Teams at Prime Intellect

Build post-training and agent systems for public-sector and regulated environments. Develop private improvement loops, permissioned tool-using agents, high-assurance evaluations, and deployable model updates within strict data and infrastructure boundaries. Conduct experiments, inspect traces, establish meaningful baselines, attribute failures, and collaborate directly with customers and research partners. The role requires hands-on model-training or agent experimentation, strong Python skills, sound research judgment, and comfort solving ambiguous problems.

Build environments, agents, evaluations, reward systems, and post-training methods for quantitative research and systematic decision-making. Develop realistic market and portfolio simulations, train long-horizon research agents, assess out-of-sample robustness, and create production feedback loops with monitoring and failure attribution. Collaborate directly with customers and research partners while maintaining a substantive research agenda.

Build environments, evaluations, agent systems, and post-training methods for embodied robotics in collaboration with an external robotics team. Train and evaluate agents using reinforcement learning, imitation learning, world modeling, planning, simulation, and multimodal perception. Own long-horizon agents, sim-to-real evaluations, reward systems, and training loops using offline and online feedback while accounting for safety and hardware constraints.