Applied AI Researcher, Multi-Agent Systems
Develop multi-agent AI architectures for enterprise coordination and collaborative reasoning. Requires research experience in MARL/GNNs, strong prototyping skills, and daily AI tool usage.
About the job
Key Responsibilities
- Design architectures in which multiple agents coordinate to solve problems requiring structured interaction across reasoning processes.
- Build systems that structure communication, route information, and coordinate decision-making across agents.
- Investigate interaction patterns governing agent collaboration, including information exchange, critique of reasoning, and coordination over complex workflows.
- Establish design principles for cohesive, high-performing agent teams where capabilities emerge from interaction.
Requirements
- Experience building or studying multi-agent systems involving structured communication, delegation, critique, or iterative coordination.
- Hands-on work with agent orchestration, communication protocols, evaluator agents, or systems enabling information exchange and decision coordination.
- Research background in related areas such as multi-agent reinforcement learning (MARL), graph neural networks (GNNs), knowledge graphs, or mixed-initiative planning.
- Proven research track record (publications, open-source work, or equivalent).
- Daily use of AI tools (e.g., ChatGPT, Cursor, Perplexity) to accelerate workflows.
- Strong programming and data analysis skills for rapid prototyping and experimentation.
- Bias toward demonstrating practical results over theoretical discussion.
Compensation & Benefits
- Base salary: $150K – $250K
- Meaningful equity
- Comprehensive benefits including 100% covered medical, dental, and vision for employees and dependents
- 401(k) with additional perks (e.g., commuter benefits, in-office lunch)
- Access to state-of-the-art models and generous AI tool usage
- Ownership of high-impact projects at top enterprises
Skills
Multi-Agent Systems, Agent Orchestration, Multi-Agent Reinforcement Learning, Graph Neural Networks, Knowledge Graphs, Communication Protocols, Programming, Data Analysis, Ai Prototyping
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