greenhouse
Founding Marketer
Graphon.AI
San Francisco, USA
Full-time
Senior
Hybrid
USD 180000-230000 yearly / year
Discovered 1 weeks ago
Free
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We are hiring our Founding Marketer to define how Graphon is understood in the market and to build the first repeatable go-to-market engine around the product.
We are looking for a hands-on marketing leader who can own the narrative, sharpen the wedge, identify the right initial ICPs, and drive product launches. You should be equally comfortable thinking strategically, writing clearly, building systems, and executing directly in a highly technical environment.
Own the narrative. Turn Graphon’s technical concepts into simple customer language that makes the value obvious.
Define the wedge. Figure out the first ICPs and use cases worth focusing on, and why they should come first.
Run marketing end to end. Take features from alpha to beta to launch with the right messaging, assets, and rollout plan.
Build repeatable systems. Create playbooks, content engines, and processes that scale beyond one-off marketing work.
Partner closely with founders, engineers, and sales to shape positioning based on real product and market feedback.
Stand up the first repeatable demand and pipeline motions across site, content, events, and outbound enablement.
Full Job Posting
About Graphon
Graphon AI is building the pre-model intelligence layer for the next generation of AI. We are developing the context engine layer that solves a fundamental challenge: “What if I could just dump all my data and GPT/Claude/Gemini could smartly reason across it?” Today, this is incredibly difficult due to the limitations of LLM context windows and the fundamental squared scaling inherent to attention mechanisms. While sparse attention and RAG systems attempt to address this, they haven't proven to be robust general solutions.
Our Approach
Pre-model intelligence is our answer. We believe a graph-based approach that analyzes relationships across data on a topological level provides the correct inductive bias for long-context multimodal reasoning.
Just as CNNs provided the bias for spatial invariance in vision, and Transformers established the standard for sequential dependencies in language, our framework provides the structural foundation needed for complex, interconnected data.
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