AI Summary
Founding engineer role owning the agentic infrastructure at an AI infrastructure startup, building systems for reliable agent execution, long-term context retention, and human-in-the-loop workflows. Responsible for designing memory systems, developing eval harnesses, and shipping agent systems directly to production customers. Requires 3+ years shipping LLM agents unattended in production with strong Python and orchestration expertise.
Key Highlights
Founding engineer role owning agentic infrastructure at a well-funded AI startup
Build and maintain core infrastructure enabling agents to execute reliably across dozens of iterations
Design memory systems retaining months of client context from real-world operational data
Develop trusted eval harnesses used for production deployment decisions
Ship agent systems directly to real customers and iterate based on live feedback
Key Responsibilities
Build and maintain core infrastructure that enables agents to execute tasks reliably across dozens of iterations
Design and implement memory systems that retain months of client context extracted from messy, real-world operational data
Develop eval harnesses that teams trust to make production deployment decisions
Own the full loop: build, measure, break, fix, and improve agent systems based on production feedback
Ship agent systems directly to real customers and iterate on them based on live feedback
Contribute across infrastructure, orchestration, customer collaboration, and early hiring as needed
Technical Skills Required
Benefits & Perks
Base salary: $150,000 to $250,000 USD annually
Meaningful early equity stake
Relocation support
Visa sponsorship available
Nice to Have
Open-source agent tooling contributions
Experience with human-in-the-loop or approval workflows
Founded or led a technical project
Job Description
About The Role
You will own the agentic infrastructure at a well-funded AI infrastructure startup, building the systems that let AI agents execute reliably, retain context over months, and know when to involve a human. This is a founding engineer role where your decisions directly shape how agents plan, remember, fail, and recover in production.
What You'll Do
- Build and maintain core infrastructure that enables agents to execute tasks reliably across dozens of iterations.
- Design and implement memory systems that retain months of client context extracted from messy, real-world operational data.
- Develop eval harnesses that teams actually trust to make production deployment decisions.
- Own the full loop: build, measure, break, fix, and improve agent systems based on production feedback.
- Ship agent systems directly to real customers and iterate on them based on live feedback.
- Contribute across infrastructure, orchestration, customer collaboration, and early hiring as needed.
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- 3 or more years shipping LLM agents that ran unattended in production for real users, with a clear understanding that the model is the easy part.
- Proven experience building eval harnesses that were actually used to make production shipping decisions.
- Strong Python proficiency applied to production systems, not prototypes.
- Experience with agent memory management, context retrieval, and orchestration on real operational data (not demo RAG pipelines).
- Hands-on experience with agent orchestration frameworks, workflow management, or infrastructure tooling.
- Experience implementing failure recovery, retry logic, or reliability patterns in production agent systems.
- Bonus: open-source agent tooling contributions, experience with human-in-the-loop or approval workflows, or a founded/led technical project.
- Exceptional intensity and execution, comfortable owning work well beyond a narrow specialty at an early-stage company.
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- Base salary: $150,000 to $250,000 USD annually.
- Equity: meaningful early equity stake.
- Relocation support and visa sponsorship available.
On-site in San Francisco, CA, United States. Relocation support is provided.
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