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—in healthcare, legal, and finance—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 Data Scientist at Amigo, you'll apply rigorous statistical methodologies to establish the quantitative foundation of our Trust Center Framework. Working as part of our Research team, you'll design measurement systems that validate agent performance with academic rigor, create confidence calculation frameworks across simulation runs, and quantify the composition of our agent architecture. Your work will provide the statistical backbone for our public validation of agent performance and enable evidence-based decision making as we scale from initial customers to millions of conversations per month. This role is critical for establishing the industry standard for autonomous agent validation and providing the quantitative evidence that builds trust in regulated industries.

Responsibilities

• Design statistical frameworks for our Trust Framework that validate agent performance with academic rigor
• Develop mathematical models that quantify trust, reliability, and performance in high-stakes domains
• Create autoscaling algorithms for compute fleets that optimize resource allocation as we scale to 100x volume
• Build projection models for quota growth and capacity planning across multi-workspace, multi-region deployments
• Establish comprehensive state analysis methodologies that measure the composition of agent operations (percentage of identity, constraints, guidelines, data, dynamic behavior in prompts)
• Design systems for context traceability that enable statistical validation of agent reasoning pathways
• Develop confidence calculation methodologies for multiple simulation runs, analyzing variance across scenarios and deployment conditions
• Create mathematical frameworks for judge coverage measurement, ensuring comprehensive evaluation of agent performance
• Implement metrics that tie directly to interpretability, safety, and business value across our agent architecture
• Design attribution systems that identify which components of our architecture (context graphs, memory systems, dynamic behaviors) contribute to performance improvements