Director/Sr. Manager, AI Inference Model Scaling

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. Sunnyvale, CA or Toronto, Canada (Hybrid) About the Team The Inference Model Scaling team enables state-of-the-art foundation models and generative AI workloads to run efficiently on Cerebras' Wafer-Scale Engine (WSE). We build the compiler frontend, model transformation pipeline, graph optimization infrastructure, high-performance kernel enablement, and runtime integration that together make next-generation AI models execute with industry-leading performance. The team works at the intersection of machine learning frameworks, compiler technologies, distributed systems, hardware architecture, and model optimization. We collaborate closely with hardware architects, runtime engineers, cloud platform teams, AI researchers, and strategic customers to rapidly bring new model architectures into production. About the Role We're looking for an experienced engineering leader to build and scale our Inference Model Scaling organization. You will define the technical vision, organizational strategy, and execution roadmap for a globally distributed engineering team responsible for enabling the latest foundation models on Cerebras hardware. You will lead the engineering organization responsible for ML model compilation and optimization as well as de

Department: Software; Team: Software