GTM Engineer
Build and deploy AI agents and automated workflows that power marketing execution, programmatic outbound, and pipeline generation at scale. Requires CS background, hands-on LLM/agent experience, and GTM intuition.
GTM Engineer
Builds AI-powered automation and infrastructure for GTM teams, including vector databases, pipelines, and integrations with tools like HubSpot and Slack. Requires strong Python, production AI experience, and vector DB expertise to enhance sales and marketing workflows.
140k – 180kMarina del Rey, CASan Francisco, CAGTM EngineeringHybrid
About the role
Responsibilities
- Architect and build AI-powered GTM systems: design and ship automation pipelines across outreach, competitive intel, sales enablement, inbound, events, content, and reporting.
- Own the AI infrastructure and code that makes the GTM team faster and more precise.
- Write and maintain production code: build and own Python scripts, APIs, and integrations that connect data sources, AI models, and GTM tools into reliable workflows.
- Push and maintain work in GitHub. Document builds for extensibility, including vector database infrastructure (ingestion pipelines, embedding models, retrieval logic).
- Lead AI workflow development: translate ideas into working systems, deciding when to use AI models, rules, or nothing.
- Build and own the knowledge infrastructure: design and maintain vector database storing institutional knowledge as embeddings for GTM.
- Own ingestion pipelines from various data sources, define chunking strategies, embedding models, and retrieval logic.
- Contribute to the GTM tech stack: manage integrations across Slack, HubSpot, Clay, and other marketing tools.
- Collaborate across marketing and sales to identify workflow bottlenecks and ship solutions.
- Drive the AI roadmap: prioritize, sequence builds, surface tradeoffs to leadership.
- Maintain and improve shipped systems: monitor performance, handle failures, adapt to changes.
Requirements
- Strong Python skills: build, debug, own scripts and pipelines; comfortable with APIs, data manipulation, GitHub.
- Experience with vector databases (e.g., Pinecone, Weaviate, pgvector): built/maintained RAG pipelines, chunking, embedding models, retrieval evaluation/debugging.
- Shipped real automation systems with inputs/outputs, failure handling, real users.
- Production AI experience: prompt engineering, API integration, output validation.
- GTM context knowledge: understand BDR needs, sales cycles, enablement utility; worked near marketing/revenue teams.
- Self-sufficient: prototype from vague ideas without specs.
- Work well with non-technical teammates: explain builds clearly.
- Curious and current on AI tooling.
Compensation
- Salary: $140,000 - $180,000 per year, plus bonus, equity, and benefits.
Skills
PythonVector DatabasesRAGPineconeWeaviatePgvectorGitHubAIPrompt EngineeringHubSpotSlackClay
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