Applied Researcher - Continual Learning

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Its platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale. The company is building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research. Role impact: You will build continual learning systems that turn agent trajectories, corrections, and failures into new tasks and model updates. Your experiments will track skill acquisition, transfer, forgetting, and loss of plasticity over long sequences of tasks. Every Applied Research role is externally facing, and most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda.

Responsibilities

Turn workflows into tasks, environments, evaluations, and reward signals; Run post-training experiments against real baselines and deploy what works; Attribute gains to the model, data, harness, inference, or infrastructure; Build continual learning systems that turn agent trajectories, corrections, and failures into new tasks and model updates; Compare streaming RL, batched post-training, and distillation; Determine which improvements belong in model weights or persistent memory, skills, prompts, and subagent state; Control update cadence, policy lag, task drift, and exploration; Evaluate each checkpoint on earlier skills, unseen tasks, and general capabilities; Build task-generation and training pipelines with reproducible data and harness versions; Select high-value production traces and interventions and establish regression gates before deployment; Build evaluations that distinguish retained learning and transfer from memorization, context accumulation, or measurement drift; Work directly with external collaborators whose deployments drive the research; Own methods for selective consolidation, replay, adaptation, memory, and online or offline RL; Own promotion gates and human oversight for agents that continue to change in production; Run field experiments, benchmarks, and open research on continual improvement

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 a willingness to change direction when the evidence changes; Strong Python and the 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 such as publications, open-source systems, benchmarks, or shipped products

Benefits

Cash Compensation Range of $150-300k + equity incentives; Flexible Work (San Francisco, New York, London, or remote); Visa Sponsorship & relocation support; Professional Development budget; Team Off-sites & conference attendance