GTM Engineer
Builds and owns AI agents and automations for go-to-market workflows, analyzes revenue performance across sales, marketing, and CS, and delivers data-backed strategic recommendations. Requires 3-5 years in RevOps or GTM engineering with hands-on Clay and AI tool fluency.
GTM Strategy & Insights
- Analyze GTM performance across Sales, Marketing, and CS — surface what's working, flag anomalies, and deliver opinionated recommendations to the revenue org
- Own agent design and AI-enabled development — build and maintain a structured, data-driven agentic universe with clear iteration and roadmap management
- Use AI tools and agents to surface insights, recommend GTM strategic moves, and design systems and process around an AI-enabled GTM team
- Design measurement frameworks that connect outbound activity to pipeline to ARR — ensuring every play is trackable and every dollar is attributable
- Partner with GTM leadership and cross-functional leaders on strategic planning, audience design, and performance reviews
Agent Ownership & GTM Automation
- Own Agentic Go-to-Market workflows as an innovator, pioneer, and advocate, pushing for new ways to implement new workflows within the GTM organization
- Own Clay as a platform — lead enrichment strategy, maintain and scale playbooks, manage credit ROI, and drive best practice adoption across the team
- Drive agent-activation within Dust — build workflows, knowledge bases, and automations that make the revenue org faster, more consistent, and more informed
- Expand the use of AI-native tooling (Claude, OpenAI, and emerging GTM tools) across research, personalization, signal detection, and lead scoring
- Own and continuously improve the leads engine — identifying high-signal triggers, building enrichment workflows, and improving targeting precision over time
- Evaluate and implement new AI tools that push Scribe's GTM capabilities forward
What Makes You a Great Fit
- 3–5 years experience in Revenue Operations, GTM Engineering, Finance, or other equivalent roles
- Hands-on Clay experience — you know how to build tables, manage enrichment strategy, think about credit efficiency, and translate data into targeting logic
- Fluency with AI-native GTM tools — Claude, Cursor, OpenAI, Dust, or equivalent; you're not a tourist in this space and can self-serve in these areas
- Consult on GTM Best Practices — Bring a consultant's lens to diagnosing GTM problems: structured thinking, clear outputs, and a bias toward recommendation over observation
- GTM strategist mindset — you've done growth, sales strategy, consulting, or RevOps and you bring structure and recommendations, not just reporting
- Strong data interpretation skills — you can look at funnel data, call out anomalies, and tell a story about what the numbers mean for the business
- Self-directed and high-output — you identify the gap and go after it; you don't need to be told what to do next
- Comfortable building from scratch — ambiguity doesn't slow you down
Nice to have:
- Experience with Salesforce or HubSpot
- SQL, Sigma, and Snowflake familiarity
- Background in B2B SaaS, ideally PLG or hybrid GTM models
Compensation
San Francisco-based candidates: $150,000 – $175,000 + equity
GTM Knowledge Engineer
Build and deploy AI agents and knowledge systems that automate seller workflows and reduce friction in quote-to-cash processes. Requires 4+ years experience with at least 1 year building AI agents, plus proficiency in JavaScript/Python and commercial SaaS workflows.
GTM Knowledge Engineer
Build and deploy AI agents and knowledge systems that automate seller workflows and reduce friction across quote-to-cash processes. Requires 4+ years experience with hands-on AI agent development, JavaScript/Python proficiency, and B2B SaaS workflow expertise.