Software Engineer, Applied AI

Clay Labs•New York, NY

1d•$170,000 - $300,000

About The Position

Clay's product is increasingly powered by AI agents — systems that research, enrich, and take action on behalf of our users, not just generate text. Several teams are working on different layers of this: agents that execute real go-to-market workflows end-to-end, and the shared platform (harness, memory, tools, retrieval, evals) that those agents run on. This role is a shared entry point across those teams. Depending on your background and interests, you'll be matched to a specific team as you move through the process — but every team here is working on the same underlying problem: closing the gap between an agent that looks good in a demo and one that's dependable enough to run unattended in production. You'll work closely with product, research-adjacent teammates, and other engineers to make sure agents aren't just capable, but reliable, steerable, and worth trusting with real work. That means the job isn't only about improving model behavior in isolation — it's about turning those improvements into measurable gains in task completion, reliability, and time saved for the people using them.

Requirements

Experience building or shipping production systems with LLMs or agents — not just prototyping. This might look like prompting and tool-use design, agent orchestration, retrieval, structured extraction, or fine-tuning
Strong backend fundamentals — APIs, databases, distributed systems — since agent features still need to run reliably inside real production infrastructure
Experience with model or agent evaluation: designing evals, measuring regressions, or turning fuzzy quality questions into measurable signals
A systems-and-outcomes mindset — you care about whether the product actually works for users, not just about model metrics in isolation
Comfort debugging messy, real-world failures and a bias toward shipping and iterating quickly in a space where best practices are still being figured out

Nice To Haves

Experience with agent frameworks, tool-calling systems, or retrieval architectures (vector search, hybrid search, RAG)
Experience building or maintaining eval/benchmark infrastructure for LLM-based systems, or running fine-tuning in production
Experience with GTM, sales, or marketing workflows (e.g. lead sourcing, enrichment, audience building)
Familiarity with Clay's stack: React, TypeScript, Python, AWS (Aurora/Postgres, ECS/Fargate, Lambda, OpenSearch, Elasticache/Redis), Terraform, Datadog
A growth mindset — we're building a team that's curious, open-minded, and happy to invest in each other's learning, not just their own

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