Prime Intellect - Applied Research

• Applied Research at Prime • We turn a workflow into tasks, environments, evaluations, and reward signals, run post-training experiments against real baselines, and deploy what works. When a result looks good, we find out whether the gain came from the model or from the data, harness, inference, or infrastructure. The reusable parts become shared research, open-source systems, and product capabilities. • Every Applied Research role is externally facing. Most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda. • Role scope • Control update cadence, policy lag, task drift, and exploration, and evaluate each checkpoint on earlier skills, unseen tasks, and general capabilities. Build task-generation and training pipelines with reproducible data and harness versions, so regressions can be traced to a specific change and rolled back. You will work directly with the external collaborators whose deployments drive the research. • Representative early problems • Select high-value production traces and interventions, convert them into training signal, and establish regression gates before a change reaches deployment. • Determine which adaptation belongs in model weights, harness state, memory, tools, or data, then measure the tradeoffs across a sequence of tasks. • Build evaluations that distinguish retained learning and transfer from memorization, context accumulation, or measurement drift. • Methods for selective consolidation, replay, adaptation, memory, and online or offline RL. • Evaluations for retention, transfer, regression, and rollback as tasks and distributions change. • Promotion gates and human oversight for agents that continue to change in production. • Field experiments, benchmarks, and open research that make continual improvement measurable and transferable.