Software Engineer, Applied AI

The posting, as Clay Labs wrote it

ABOUT CLAY

AI is unleashing the biggest wave of company creation in history. Clay's mission is to be the engine those companies use to grow to their full potential.

Clay predicts the next best action for your business and then helps you take it. We started by aggregating the best data for B2B companies. Then, we built the infrastructure to run any personalized campaign on top of it, including emails, ads, landing pages, and workflows that make reps more productive. Now, we're building agents that can help grow your company for you. We're already helping thousands of customers — including Anthropic, OpenAI, Google, and Visa — go to market with unique data, signals, and AI research.

In 2026, we raised a $115M Series D https://www.linkedin.com/safety/go/?url=https%3A%2F%2Fwww.nytimes.com%2F2026%2F09%2F09%2Fbusiness%2Fdealbook%2Fclay-ai-fundraising.html%3Fsmid%3Durl-share%26unlocked_article_code%3D1._1A.E5rN.MeYsbgEsUKbB&urlhash=XS4q&mt=4bpSf4a8omAKuAHqAqZx6C11QpbLK_QwIhSV7gSf4Wf9aywPF_KBSiX9IkWE1AhXBEjPH8mK6fAEN7CMFlpvo0lPAtKRjs-dhgzYqHiVKUMCRtw0jScFgxWCRRfYs4HCX0YL9L6PCQz-edBLinvRdU_TaVUL2dA3&isSdui=true&lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3BoQmIsufoRJm7X2HC5Rqd%2Fw%3D%3D at $7.1B Valuation led by Wellington — and are on track to cross $200M in revenue this quarter. We also launched a $1M Scholarship fund for GTME Education.

Some things to know about us

• Our community http://community.clay.com includes 17,000+ customers, 200+ integration partners, 125+ agencies, 50+ Clay clubs https://luma.com/claylive, and 30k members on Slack.
• Our culture https://nextplayso.substack.com/p/spotlight-clay is unique inside and outside of work. Our team members are also DJs, activists, writers, clowns, marathoners, skydivers, psychedelic therapists, social workers, and more.
• All employees can work with world-class coaches who specialize in creativity, management, and more.
• Our operating principles — including negative maintenance and non-attached action — guide our work. Read more about them here https://cdn.prod.website-files.com/61477f2c24a826836f969afe/685d83a71452245cc1129791_4d770abfd83e276ec15315a2e06945bd_Clay2025_OperatingPrinciples.pdf.
• Read about us in the NYT https://www.nytimes.com/2025/08/05/business/dealbook/clay-ai-marketing-fundraise.html, Forbes http://google.com/search?q=forbes+clay&rlz=1C5OZZY_enUS1155US1155&oq=forbes+clay&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQABiABDIHCAIQABiABDIHCAMQABiABDIHCAQQABiABDIHCAUQABiABDIHCAYQABiABDIHCAcQABiABDIHCAgQABiABDIHCAkQABiABNIBBzkzM2owajSoAgOwAgHxBVAe8UAxJx_p&sourceid=chrome&ie=UTF-8, First Round Review https://review.firstround.com/podcast/inside-clays-unconventional-path-to-1-25b/, and more https://www.clay.com/press.

Hear from our employees directly on our Glassdoor https://www.glassdoor.com/Overview/Working-at-Clay-EI_IE9850794.11,15.htm page!

ABOUT THE TEAM

Clay's product is increasingly powered by AI agents — systems that research, enrich, and take action on behalf
[…]
eval architecture

• Improve agent performance through prompting strategies, tool-use design, and context construction
• 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

WHAT YOU'LL BRING

• Experience building or shipping production systems with LLMs or agents. This might look like multi-step agent orchestration, prompting and tool-use design, retrieval, structured extraction, or fine-tuning.
• Strong backend fundamentals in APIs, databases, distributed systems.
• 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, Clickhouse, 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