Applied Researcher – Continual Learning

🔥 17 hours ago

🌐 United States, United Kingdom – Remote

🇺🇸 United States – Remote 🇬🇧 United Kingdom – Remote

🏄 California – Remote

🏄 California – Remote

💵 $150k - $300k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

👻 Ghost score 5%

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Prime Intellect

WebsiteLinkedInAll Job Openings

1 - 10 employees

🤖 Artificial Intelligence

☁️ SaaS

Artificial Intelligence • SaaS • Cloud Computing

Prime Intellect is a company focused on democratizing AI development by providing scalable and decentralized computing resources for training models. Their platform allows users to find and share global compute resources, enabling the training of state-of-the-art models through distributed clusters. They promote the collective ownership of AI innovations, including language and scientific models. Prime Intellect also offers a range of GPU options to facilitate affordable and efficient model training. They aim to advance decentralized training research and open-source AI development on a global scale.

📋 Description

• 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. • Determine which improvements belong in 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. • Run post-training experiments against real baselines and deploy what works. • Control update cadence, policy lag, task drift, and exploration. • Evaluate checkpoints on earlier skills, unseen tasks, and general capabilities. • Build reproducible task-generation and training pipelines with versioned data and harnesses. • Select production traces and interventions, convert them into training signal, and establish regression gates. • Build evaluations for retained learning, transfer, regression, and rollback. • Own methods for selective consolidation, replay, adaptation, memory, and online or offline RL. • Lead field experiments, benchmarks, and open research on continual improvement. • Work directly with external collaborators, customers, users, or research partners.

🎯 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.

🏖️ Benefits

• Equity incentives • Flexible Work (San Francisco, New York, London, or remote) • Visa Sponsorship & relocation support • Professional Development budget • Team Off-sites & conference attendance

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