Researcher (Agent)
Amigo
Posted on May 13, 2025
Researcher (Agent)
To apply, send us your resume and anything else you'd like to careers@amigo.ai
About Amigo
We're helping enterprises build autonomous agents that reliably deliver specialized, complex services—healthcare, legal, and education—with practical precision and human-like judgment. Our mission is to build safe, reliable AI agents that organizations can genuinely depend on. We believe superhuman level agents will become an integral part of our economy over the next decade, and we've developed our own agent architecture to solve the fundamental trust problem in AI. Learn more here.
Role
As a Researcher in Agent Development at Amigo, you'll investigate and enhance the unified Memory-Knowledge-Reasoning (M-K-R) cycle that forms the foundation of our agent architecture. Working as part of our Research team, you'll research methods to increase the bandwidth between these integrated cognitive systems and develop techniques that enable agents to handle complex, long-horizon problems with less external scaffolding. Your work will focus on improving context engines, memory systems, knowledge activation, and reasoning pathways as an integrated whole, driving our Agent V2 architecture toward greater autonomy and reliability. This research is critical for overcoming the token bottleneck limitation and preparing for future neuralese capabilities.
Responsibilities
Research methods to enhance the integration bandwidth between memory, knowledge, and reasoning systems to enable more complex agent capabilities
Develop techniques for dynamic abstraction control that allow agents to seamlessly move between different granularity levels depending on reasoning needs
Design approaches for contextual reframing that transform stored information into optimal configurations for specific tasks
Create frameworks for quantized problem decomposition that break complex challenges into tractable units while maintaining progress toward larger goals
Research methods for optimizing the inner ↔ outer world information flow, including intelligent contextual compression and processing
Develop techniques that reframe reasoning steps as bounded optimization problems with explicit constraints and context
Design and implement checkpointing mechanisms that enable long-horizon reasoning while maintaining coherent progress
Investigate approaches for optimizing latent-space priming and knowledge activation to directly shape problem-space transformation
Collaborate with the Memory team to enhance the memory-reasoning bridge across our layered architecture
Work with the Reinforcement Learning team to improve agent adaptation under evolutionary pressure
Contribute to research publications and the broader field of agent architecture and design
Qualifications
PhD or equivalent research experience in AI, machine learning, cognitive systems, or related fields
Strong understanding of LLM architectures, capabilities, and limitations, particularly the token bottleneck constraint
Experience with agent architectures, reasoning frameworks, or cognitive systems design
Background in developing systems that integrate multiple AI components into cohesive agents
Familiarity with memory systems, knowledge representation, and complex reasoning frameworks
Understanding of optimization approaches and problem decomposition techniques
Strong programming skills with the ability to implement complex agent architectures
Excellent research and analytical skills with the ability to design and run rigorous experiments
Strong communication skills for collaborating across research domains and explaining complex concepts
Passion for building trustworthy AI that can be deployed responsibly in high-stakes domains
Location: NYC (Onsite)
To apply, send us your resume and anything else you'd like to careers@amigo.ai