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About the Role

Prime Intellect is building the open superintelligence stack, providing infrastructure for ambitious AI teams. Their platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. They are developing open frontier AI, including open-source models and a full-stack platform for AI research and development.

Responsibilities

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 to determine improvements for model weights or persistent memory, skills, prompts, and subagent state.
Track skill acquisition, transfer, forgetting, and loss of plasticity over long sequences of tasks.
Turn workflows into tasks, environments, evaluations, and reward signals, running post-training experiments against real baselines.
Determine if gains came from the model, data, harness, inference, or infrastructure, and make reusable parts into shared research, open-source systems, and product capabilities.
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 for tracing and rolling back regressions.
Work directly with 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, and measure tradeoffs across a sequence of tasks.
Build evaluations that distinguish retained learning and transfer from memorization, context accumulation, or measurement drift.
Develop methods for selective consolidation, replay, adaptation, memory, and online or offline RL.
Create evaluations for retention, transfer, regression, and rollback as tasks and distributions change.
Establish promotion gates and human oversight for agents that continue to change in production.
Conduct field experiments, benchmarks, and open research to make continual improvement measurable and transferable.

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, including meaningful baselines, careful trace inspection, clear failure attribution, and willingness to change direction based on evidence.
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, such as publications, open-source systems, benchmarks, and shipped products.
Ability to demonstrate something difficult built, what failed, and how the evidence changed the approach.

Skills

Continual learning
Online learning
Reinforcement Learning (RL)
Meta-learning
Memory systems
Post-training data loops
Adaptive production ML
Evaluation under changing distributions
Model-training
Agent development
Experimental loops
Python

Location

San Francisco
New York
London
Remote

Work Type

Remote
Full-time

Experience Level

Professional

Salary/Compensations

$150-300k

Benefits

Equity incentives
Visa Sponsorship & relocation support
Professional Development budget
Team Off-sites & conference attendance

About the Company

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.
Their platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale.
They are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform their own research team uses to build them.
The next generation of AI companies, enterprises, and research teams 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.
They 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.