Applied AI Engineer

Engg; San Francisco (CA); On-site; USD 150,000 - 250,000; Full time

Benefits offered by this job

Meaningful early equity

Relocation assistance

Visa sponsorship

Job summary

Engg is seeking an on-site infrastructure engineer to own the seed-stage AI agent infrastructure in San Francisco. You will design and ship systems that let agents execute reliably, retain client context for months, and know when to hand off to a human.

This foundational role has outsized impact from day one. You will build core infra, memory systems, and eval harnesses, own the full loop from development to production, and ship agent software for real customers.

Qualifications

• 3+ years shipping LLM agents that ran unattended in production for real users.
• Hands-on experience building eval harnesses used to make production deployment decisions.
• Experience with retrieval and memory systems that work on real, messy operational data.
• Strong Python proficiency for building production systems end to end.
• Experience with agent orchestration, workflow management, or reliability patterns (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; open-source contributions or a founded technical project is a bonus.

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 from messy 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.

Skills

Python

LLM agents

Eval harnesses

Memory systems

Agent orchestration

Reliability patterns

Open-source contributions

Job description

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 is 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.