Software Engineer, Applied AI

Clay helps organizations turn growth ideas into reality through unique data, signals, and AI research, and its product is increasingly powered by AI agents. The Software Engineer, Applied AI will design, build, evaluate, and improve reliable AI agents and the underlying platform, working across agent products, infrastructure, retrieval, tooling, safety, and production evaluation systems.

Responsibilities:

• Design and iterate on agent behavior across real GTM workflows — for example, sourcing a Total Addressable Market (TAM) list by combining search, audience building, and enrichment into one flow
• Map manual, multi-step workflows that GTM teams do today and turn them into agent-driven flows that are as good as, or better than, a human doing it by hand
• Build and run evals that measure whether an agent actually completed the task correctly — not just whether the output looked plausible — and use them to catch regressions and failure modes
• Analyze real failures in production and systematically improve robustness, not just patch the specific case in front of you
• Work with product to take agent flows from early prototype through closed beta and into general availability, and help define what "good" looks like for each one
• Build the core agent harness that other teams build on top of, including memory systems, tool infrastructure, and retrieval architecture
• Improve agent performance through prompting strategies, tool-use design, and context construction — the layer between "the model can do this" and "the product does this reliably"
• Design guardrails and safety checks so agents behave predictably in production
• Build a cross-surface evals framework so every team building on the platform can measure quality, regressions, and performance the same way
• Build feedback loops that turn real usage and production logs into better prompts, tools, and eval coverage over time
• Support teams building their own forks or variants of the managed agent for their specific use case