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Salary: USD 150,000 - 300,000 / annual
Prime Intellect is building an open superintelligence stack—infrastructure that frontier AI labs typically build internally, now available to every ambitious AI team. The platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale, from supervised fine-tuning and reinforcement learning to tool use, agent workflows, and continuously improving production models.
As an Applied Research Scientist focused on Continual Learning, you will build systems that turn agent trajectories, corrections, and failures into new tasks and model updates. You will compare streaming RL, batched post-training, and distillation approaches to determine which improvements belong in model weights versus persistent memory, skills, prompts, or subagent state. Your experiments will track skill acquisition, transfer, forgetting, and loss of plasticity over long sequences of tasks.
You will control update cadence, policy lag, task drift, and exploration, evaluating each checkpoint on earlier skills, unseen tasks, and general capabilities. You will build task-generation and training pipelines with reproducible data and harness versions so regressions can be traced to specific changes and rolled back. You will work directly with external collaborators whose deployments drive the research agenda.
Key responsibilities include: selecting high-value production traces and interventions to convert into training signals and establishing regression gates before changes reach deployment; determining which adaptation belongs in model weights, harness state, memory, tools, or data, then measuring tradeoffs across task sequences; building evaluations that distinguish retained learning and transfer from memorization, context accumulation, or measurement drift; owning methods for selective consolidation, replay, adaptation, memory, and online/offline RL; creating evaluations for retention, transfer, regression, and rollback as tasks and distributions change; establishing promotion gates and human oversight for agents that continue to change in production; and conducting field experiments, benchmarks, and open research that make continual improvement measurable and transferable.
Every Applied Research role at Prime Intellect is externally facing. Most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda. When a result looks good, the team determines whether the gain came from the model, data, harness, inference, or infrastructure, and the reusable parts become shared research, open-source systems, and product capabilities.
REQUIREMENTS:
- Relevant experience in continual or online learning, RL, meta-learning, memory systems, post-training data loops, adaptive production ML, or evaluation under changing distributions
- Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops
- Strong experimental judgment: meaningful baselines, careful trace inspection, clear failure attribution, and willingness to change direction when evidence changes
- Strong Python and ability to build artifacts that other researchers and engineers can run, inspect, and extend
- Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them
- Evidence of exceptional work valued through publications, open-source systems, benchmarks, and shipped products
