Applied AI Engineer

Clera

$150K — $250K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 3+ years of experience with LLM agents in production environments.
  • Hands-on experience building evaluation harnesses for deployment decisions.
  • Proven expertise with retrieval and memory systems in real operational contexts.
  • Strong proficiency in Python for end-to-end production system development.
  • Experience in agent orchestration and workflow management, especially with reliability patterns.
  • Background in building agent tools or libraries is advantageous.
  • Familiarity with human-in-the-loop systems is a plus.

Responsibilities

  • Build and maintain core infrastructure for reliable agent task execution.
  • Design memory systems to store extensive client context from real data.
  • Develop trustworthy evaluation harnesses for production decisions.
  • Manage the end-to-end process: create, measure, troubleshoot, and improve systems.
  • Implement agent systems for actual clients and iterate based on feedback.
  • Collaborate across various teams in infrastructure, orchestration, and hiring as the team expands.

Benefits

  • Early equity opportunities for significant ownership stake.
  • Relocation assistance available for moves to San Francisco.
  • Visa sponsorship offered for eligible candidates.
Full 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 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.

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