Clera
GTM Engineer
About this role
Build the growth infrastructure for an early-stage AI infrastructure company. Own the end-to-end growth data layer, automate outbound campaigns, run PLG experiments, and connect the entire stack through APIs and automation.
What you'll do
- Design and maintain the growth data layer, funnel instrumentation, and company-wide dashboards serving as source of truth
- Build lookalike models and automated outbound campaigns using enrichment providers and LinkedIn playbooks
- Run experiments across PLG channels (content, integrations, backlinks, paid) and measure impact on signups
- Connect analytics platforms, CRMs, enrichment tools, and outbound systems via APIs, webhooks, and automation
- Evaluate and manage the growth tech stack, deciding what tools to adopt or remove
- Develop and ship AI-powered internal tools with governance and adoption strategies
What they're looking for
- Python, TypeScript, and SQL scripting
- Data pipeline design and ETL via APIs/webhooks
- Growth data layer architecture and instrumentation
- A/B testing and PLG growth experimentation
- Lookalike modeling and outbound campaign automation
- Analytics and dashboard platforms (PostHog or similar)
- AI-powered tool development and prototyping
- CRM and enrichment tool integration
Benefits
- Salary: $120,000–$200,000 USD annually
- Early-stage equity
- On-site role in San Francisco, Los Angeles, or New York City
- Ownership of critical growth infrastructure
- Opportunity to shape a new AI infrastructure market
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Clera
Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.
View all jobs at CleraLikely interview questions
- Walk us through a growth data layer you've built end-to-end—what metrics did you instrument and how did the dashboards influence company decisions?
- Describe an experiment you ran in a PLG channel that significantly moved signups. How did you set it up, measure it, and iterate?