Software Engineer, Applied AI

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 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 includes 17,000+ customers, 200+ integration partners, 125+ agencies, 50+ Clay clubs, and 30k members on Slack.
• Our culture 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.
• Read about us in the NYT, Forbes, First Round Review, and more.

Hear from our employees directly on our Glassdoor page!

About the Team

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. These aren't lightweight copilots layered onto an existing product; they're long-horizon agents built to take on the kind of multi-step, judgment-heavy work that skilled GTM teams spend real time on today. 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.

About the Role

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.

What You'll Do

Depending on the team, you might work on:

Agent products

• Design and iterate on agent behavior across real GTM workflows. For example, sourcing a Total Addressable Market (TAM) list, which in practice means navigating ambiguous Ideal Customer Profile (ICP) definitions, reconciling conflicting signals across data sources, and making judgment calls that experienced analysts spend real time on.
• 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.
• 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.

Agent platform & infrastructure

• 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
• 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