Graphon AI develops a pre-model intelligence and relational representation layer that maps hidden structural connections across multimodal enterprise data.
<h1 data-path-to-node="3">About Graphon</h1> <p data-path-to-node="3"><strong data-path-to-node="3" data-index-in-node="0">Graphon AI is building the pre-model intelligence layer for the next generation of AI.</strong> We are developing the context engine layer that solves a fundamental challenge: <em data-path-to-node="3" data-index-in-node="167">“What if I could just dump all my data and GPT/Claude/Gemini could smartly reason across it?”</em> 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.</p> <h3 data-path-to-node="4"><strong data-path-to-node="4" data-index-in-node="0">Our Approach</strong></h3> <p data-path-to-node="5">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.</p> <p data-path-to-node="6">Just as <strong data-path-to-node="6" data-index-in-node="8">CNNs</strong> provided the bias for spatial invariance in vision, and <strong data-path-to-node="6" data-index-in-node="69">Transformers</strong> established the standard for sequential dependencies in language, our framework provides the structural foundation needed for complex, interconnected data.</p> <p data-path-to-node="7">We believe the next wave of AI progress will come from better infrastructure around models:</p> <ul data-path-to-node="8"> <li> <p data-path-to-node="8,0,0"><strong data-path-to-node="8,0,0" data-index-in-node="0">Better Memory & Context:</strong> Creating truly persistent, scalable state.</p> </li> <li> <p data-path-to-node="8,0,0"><strong data-path-to-node="8,1,0" data-index-in-node="0">Better Representations:</strong> Moving beyond simple embeddings to relational world models.</p> </li> <li> <p data-path-to-node="8,2,0"><strong data-path-to-node="8,2,0" data-index-in-node="0">Universal Infrastructure:</strong> A way to stop rebuilding the same context layer for every agent, dataset, and application.</p> </li> </ul> <h3 data-path-to-node="9"><strong data-path-to-node="9" data-index-in-node="0">The Team</strong></h3> <p data-path-to-node="10">We are a lean, experienced team that values ownership, pragmatism, and technical excellence. We have raised a significant seed round from top-tier VCs and strategic foundation model providers to scale this new paradigm.</p> <ul data-path-to-node="11"> <li> <p data-path-to-node="11,0,0"><strong data-path-to-node="11,0,0" data-index-in-node="0">Arbaaz Khan, CEO:</strong> ex-Amazon; PhD in Robotics and ML.</p> </li> <li> <p data-path-to-node="11,1,0"><strong data-path-to-node="11,1,0" data-index-in-node="0">Clark Zhang, CTO:</strong> ex-Meta; PhD in Robotics and ML.</p> </li> <li> <p data-path-to-node="11,2,0"><strong data-path-to-node="11,2,0" data-index-in-node="0">Deepak Mishra, COO:</strong> ex-VC; 10+ years of Enterprise Technology experience.</p> </li> </ul> <h1 class="p2">About You<span class="Apple-converted-space"> </span></h1> <ul> <li class="p3">You have (or are about to have) an undergrad/masters/doctoral degree in a related field.<span class="Apple-converted-space"> </span></li> <li class="p3">High agency to pursue and solve problems you encounter.<span class="Apple-converted-space"> </span></li> <li class="p3">A 'right tool for the job' mindset. Loves brainstorming with the team to find the best fix.<span class="Apple-converted-space"> </span></li> <li class="p3">Familiarity with how a modern Machine Learning stack operates.<span class="Apple-converted-space"> </span></li> <li class="p3">Familiarity with standard software skills and tools (version control, basic networking principles, etc.)<span class="Apple-converted-space"> <br><br></span></li> </ul> <p data-start="1345" data-end="1430"><strong data-start="200" data-end="227">Compensation & Benefits</strong></p> <ul> <li data-start="232" data-end="312"><strong data-start="232" data-end="254">Base salary range:</strong> $180,000 – $250,000 (depending on experience and level)</li> <li data-start="315" data-end="410"><strong data-start="315" data-end="336">Meaningful equity</strong> with the opportunity to own a real stake in a category-defining company</li> <li data-start="413" data-end="506"><strong data-start="413" data-end="439">Comprehensive benefits</strong>, including health, dental, vision, and competitive paid time off</li> </ul> <p class="p3"><span class="Apple-converted-space"> </span></p>