Applied Research - Continual Learning
$150k - $300k • Remote • San Francisco • FullTime
Posted 1d ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Research Engineer
About the job
Prime Intellect is building an open superintelligence stack, providing infrastructure for AI labs. Their platform, Lab, unifies compute, environments, evaluations, and training for post-training at scale, including SFT, RL, tool use, agent workflows, and continuous model improvement. They are focused on open-source models for long-horizon tasks and the platform their research team uses. The company has raised significant funding from prominent investors and is looking for individuals passionate about building at the intersection of frontier research, infrastructure, and go-to-market strategy for a nascent category.
Responsibilities
- Build continual learning systems that convert agent trajectories, corrections, and failures into new tasks and model updates.
- Compare streaming RL, batched post-training, and distillation to determine optimal placement of improvements (model weights, persistent memory, skills, prompts, subagent state).
- Track skill acquisition, transfer, forgetting, and loss of plasticity over long task sequences.
- Select high-value production traces and interventions, convert them into training signals, and establish regression gates before deployment.
- Determine whether adaptation belongs in model weights, harness state, memory, tools, or data, and measure tradeoffs across tasks.
- Build evaluations to distinguish retained learning and transfer from memorization, context accumulation, or measurement drift.
- Develop methods for selective consolidation, replay, adaptation, memory, and online/offline RL.
- Create evaluations for retention, transfer, regression, and rollback as tasks and distributions change.
- Establish promotion gates and human oversight for continuously changing production agents.
- Conduct field experiments, benchmarks, and open research to make continual improvement measurable and transferable.
- Work directly with external collaborators whose deployments drive research.
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 adaptability based on evidence.
- Strong Python skills and ability to build runnable, inspectable, and extensible artifacts for other researchers and engineers.
- Comfort owning ambiguous problems and keeping partners and teammates informed.
- Demonstrated exceptional work through 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