GTM Engineer

About the role

Sift is hiring a builder at the intersection of AI, automation, and go-to-market execution. Our platform is trusted by the most demanding engineering teams in aerospace, defense, energy storage, and robotics. The people buying and using Sift move fast, operate in high-stakes environments, and expect the tools around them to keep up.

Responsibilities

• 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.

• Translate ideas from the team into working systems. Know when to use an AI model, when to use a rule, and when to use nothing at all. Build systems where AI output is reliable enough to trust in production.

• Build and maintain the vector database infrastructure that powers Sift's GTM AI Operating System, from ingestion pipelines to embedding models to retrieval logic.

• Collaborate with Ops to manage integrations across Slack, HubSpot, Clay, and other marketing tools in our stack. Keep systems clean, documented, and scalable as the business grows.

• Work directly with the marketing team and BDRs to understand workflow bottlenecks, identify automation opportunities, and ship solutions that actually get used.

• Help prioritize and sequence what to build next. Maintain a clear view of what is in production, what is in progress, and what is next. Surface tradeoffs and bring recommendations to leadership.

• Maintain and improve what you ship. Build systems designed to last. Monitor pipeline performance, catch failures before the team does, and improve workflows as needs change or data quality shifts.

Requirements

• You write Python: You know Python well enough to build, debug, and own your own scripts and pipelines without help. You are comfortable with APIs, data manipulation, and working in a GitHub repo. You can read someone else's code and improve it.

• You have worked with vector databases: You understand how embedding-based retrieval works and have built or maintained a RAG pipeline in production. You know how to chunk documents, choose an embedding model, evaluate retrieval quality, and debug when the wrong context comes back. Experience with Pinecone, Weaviate, pgvector, or similar is expected.

• You have shipped real automation: Not configured a Zapier template. Built a system with inputs, outputs, failure handling, and real users depending on it. You can walk through exactly what you built, what broke, and how you fixed it.

• You build with AI in production: You understand prompt engineering, API integration, and output validation. You know what AI is good at and where it fails. You have built workflows where the AI output feeds into something real, and you know how to make that reliable.

• You know the GTM context: You understand what a BDR needs to find a good lead, what slows down a sales cycle, and what makes a piece of enablement actually useful in a deal. You have worked near a marketing or revenue team before and do not need the basics explained.

• You are self-sufficient: You can go from a vague idea to a working prototype without a spec document or a dedicated engineering team. You ask the right questions, fill in the gaps, and ship.

• You work well with non-technical teammates: You can explain what you are building and why it matters without making people feel lost. You listen before you build and check in before you assume.

• You are curious and current: You follow the AI tooling space closely, bring new ideas to the team, and have opinions about what is worth using and what is hype.