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Senior GTM Engineer

AssemblyAI - San Francisco, CA, USA - In-office - posted 2026-08-28

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AssemblyAI is hiring its first Senior GTM Engineer to own the end-to-end go-to-market systems stack. The company processes 2M+ hours of audio daily, serves 600M+ monthly inference calls, and powers 2B+ end-user experiences with best-in-class Voice AI models. With under 100 employees and ~$500K ARR per employee, AssemblyAI is one of the most capital-efficient AI companies, operating as a true meritocracy with minimal bureaucracy. In this role, you'll sit at the intersection of Sales, Marketing, and Data, building systems that help a lean team perform like one three times its size. You'll own the GTM stack end-to-end: Salesforce, enrichment platforms, outbound automation, lead routing, scoring, and AI agents that automate work traditionally done by sales ops teams. Key responsibilities include: owning Salesforce, sequencing tools, and integrations between them; writing production code daily (scripts, internal tools, small services); building AI workflows for prospecting, account research, lead scoring, and personalization; turning product usage data (BigQuery, Looker) into actionable seller signals; automating territory building and account targeting; maintaining funnel data integrity; and prototyping quickly to measure what works. You'll need 4+ years in GTM/sales engineering, growth engineering, RevOps with heavy build responsibility, or software engineering with GTM exposure. Strong SQL and Python are essential, along with solid software engineering fundamentals (clean code, systems thinking, version control, debugging). You should be fluent with AI-assisted development tools like Claude Code as a core part of your workflow, have hands-on Salesforce depth (objects, flows, routing, API-level integration), real experience putting LLMs into production workflows, and familiarity with the modern GTM stack (Clay, sequencers, warehouse-native tooling). A builder's mindset with operator's judgment—scoping for business impact, shipping iteratively, and measuring results—is essential.

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