Applied Researcher – Agentic RL

🔥 14 hours ago

🏄 California, New York – Remote

🏄 California – Remote 🗽 New York – 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

• Work on reinforcement learning for agents that reason over long horizons, use tools, coordinate with other agents, recover from failure, and improve from experience • Develop RL and ML systems across algorithms, environments, harnesses, reward and credit-assignment methods, evaluation, and scalable experimental systems • Work with application teams, partners, and open-source users on transferable methods and systems • Turn workflows into tasks, environments, evaluations, and reward signals • Run post-training experiments against real baselines and determine whether gains come from models, data, harnesses, inference, or infrastructure • Build agentic judges and credit-assignment methods for complete trajectories and tool use • Make multi-agent and long-horizon environments train reliably at scale while preserving debuggable traces and comparable baselines • Detect reward hacking, harness behavior, data leakage, and infrastructure artifacts, then redesign experimental loops • Own reusable environments, harnesses, rewards, evaluations, experiments, open-source systems, and papers

🎯 Requirements

• Relevant experience in hands-on RL training, scalable ML systems, agent environments, evaluation, inference/training infrastructure, or research crossing methods and systems • 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, and clear failure attribution • Strong Python skills • Ability to build artifacts that other researchers and engineers can run, inspect, and extend • Comfort owning ambiguous problems and keeping partners and teammates current • Publications, open-source systems, benchmarks, or shipped products demonstrating exceptional work are valued

🏖️ 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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