Applied AI Engineer

Build and own agent infrastructure for an AI-driven services company. You will help make agents reliable in production, with durable context, trustworthy evaluations, and clear paths for human intervention.

• Build and maintain infrastructure that helps agents execute tasks reliably across many iterations.

• Design memory and retrieval systems that preserve useful context from messy operational data over time.

• Develop evaluation harnesses that teams can use to make production deployment decisions.

• Measure, test, troubleshoot, and improve agent systems, including failure recovery and retries.

• Ship agent systems to customers and iterate based on production feedback.

• Contribute across infrastructure, orchestration, customer collaboration, and team growth.

• At least 3 years of experience building and shipping LLM agents that ran unattended in production for real users.

• Strong Python skills and hands-on experience building production systems.

• Experience implementing agent memory and context retrieval for real operational data, not only demo datasets.

• Experience building evaluation harnesses used to guide production deployment decisions.

• Experience with agent orchestration, infrastructure, workflow management, or agent tooling.

• Experience implementing production reliability patterns, including failure recovery and retry logic.

• Evidence of technical ownership, such as taking a project from conception to production, founding or leading a technical project, or contributing to open-source agent tooling or LLM infrastructure.

• Early engineering experience at a high-growth or AI-focused startup, experience building complex production systems, or a strong technical education background.

$150,000 to $250,000 USD base salary, plus meaningful early equity. Visa sponsorship and relocation support are available.

On-site in San Francisco, United States.