ML Infrastructure Engineer

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.

We believe the next wave of AI progress will come from better infrastructure around models:

• Better Memory & Context: Creating truly persistent, scalable state.

• Better Representations: Moving beyond simple embeddings to relational world models.

• Universal Infrastructure: A way to stop rebuilding the same context layer for every agent, dataset, and application.

The Team

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.

• Arbaaz Khan, CEO: ex-Amazon; PhD in Robotics and ML.

• Clark Zhang, CTO: ex-Meta; PhD in Robotics and ML.

• Deepak Mishra, COO: ex-VC; 10+ years of Enterprise Technology experience.

About You

Required

• Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Robotics, or a related technical field (or equivalent practical experience).

• Strong software engineering fundamentals with experience building production systems (Python required; experience with C++/Go a plus).

• Experience working with machine learning infrastructure, such as training pipelines, inference/serving systems, data pipelines, or model deployment.

• Familiarity with modern ML stacks (e.g., PyTorch/JAX, GPUs, distributed training or inference).

• Solid understanding of core systems concepts (version control, Linux, basic networking, CI/CD).

• High agency and ownership mindset—you identify problems, propose solutions, and drive execution.

• Pragmatic, “right tool for the job” thinker who enjoys collaborating and iterating quickly.

Nice to Have

• Experience scaling ML systems in production environments.

• Experience with distributed systems, large-scale data processing, or GPU optimization.

• Exposure to multimodal ML, retrieval systems, or graph-based representations.

• Prior startup experience or comfort operating in ambiguous, fast-moving environments.
  
  

Compensation & Benefits

• Base salary range: $180,000 – $250,000 (depending on experience and level)
• Meaningful equity with the opportunity to own a real stake in a category-defining company
• Comprehensive benefits, including health, dental, vision, and competitive paid time off