Senior Staff AI Accelerator Performance Architect

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 with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. Senior Staff AI Accelerator Performance Architect Wafer-scale computing creates a distinctive architecture space in which compute placement, memory capacity and bandwidth, communication, kernel execution and system-level behavior must be understood together. We are looking for a performance architect to guide the evolution of our next-generation AI systems. You will connect real workloads to architectural behavior, identify the bottlenecks that matter, quantify potential improvements and influence hardware and software roadmaps through rigorous performance analysis. This role is ideal for someone with deep knowledge of hardware architecture, developed through hardware, compiler, kernel or system-performance work, who enjoys operating at the intersection of applications, kernels, architecture and system performance. What You’ll Do Own and evolve performance models and modeling methodologies for next-generation accelerator and system architectures. Build and extend analytical, simulation-based or trace-driven models across workloads, architectural features and product generations. Analyze important AI workloads, from individual kernels through end-to-end inference and training execution, to determine where time, bandwidth, compute and capacity are spent. Identify hardware and software bottlenecks and quantify opportunities to improve latency, throughput, utilization and energy efficiency. Evaluate proposed architectural features and determine their expected performance return across representative workloads. Study how models and kernels map onto the underlying compute, memory and communication architecture. Partner with architecture, compiler, kernel, runtime and systems teams to evaluate alternative mappings and optimizations. Develop workload projections and competitive performance analyses grounded in transparent assumptions. Create concise recommendations that translate complex performance results into architectural and product decisions. Improve modeling methodology, validation and correlation with RTL, emulation and silicon measurements. Help define representative workloads, performance targets and success criteria for future products. What We’re Looking For 7+ years of experience in performance analysis, performance modeling or architecture exploration for CPUs, GPUs, AI accelerators or other high-performance computing systems. Strong understanding of hardware architecture developed through hardware, compiler, kernel, runtime or system-performance work. Experience developing analytical, simulation-based or trace-driven performance models using Python, C++ or similar environments. Solid understanding of processor architecture, memory systems, interconnects, parallel execution and hardware resource constraints. Ability to move between kernel-level behavior and end-to-end application or system performance. Experience profiling workloads, forming performance hypotheses and validating them with quantitative evidence. Understanding of how software mapping and programmability affect realized hardware performance. Ability to communicate modeling assumptions, uncertainty, bottlenecks and recommendations clearly. MS or PhD in Electrical Engineering, Computer Engineering, Computer Science or equivalent practical experience. Particularly