Prime Intellect
Applied Researcher - Continual Learning
$300K
San Francisco +3 · Full-time · Posted Oct 19, 2026
- Location
- San Francisco
New York
Remote
London - Visa sponsorship
- Visa sponsorship & relocation support
About the job
Build and evaluate continual-learning systems that turn agent experience into model updates, memory, and other adaptations, then measure their effects in changing production workflows.
What you'll do
- Develop learning methods and pipelines from production traces, corrections, and failures; compare adaptation approaches.
- Build evaluations, regression gates, and rollback processes to measure retention, transfer, and performance as tasks change.
- Run field experiments with external collaborators and contribute reusable research, benchmarks, and open-source systems.
What they ask for
- 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, trace inspection, and failure attribution.
- Strong Python skills and ability to build artifacts other researchers and engineers can run, inspect, and extend.
Nice to have
- Publications, open-source systems, benchmarks, or shipped products demonstrating exceptional work.
Pay
$150-300k (base salary)
Tools
Python
AI focus
Research and develop continual-learning systems for agents, including online or offline RL, memory, adaptation, and evaluation of learning over long sequences of tasks.
Model-written summary · the original posting is authoritative