ML Infrastructure Engineer San Francisco

Graphon is pioneering a new class of Relational Systems Models (RSM) to deliver state-of-the‑art multimodal context, search, and analytics.

We take a different approach from today's compute‑heavy methods by introducing a new, fundamental inductive bias. Just as CNNs provided the bias for spatial in variance (vision), and Transformers provided the bias for sequence‑to‑sequence relationships (language), our RSMs introduce the correct relational bias for long‑context, multimodal reasoning.

Our core technology builds a self‑learned graph structure that is this inductive bias, enabling ML models to operate with state‑of‑the‑art efficiency and contextual accuracy. We have raised a significant seed round from top‑tier VCs and strategic foundation model providers to scale this new paradigm.

We’re a small and experienced team that moves fast and values ownership, pragmatism, and technical excellence. You’ll work directly with the founding team to tackle interesting problems in scaling novel technologies to serve companies we work with across multiple domains.

Our founding team combines deep technical and enterprise expertise: CEO Arbaaz Khan (ex‑Amazon; PhD in Robotics and ML), CTO Clark Zhang (ex‑Meta; PhD in Robotics and ML), and COO Deepak Mishra (ex‑VC; 10+ years of Enterprise Technology experience).

• 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.

• 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.

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