Growth Engineer
AI summary of the role
Giga is hiring a Growth Engineer to build full-funnel content attribution and AI-native content repurposing pipelines.
What you’ll do
- Build content attribution and engagement infrastructure (50%): full-funnel tracking, event instrumentation, engagement modeling, exec dashboards, and feedback loops.
- Develop AI-native content repurposing engine (35%): pipelines to transform each flagship post into ~15 derivative assets with quality control and human approval gates.
- Create programmatic/AEO content infrastructure (15%): generate hundreds of structured pages optimized for SEO and AEO/GEO, measured by indexation, citation rate, and signups.
- Wire Segment-class tooling to warehouse and BI layer for real-time performance insights.
What you’ll bring
- Built production attribution and event pipelines with Segment-class tooling, warehouse, and BI layer.
- SQL fluent, write Python or JS for systems work, comfortable wiring APIs and CMS integrations.
- Shipped AI-native workflows using LLM APIs in production.
- Content instinct: understand why a post matters, not just which event fired.
Technologies
Segment · SQL · Python · JavaScript · LLM APIs · CMS · BI · ChatGPT · Perplexity · Claude
Source and classification
Internal deployment & tooling · Evidence for this classification:
forward, and the kind of momentum that defines generational companies. If being part of that resonates with you, we'd love to hear from you! Voice AI startup Giga raises $61M Series A DoorDash and Giga Partnership About the Role Our focus is on publishing technical content that engineers respect. What we don't have is the infrastructure to measure whether that content drives pipeline, or the systems to turn each flagship post into derivative assets. We're hiring a Growth Engineer to build both — full-funnel attribution, engagement modeling, and AI-native repurposing pipelines. This is not an outbound GTM Engineer role. What You'll Own Content attribution and engagement infrastructure (50%): Full-funnel tracking from content consumption to signup to activation to pipeline. Event instrumentation (Segment-class + warehouse + BI), engagement modeling (time on page, scroll depth,
More from the job description
This role is on-site in San Francisco in the Dogpatch neighborhood. About Giga Giga has recently raised a $61M Series A and is working with Fortune 500 customers to deploy the next generation of customer experience - real-time AI agents that can understand emotion, resolve issues instantly, and scale across the world's largest enterprises. Industry leaders like DoorDash trust Giga with their most complex support and operations workflows across voice, chat, and email, in high-stakes regulated environments where accuracy and compliance matter. We're at an exciting inflection point. While we've found real commercial success, our ambitions are larger: to become the go-to AI platform for all enterprise automation, powered by our voice superintelligence. The work affects millions of people every day, and our team has the autonomy to make true impact - with brilliant founders, a clear path forward, and the kind of momentum that defines generational companies. If being part of that resonates with you, we'd love to hear from you! Voice AI startup Giga raises $61M Series A DoorDash and Giga Partnership About the Role Our focus is on publishing technical content that engineers respect. What we don't have is the infrastructure to measure whether that content drives pipeline, or the systems to turn each flagship post into derivative assets. We're hiring a Growth Engineer to build b [... source excerpt omitted ...] AI Overviews). Performance is measured by indexation, citation rate, and downstream signups. Pages that do not convert are removed rather than archived. Who You Are You've built production attribution and event pipelines — Segment-class tooling wired to a warehouse and BI layer. You're SQL fluent, write Python or JS for systems work, and are comfortable wiring APIs and CMS integrations. You've shipped AI-native workflows using LLM APIs in production, not just prototyped them. You have content instinct: you understand why which post matters, not just which event fired. You translate fuzzy editorial goals into measurable systems. You ship v0 in two weeks and iterate — you don't wai
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