Applied Research - Continual Learning

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 - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them.

The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.

Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators - including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, Semi Analysis, Lang Chain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, Open Router, Standard Intelligence, Fleet, Core Auto, and more.

We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.

You will build continual learning systems that turn agent trajectories, corrections, and failures into new tasks and model updates. You will compare streaming RL, batched post-training, and distillation, and determine which improvements belong in model weights or persistent memory, skills, prompts, and subagent state. Your experiments will track skill acquisition, transfer, forgetting, and loss of plasticity over long sequences of tasks.

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.

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.

• 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.

• Relevant experience: 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…