About the Role
Own the agent infrastructure that helps AI systems execute tasks reliably, retain context, and involve people when needed. You will build and ship production systems, using real-world feedback to improve how agents plan, recover, and perform.
What You'll Do
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Build and maintain infrastructure for reliable agent execution across long-running, multi-step tasks.
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Design memory and retrieval systems that work with messy operational data and preserve useful context over time.
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Develop evaluation harnesses that teams can trust when deciding whether to deploy LLM systems.
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Measure, test, debug, and improve agent systems throughout the development and production lifecycle.
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Collaborate with customers and teammates to iterate on production agents, orchestration, and reliability.
What We're Looking For
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At least 3 years of experience building and shipping LLM agents that run unattended in production for real users.
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Strong Python skills and experience building production systems and agent infrastructure.
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Experience developing evaluation harnesses used to inform production deployment decisions.
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Hands-on background with agent memory, context retrieval for operational data, and orchestration or workflow systems.
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Experience implementing failure recovery, retries, or other production reliability patterns for agents.
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Experience building agent tools, frameworks, or libraries; a technical project taken from idea to production is also relevant.
Compensation & Benefits
Base salary of $150,000 to $250,000 USD annually, plus meaningful early equity. Visa sponsorship is available.
Location
On-site in San Francisco, United States.
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