions. Leverage applied evaluation data to refine model performance and uncover new capabilities.Collaborative Engineering with CustomersWork closely with customers to gain a profound understanding of workflows, challenges, and opportunities, enabling you to deliver tailored solutions that meet their specific needs.
Oct 27, 2025
