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 Foundations Engineer at Amigo, you'll build the data infrastructure that powers our evolutionary chambers, reinforcement learning pipelines, and Trust Framework. Reporting to the ML Infrastructure Lead, you'll create scalable data systems that capture agent interactions, simulation results, and evaluation metrics while supporting our transition from scaffolded architecture to advanced reinforcement learning. You'll design data workflows that enable both research innovation and production reliability as we scale to millions of conversations per month. This role is essential for establishing the data foundation that drives our continuous alignment loop and enables our evolution toward superhuman agent performance.

Responsibilities

• Design and implement data architectures that support our three-layer iterative evolution approach (Problem Model, Judge, Agent)
• Build scalable pipelines for capturing, processing, and analyzing agent interactions across both production and simulation environments
• Create data systems that enable our Trust Center Framework (agi.work) with comprehensive metrics and performance validation
• Develop infrastructure for agent simulations that test thousands of scenarios with perfect reproducibility
• Implement data workflows for reinforcement learning that enable continuous agent improvement
• Build systems for efficient storage, retrieval, and analysis of high-volume agent interactions
• Design and implement real-time data processing for live agent monitoring and performance analysis
• Create data foundations for the neighborhood expansion problem, supporting scientific discovery and validation approaches
• Develop frameworks for experiment tracking, reproducibility, and model versioning
• Implement efficient data structures for context tracking and agent reasoning verification