Applied Researcher - Continual Learning

Prime Intellect; Oregon; Hybrid; USD 150,000 - 300,000; Full time

Benefits offered by this job

Cash compensation: $150k–$300k +equity

Flexible work locations (SF, NY,London

Visa sponsorship & relocation support

Professional development budget

Team off-sites & conference attendance

Job summary

Prime Intellect, Inc. is seeking researchers to build continual learning systems that convert agent trajectories, corrections, and failures into new tasks and model updates.

You will compare streaming RL, batched post-training, and distillation to determine whether improvements belong in model weights, memory, prompts, or subagent state. Your work will focus on evaluating retention, transfer, and plasticity across long task sequences, and you will collaborate with external partners to deploy

Qualifications

• 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 related experimental loops.
• Strong experimental judgment: meaningful baselines, trace inspection, failure attribution, and willingness to adapt.
• Strong Python and ability to build runnable artifacts for researchers/engineers.
• Comfort owning ambiguous problems and keeping partners updated as you resolve them.
• Evidence of exceptional work: publications, open-source systems, benchmarks, and shipped products.

Responsibilities

• Translate agent trajectories, corrections, and failures into new tasks and model updates.
• Compare streaming RL, batched post-training, and distillation to decide where improvements belong (weights, memory, prompts, or subagent state).
• Build task-generation and training pipelines with reproducible data and harness versions.
• Collaborate with external collaborators whose deployments drive the research.
• Develop evaluations for retention, transfer, regression, and rollback as tasks/distributions change.
• Create field experiments, benchmarks, and open research that measurably improve and transfer across setups.

Skills

Continual learning/online learning

Reinforcement learning

Memory systems

Post-training data loops

Adaptive production ML

Evaluation under distribution shift

Python

Tools

Python

Job description

Own Your Intelligence

Role Impact

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.

Responsibilities

Applied Research at PrimeWe 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.

Role scope

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.

Representative early problems

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.

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.

Requirements

• 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 partners and teammates current as you resolve them.
• Evidence of exceptional work: We value publications, open‑source systems, benchmarks, and shipped products as evidence of exceptional work. Show us something difficult you built, what failed, and how the evidence changed your approach.

Benefits and Perks

• 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

If you're excited about the opportunity to build the foundation for the future of decentralized AI and create a platform that empowers developers and researchers to push the boundaries of what's possible, we'd love to hear from you.