[Hiring] Applied Research - Continual Learning @Prime Intellect

Role Description

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale.

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.

Responsibilities

• Turn a workflow into tasks, environments, evaluations, and reward signals.
• Run post-training experiments against real baselines and deploy what works.
• Determine whether gains come from the model or from the data, harness, inference, or infrastructure.
• 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.
• Work directly with external collaborators whose deployments drive the research.

Representative early problems

• Select high-value production traces and interventions, convert them into training signals, and establish regression gates before a change reaches deployment.
• Determine which adaptation belongs in model weights, harness state, memory, tools, or data.
• Build evaluations that distinguish retained learning and transfer from memorization, context accumulation, or measurement drift.

What you will own

• 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.

Qualifications

• 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 skills 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, including publications, open-source systems, benchmarks, and 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.