# Staff AI Engineer - Agent Architecture & Behavior at Artisan

- Company: Artisan
- What the company does: Artisan automates your outbound with an all-in-one, AI-first platform powered by AI employees. Get better outbound sales results with an AI BDR. Backed by Y Combinator.
- Company website: https://artisan.co/?utm_source=ycombinator
- Type: Startups (AI role)
- Level: Senior
- Location: San Francisco
- Work setup: On-site
- Pay: $250K to $325K base salary per year (USD)
- Posted: 2026-09-13
- Apply by: 2026-10-28
- Apply: https://jobs.ashbyhq.com/artisan/4e533b35-d89d-4801-89d2-a77f8b4d5290
- Page: https://www.1752.vc/careers/jobs/artisan-staff-ai-engineer-agent-architecture-and-behavior/

## About the role

At Artisan, we're working on a new, ambitious project that will push the boundaries of what agentic AI can do. We're keeping the product details private ahead of launch, but we can tell you this: the technical problems are substantial, the scope for invention is real, and this hire will shape the core technology.

## What they're looking for

- You have personally built and shipped a substantial agentic system. Production use or rigorous, reproducible open-source work matters more than the name of a framework or employer
- You have deep practical experience with LLM tool use, planning, context engineering, and evaluations. You have implemented multi-agent coordination or substantial parallel agent/tool execution and can explain its failure modes
- You have hands-on experience with browser or computer automation in an agentic system, including observing state, verifying effects, and recovering when an interface or execution path fails
- You are an excellent software engineer in Python, TypeScript, or a comparable language. You are comfortable with asynchronous services, state machines, persistence, concurrency, retries, and cancellation
- You know which decisions belong to a model and which guarantees must be enforced in code. You can reason carefully about permissions, untrusted inputs, uncertain external outcomes, and human approvals
- You can design meaningful experiments, debug real system behavior, and explain what improved, why it improved, and where the evidence is still weak

Tags: Engineering