Stack Ai
Sales Development Representative
2mo ago
94000 –170000 USD / yearUSAMiddleRemotelinkedincold callingemail
Generate qualified pipeline through outbound prospecting and inbound follow-ups for AI-focused SaaS product.
Responsibilities
- We’re hiring a Sales Development Representative (SDR) to generate qualified pipeline through outbound prospecting and inbound follow-ups. As an early non-engineering hire, you’ll help shape our go-to-market motion and messaging, and play a fundamental role in onboarding potential customers.
Requirements
- 2+ years in BDR/SDR or sales (B2B SaaS; AI/GenAI a plus ). Strong track record sourcing pipeline; enterprise experience preferred.
- Comfortable learning technical concepts and translating GenAI into clear business value.
- Proactive, organized, iterative, creative, adaptable, and enjoy testing messaging and improving processes.
- Existing Fortune 500 network is a plus, not required.
Other
- Prospect & outreach : Identify and engage target accounts via email, LinkedIn, cold calling, organizing events (led by you), and attending conferences.
- Qualify & coordinate : Run first-call discovery, identify decision makers, book meetings for AEs, and identify potential use cases for prospects.
- Build relationships : Nurture champions and connect with decision makers.
- Stack AI is a no-code drag-and-drop tool to quickly design, test, and deploy AI workflows that leverage Large Language Models (LLMs), such as ChatGPT, to automate any business process.
- Our core value is to make it extremely easy to build arbitrarily complex AI pipelines using a visual interface that allows you to connect different data sources with different AI models.
- Our customers use Stack AI to build applications such as:
- Chatbots and Assistants : AI agents that interact with users, answer questions, and complete tasks, using your internal data and APIs.
- Document Processing : apps to answer questions, summarize, and extract insights from any document, no matter how long.
- Answer Questions on Databases : connect GPT-like models to databases (such as Notion, Airtable, or Postgres) and ask questions about them.
- Content Creation : generate tags, summaries, and transfer styles or formats between documents and data sources.