Applied AI Engineer
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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 from messy, real-world operational data.
- Develop eval harnesses that teams actually trust to make production shipping decisions.
- Own the full loop: build it, measure it, break it, fix it, and make it learn.
- Ship agent systems for real customers and iterate directly based on production feedback.
- Contribute across infrastructure, orchestration, customer collaboration, and early hiring as the team grows.
What they're looking for
- 3+ years shipping LLM agents that ran unattended in production for real users, with a clear understanding that the model is the easy part.
- Hands-on experience building eval harnesses that were actually used to make production deployment decisions.
- Experience with retrieval and memory systems that work on real, messy operational data, not demo RAG pipelines.
- Strong Python proficiency for building production systems end to end.
- Experience with agent orchestration, workflow management, or reliability patterns such as failure recovery and retry logic.
Nice to have
- Experience building agent tooling, frameworks, or libraries is a strong plus.
- Experience with human-in-the-loop or approval workflow systems is a plus.
- Track record of intensity and execution: you force things into existence; open-source contributions or a founded technical project are a bonus.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
About the Role
You will own the agentic infrastructure at a seed-stage AI startup building the foundational layer for AI-native service operations. This means designing and shipping the systems that let agents execute reliably in production, retain months of client context, and know when to hand off to a human. This is a foundational engineering role at a very early team, with outsized scope and impact from day one.
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 from messy, real-world operational data.
Develop eval harnesses that teams actually trust to make production shipping decisions.
Own the full loop: build it, measure it, break it, fix it, and make it learn.
Ship agent systems for real customers and iterate directly based on production feedback.
Contribute across infrastructure, orchestration, customer collaboration, and early hiring as the team grows.
What We're Looking For
3+ years shipping LLM agents that ran unattended in production for real users, with a clear understanding that the model is the easy part.
Hands-on experience building eval harnesses that were actually used to make production deployment decisions.
Experience with retrieval and memory systems that work on real, messy operational data, not demo RAG pipelines.
Strong Python proficiency for building production systems end to end.
Experience with agent orchestration, workflow management, or reliability patterns such as failure recovery and retry logic.
Experience building agent tooling, frameworks, or libraries is a strong plus.
Experience with human-in-the-loop or approval workflow systems is a plus.
Track record of intensity and execution: you force things into existence; open-source contributions or a founded technical project are a bonus.
Compensation & Benefits
Base salary: $150,000 to $250,000 USD annually.
Meaningful early equity.
Relocation assistance provided.
Visa sponsorship available.
Location
On-site in San Francisco, CA, United States.
Company
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