3+ years of experience in GTM Engineering, workflow automation, or a technical RevOps role — ideally at a high-growth, AI-native B2B company
You’ve shipped production agents on Vercel — build custom internal GTM apps and agents on Vercel, with durable workflows and sandboxed execution, so what you ship runs reliably in production instead of as one-off scripts
Genuine fluency with AI coding assistants and agent tooling (Claude Code, Cursor, Codex, or similar) — you're actively building with these tools in production, not just experimenting
Hands-on experience with Clay as a primary enrichment and workflow tool — you have opinions about how to use it well and you've pushed it to its limits
Direct experience with Salesforce — you know the data model, you've built on it, and you're comfortable owning CRM-level changes
A track record of owning end-to-end AI and automation workflows, not just contributing to or consuming them
Proficiency with integration and automation platforms (n8n, Zapier, Make, Workato, or similar) and a fundamental understanding of APIs and webhooks
Strong judgment on the build decision — you know when low-code gets you 80% of the way there and when it becomes the bottleneck, and you act accordingly
Experience in high-growth B2B technology companies
Familiarity with enrichment and data-provider ecosystems beyond Clay
Comfort building reporting and alerting on a data warehouse (BigQuery, Databricks) and BI layer (Sigma, Hex) — not as a data engineer, but as a consumer and builder on top of the data layer
Experience building with agent platforms (Gumloop, n8n, Notion Agents, etc.) and/or agent frameworks (Vercel AI SDK, Claude Agent SDK, Mastra, etc.)
Experience with highly technical software sales cycles and complex GTM motions
An instinct for stack consolidation — you've inherited a messy tool environment before and helped bring it toward something more intentional without breaking what works
Systems thinker — you naturally break problems into composable parts, building solutions with separation of concerns so they're reusable, extensible, and easy to maintain
Comfortable with a little sprawl — you're not paralyzed by ambiguity or imperfect tooling. You know how to experiment, build fast, and clean up as you go
Challenging work and exposure to a variety of ML startups