Applied AI Engineer

Clera

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

Qualifications

  • 3+ years experience deploying LLM agents with real users in production environments.
  • Proven track record of building eval harnesses for production decisions.
  • Strong proficiency in Python for production systems, not just prototypes.
  • Experience managing agent memory and context retrieval on actual operational data.
  • Hands-on with agent orchestration frameworks and workflow management tools.
  • Experience with implementing reliability patterns and failure recovery in agents.
  • Bonus for contributions to open-source projects, experience with workflow approvals, or leading technical projects.

Responsibilities

  • Build and maintain infrastructure for reliable agent task execution.
  • Design systems for retaining client context from messy operational data.
  • Develop trusted eval harnesses for production deployment decisions.
  • Oversee the full lifecycle of agent systems from building to improvement based on feedback.
  • Ship systems to customers and iterate based on real-time feedback.
  • Contribute to various aspects of infrastructure, customer collaboration, and early hiring.

Benefits

  • Equity stake in a well-funded startup.
  • Relocation support available.
  • Visa sponsorship for eligible candidates.
Full 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.
What We're Looking For
  • 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.
Compensation & Benefits
  • Base salary: $150,000 to $250,000 USD annually.
  • Equity: meaningful early equity stake.
  • Relocation support and visa sponsorship available.
Location

On-site in San Francisco, CA, United States. Relocation support is provided.

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