To apply, send us your resume and anything else you'd like to [email protected]

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 Safety at Amigo, you'll develop advanced safety systems that ensure our agents operate reliably and align with human values and enterprise objectives. Working as part of our Research team, you'll create architectures that integrate safety into the core Memory-Knowledge-Reasoning (M-K-R) cycle rather than applying isolated filters. Your work will focus on building contextual safety mechanisms that adapt to specific situations while maintaining natural conversational flow. You'll also research how safety systems must evolve as we transition toward neuralese and higher bandwidth reasoning. This research is fundamental to our alignment-first design principle and critical for deploying AI in high-stakes domains.

Responsibilities

• Design multi-layered safety architectures that integrate safety at all levels of our agent framework (context graphs, dynamic behaviors, functional memory, etc.)
• Develop methods for contextualizing safety rather than applying simple filters, ensuring safety emerges from well-orchestrated M-K-R integration
• Research techniques for global guidelines that embed foundational safety protocols at the structural level of context graphs
• Create dynamic safety behaviors that adapt precisely to each situation's unique requirements
• Develop real-time monitoring systems that analyze conversations for suspicious signals or concerning patterns
• Research methodologies for red-lining boundaries that establish clear areas requiring strict protocols or human escalation
• Build frameworks for post-processing safety analysis that detects subtle patterns across multiple sessions
• Design audit systems with comprehensive trails across all safety mechanisms
• Research how safety systems must evolve as we transition toward neuralese capabilities and higher bandwidth reasoning
• Systematically measure and benchmark safety across different architectural approaches
• Collaborate with other research teams to ensure safety is integrated throughout our platform
• Contribute to research publications and the broader field of AI safety and alignment