Software Engineer, Applied AI

Our mission is to help organizations turn any growth idea into reality.

We see growth as a creative practice not a formula. Finding and reaching your best-fit customers takes unique ideas and constant iteration. As AI makes execution faster and tactics easier to copy creativity is the only lasting advantage. Were already helping thousands of customers including Anthropic Notion Google and Ramp go to market with unique data signals and AI research.

In 2025 we raised a $100M Series C backed by world-class investors including Sequoia CapitalG and First Round and crossed $100M in revenue.

In 2026 we announced our second employee tender offer in 9 months at a new $5B valuation. We also launched a community equity round for our customers agency partners and club members.

Some things to know about us:

• Our community includes 11000 customers 150 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 for free 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!

Clays product is increasingly powered by AI agents systems that research enrich and take action on behalf of our users not just generate text. These arent lightweight copilots layered onto an existing product; theyre 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 youll 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 thats dependable enough to run unattended in production.

Youll work closely with product research-adjacent teammates and other engineers to make sure agents arent just capable but reliable steerable and worth trusting with real work. That means the job isnt only about improving model behavior in isolation - its about turning those improvements into measurable gains in task completion reliability and time saved for the people using them.

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

• 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

• 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 Clays stack: React TypeScript Python AWS (Aurora/Postgres ECS/Fargate Lambda OpenSearch Clickhouse Elasticache/Redis) Terraform Datadog

• A growth mindset - were building a team thats curious open-minded and happy to invest in each others learning not just their own

Required Experience:

IC