Staff AI Engineer - Agent Architecture & Behavior

🇺🇸 San Francisco, California
$3K - $3K Annual
Posted 6 days ago
Expires December 15, 2026

Artisan · Full-time · In person in San Francisco

Base salary: $250,000–$325,000 USD annually. Equity: 0.15%–0.30%. US visa sponsorship available.

BUILD SOMETHING NEW AT THE FRONTIER OF APPLIED AI

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.

We're looking for a hands-on technical lead to design and build the underlying AI architecture. You'll work across agent behavior, complex multi-agent systems, tool use, context, and evaluation, taking promising ideas through to dependable production software.

This is an individual-contributor role with broad technical ownership. You'll make consequential architecture decisions, write the hardest parts of the system, and work closely with our existing engineers and leadership. You should enjoy both exploring an uncertain problem and doing the detailed engineering required to make a solution work.

WHAT YOU'LL OWN

- Agent architecture and behavior. Design and implement agent execution loops, planning strategies, tool interfaces, and verification. Turn ambiguous technical requirements into clear system boundaries and working code.

- Multi-agent systems. Build delegation, coordination, context sharing, and result synthesis. Handle concurrent work, conflicting updates, cancellation, and stale results. Establish when a multi-agent approach improves on a simpler baseline.

- Reliable execution. Make complex, stateful workflows resilient to interruptions and partial failures. Build checkpoints, recovery strategies, and appropriate human intervention into the architecture.

- Context, memory, and reusable methods. Improve retrieval, context construction, persistent state, and skill representation. Investigate how systems can use feedback and prior experienc...

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