Applied AI Engineer for Production-Grade Agents

Clay; New York (NY); On-site; USD 170,000 - 300,000; Full time

Job summary

Clay is seeking an engineer to help scale AI agents that research, enrich, and act for users. You’ll be matched to a team and work across agent products and platform infrastructure to push reliable, measurable improvements in GTM workflows.

You’ll collaborate with cross-functional teams to design evals, improve prompts and tooling, and ensure production-grade reliability and safety. This role offers opportunities to shape how AI-driven workflows scale across complex use cases.

Qualifications

• Experience building or shipping production systems with LLMs or agents, not just prototyping.
• Strong backend fundamentals—APIs, databases, distributed systems for production reliability.
• Experience with model or agent evaluation: designing evals and measuring regressions.
• A systems-and-outcomes mindset focused on user impact beyond model metrics.
• Bias toward shipping, quick iteration, and robust handling of real-world failures.

Responsibilities

• Collaborate with product and research-adjacent teammates to ensure agents are reliable, steerable, and trustworthy.
• Help turn agent improvements into measurable gains in task completion and time saved for users.
• Design and implement agent products and platform features that enable production-scale use.
• Build and refine the core agent harness, memory, tool infrastructure, and retrieval architecture.
• Develop guardrails and safety checks for production behavior.

Skills

Production systems with LLMs/agents

Backend fundamentals

Model/agent evaluation

Systems-and-outcomes mindset

Shipping fast in uncertain space

Tools

React

TypeScript

Python

AWS

Job description

Clay is seeking an engineer to help scale AI agents that research, enrich, and act for users. You’ll be matched to a team and work across agent products and platform infrastructure to push reliable, measurable improvements in GTM workflows.

You’ll collaborate with cross-functional teams to design evals, improve prompts and tooling, and ensure production-grade reliability and safety. This role offers opportunities to shape how AI-driven workflows scale across complex use cases.