Growth Engineer

This role is on-site in San Francisco in the Dogpatch neighborhood.

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

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.

• 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, return visits, paths), exec dashboards, and feedback loops into editorial so the team knows what's working within a week of publishing.

• AI-native content repurposing engine (35%): Pipelines transform each flagship engineering post into approximately 15 derivative assets, including threads, LinkedIn posts, email excerpts, video cutdowns, SEO variants, and podcast clips. The system incorporates automated processes, content management, and human approval gates. Quality control ensures derivatives maintain consistent voice, are routed to the correct channels, and are measured against the flagship post's performance.

• Programmatic / AEO content infrastructure (15%): Hundreds of structured pages are generated from a canonical content layer, including comparison pages, use cases, integrations, and answer-engine explainers. The tech stack is optimized for SEO as well as AEO/GEO (including ChatGPT, Perplexity, Claude, and Google AI Overviews). Performance is measured by indexation, citation rate, and downstream signups. Pages that do not convert are removed rather than archived.

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 wait eight weeks for perfect.

Growth or marketing engineering at content-led B2B SaaS (Ramp, Vercel, Linear, Attio, Webflow, Retool, Notion, Stripe). Analytics engineers who moved into marketing. Founders of content/attribution SaaS that didn't work out. Technical marketing ops leaders ready to build instead of administer.

Share a link to the hardest project you've built and include a short paragraph on why was it hard.