Senior Agentic AI Engineer

About Eigen Labs

Eigen Labs is building the coordination engine for a world run by humans and AI agents alike. We are at an inflection point where AI agents are becoming economic actors, capable of writing code, coordinating work, generating revenue, and operating with increasing autonomy. However, the current production environment lacks the trust needed for agents to handle high-stakes operations involving money, coordination, or ownership. We believe that what's missing is trust, and crypto provides the necessary primitives for agentic companies: identity, execution guarantees, attribution, ownership, and capital.

EigenLayer introduced restaking for programmable, shared cryptoeconomic security. EigenCloud extends this foundation into a developer platform for verifiable compute, data, and execution, forming the trust layer for the age of AI. EigenCloud ensures that execution is verifiable, contributions are attributable, and outcomes can be trusted and rewarded. In 2026, we are shifting from infrastructure to opinionated, user-facing systems, building quickly and working closely with top builders to shape this emerging category.

The Role

As a Senior Agentic AI Engineer at Eigen, you will work on the core systems that make AI agents actually usable in production. You should have experience building real things like distributed systems, APIs, and developer tools, and possess deep knowledge of agents, LLM pipelines, or autonomous workflows. This role is for someone who not only uses AI tools but also has strong opinions on the future direction of AI. You will own significant parts of the system and are expected to move quickly from idea to working product, spending most of your time building rather than managing or discussing roadmaps.

This is a full-time, remote-friendly role, with a preference for candidates in U.S. time zones and close collaboration with the Seattle-based team.

What You Will Do

• Build agent runtimes and orchestration systems (planning, tool use, memory, coordination).
• Make agents reliable (retries, failure handling, state management).
• Make agents observable (tracing, debugging, evaluation).
• Make agents cheap (cost-aware execution, performance optimization).
• Make agents useful in production (not demos - real systems people depend on).
• Integrate LLMs, APIs, and external data into coherent, working systems.
• Define how developers build, debug, and extend agent behavior.

What You’ll Bring

We value evidence of your capabilities more than years of experience. Show us that you have built challenging, functional systems.

Strong Signals

• You’ve shipped real systems that people depend on.
• You’ve transformed ambiguous problems into working products.
• You’ve built or deeply explored agent systems, LLM pipelines, or automation beyond simple demos.
• You understand system behavior in production, including failure modes, tradeoffs, and reliability.
• You move fast and iterate, without waiting for perfect specifications.

Technical Foundation

• Strong backend / systems experience (distributed systems, APIs, infrastructure).
• Proficiency in at least one core language (Python, Go, TypeScript, Rust, etc.).
• Experience with reliability patterns (state, retries, observability).
• Comfort working across the stack when needed.

Nice To Have

• Experience with agent frameworks (LangChain, AutoGen, CrewAI, or custom systems).
• Experience building multi-agent or orchestration systems.
• Familiarity with LLM evaluation, prompting, or performance tuning.
• Experience with workflow engines (Temporal, Airflow, Dagster).
• Experience building developer platforms, SDKs, or internal tools.
• Exposure to retrieval systems, vector databases, or memory architectures.
• Interest in crypto, distributed systems, or verifiable compute.