AI Engineer

Catena Labs is building the first AI-native financial institution to provide the new financial infrastructure and services AI systems need to conduct commerce. We’re giving AI agents the tools to identify themselves and use money safely and efficiently. At the same time, we’re providing businesses and consumers a trusted, licensed financial partner with the capability to manage risk and compliance. By building a financial institution with AI for AI, we are addressing the legacy financial system barriers to creating an AI-driven economy.

Backed by leading venture investors, we’re assembling a team of engineers, designers, data scientists, and finance professionals working together to build a stronger, more resilient global economy that creates broad prosperity. We’re looking for people with the talent, curiosity, and passion to invent an entirely new category of financial services.

What You’ll Work On

• Autonomous Agentic Workflows: Build agent-to-agent protocols where digital workers coordinate, make decisions, and execute financial transactions securely.

• Purpose-Built AI Agents: Design and develop specialized agents tailored to handle specific tasks, such as supporting risk management, ensuring compliance, or even creating other AI agents.

• Advanced Agent Design: Use expertise in AI agent development, including evaluation methods, system design, and prompt engineering, ensuring each agent is equipped with the right tools and clear instructions on when and how to use them.

• Digital Workforce Primitives: Build the foundational elements for how agents will pay and get paid in an autonomous future, with human-in-the-loop when appropriate.

• Agentic Identity, Memory & Reputation: Identity and memory architecture that helps agents build reputation, learn from the past and reason in context.

• Cross-Language SDKs and Dev Tools: Interfaces for developers building in agent ecosystems like MCP, A2A, LangChain, CrewAI, Vercel AI, Pydantic and beyond, across multiple languages like TypeScript and Python.

• Behavioral Evaluation Pipelines: Systems for testing, tuning, and continuously improving agent behaviors and decision-making.

A Few Signals We Look For

• Experience leveraging AI tools effectively to multiply productivity, streamline processes, and enhance decision-making.

• Experience shipping scalable agent-based or LLM-integrated systems in production environments.

• Familiarity with, and frequent use of, modern AI tools and frameworks, such as MCP and A2A, with a finger on the pulse of the industry.

• Clear understanding of how to structure agents with short and long-term memory, as well as when and how to create a separation of concerns.

• Deep interest in how and why identity and economic agency intersect in AI systems.

• A track record of navigating ambiguity, proposing architecture, and building systems that scale.

• Curiosity about what makes AI agents safe, useful, and autonomous, and the drive to improve that every day.

• Comfort with fast-paced environments where 0→1 thinking is not a phase — it’s the job.