Director/Sr. Manager, AI Inference Model Scaling
Cerebras · Sunnyvale, CA
About this role
## About Cerebras Cerebras Systems builds the world’s largest AI chip—56x larger than GPUs—enabling industry-leading training and inference speeds (10x faster than GPU-based hyperscale cloud inference services). This performance leap unlocks real-time iteration and more agentic computation for smarter AI applications. ## 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)**. You’ll help build: - Compiler frontend - Model transformation pipeline - Graph optimization infrastructure - High-performance kernel enablement - Runtime integration The team works across ML frameworks, compiler technologies, distributed systems, hardware architecture, and model optimization—partnering with hardware architects, runtime engineers, cloud platform teams, AI researchers, and strategic customers. ## About the Role Cerebras is seeking an experienced engineering leader to **build and scale the Inference Model Scaling organization**. You will define the technical vision, organizational strategy, and execution roadmap for a globally distributed engineering team enabling the latest foundation models on Cerebras hardware. You will lead engineering for **ML model compilation and optimization** as well as **development of high-performance kernels**, partnering across compiler, runtime, cloud infrastructure, hardware architecture, product management, and AI research. ## Responsibilities ### Technical Leadership - Define the technical roadmap and strategy - Establish technical direction across multiple teams and engineering leaders - Lead design reviews and establish engineering standards - Drive support for emerging LLM architectures and inference workloads ### Team Leadership - Hire, mentor, and grow a high-performing engineering team - Develop future technical leaders and managers - Drive organizational planning, headcount strategy, and investment priorities - Foster an engineering culture focused on execution, quality, and innovation - Scale engineering processes while maintaining execution velocity ### Cross-Functional Collaboration - Partner with Cloud Platform, ML, and Hardware teams for end-to-end service enablement (Cloud and On-Premise) - Work with Product Management to prioritize model enablement and customer needs - Collaborate with customers and solution architects on new model bring-up - Influence future hardware/software co-design through ML model enablement and optimization insights ### Delivery & Execution - Own planning, prioritization, and execution across multiple concurrent initiatives - Balance rapid model support with long-term ML Compiler architecture - Drive predictable delivery for strategic customer commitments ## Required Qualifications - BS, MS, or PhD in Computer Science, Computer Engineering, or related field - 12+ years building compiler, ML systems, or infrastructure software - 5+ years leading engineering teams - Deep experience with modern compiler infrastructure (LLVM, MLIR, XLA, TVM, Torch FX, or similar) - Strong understanding of graph compilation and optimization - Experience with Python and C++ - Experience delivering production-quality software - Strong communication and cross-functional leadership skills ## Preferred Qualifications - Experience building compiler frontends for AI accelerators - Experience supporting PyTorch, JAX, TensorFlow, or ONNX - Experience with LLM inference or tr
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