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Salary: USD 118,500 - 171,000 / annual
CircleCI is hiring an Agentic Operator for its growth marketing team to build AI-powered infrastructure that accelerates the experimentation cycle. Rather than running experiments sequentially, you'll design and deploy agent pipelines, orchestration layers, and automation systems that let the growth team run more tests in parallel and extract insights faster than traditionally-staffed teams.
You'll own the full experimentation loop: intake, prioritization, instrumentation, execution, and readout—all accelerated through automation. Your core output is reducing the time between hypothesis and signal. Before human resources touch a test, your agents will have already scored it using LLM-evaluated copy variants, synthetic audience testing, and funnel analysis. Only the strongest hypotheses advance to execution.
Key responsibilities include building agent pipelines for concept pre-validation; creating an automated experiment queue that triggers the next test based on real-time signals rather than scheduled meetings; establishing a rigorous experimentation process with falsifiable hypotheses, clean measurement, and defined success metrics; orchestrating the existing stack (Amplitude, HubSpot, Clay, REO.dev) alongside frontier LLMs (Claude, GPT) and workflow tools (n8n, Gumloop); and building infrastructure for parallel experimentation across channels to compress learning cycles without adding headcount.
You'll report to the Director of Demand Generation and work closely with channel owners, lifecycle, product, and sales teams. This is a zero-to-one builder role where the playbook doesn't exist yet—you'll define the process in code.
Requirements:
- Deep understanding of B2B SaaS acquisition funnels, CRO, CAC/LTV, and hypothesis-driven testing; ability to prioritize experiments intelligently and catch flawed hypotheses early
- Proven experience shipping multi-agent workflows in production (not just personal automation scripts); fluency in API-based orchestration, prompt engineering, and self-improving feedback loops
- Track record building internal tooling that other teams depended on
- Systems thinking: ability to identify bottlenecks, design for 10x scale, and build systems that improve over time
- Experience operating in zero-to-one environments where you defined process rather than following playbooks
- Demonstrated experience managing or coordinating multi-stakeholder experimentation programs
- Ability to describe specific experiment cadence improvements you drove (e.g., scaling from 3 to 18 tests/month) and explain exactly what you automated to achieve it
