Senior Staff AI Accelerator Performance Architect

The role

You will guide the evolution of next-generation AI systems by connecting real workloads to architectural behavior. You will own performance models, identify bottlenecks, and influence hardware and software roadmaps through rigorous analysis.

What you will 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.
• Analyze AI workloads to determine time, bandwidth, compute and capacity usage.
• Identify hardware and software bottlenecks and quantify improvement opportunities.
• Evaluate proposed architectural features and determine expected performance return.
• Study how models and kernels map onto compute, memory and communication architecture.
• Partner with architecture, compiler, kernel, runtime and systems teams.
• Develop workload projections and competitive performance analyses.
• Create recommendations that translate performance results into architectural and product decisions.
• Improve modeling methodology, validation and correlation with RTL, emulation and silicon measurements.

Required qualifications

• 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.

Nice to have

• Performance analysis of transformer inference or training workloads.
• Attention, GEMM/GEMV, collective communication, mixture-of-experts, quantization or memory-capacity-constrained execution.
• Kernel optimization, compiler performance, runtime scheduling or distributed accelerator systems.
• Model validation using RTL simulation, emulation, FPGA prototypes or silicon measurements.
• Competitive analysis of AI accelerators and large-scale AI systems.

Benefits

• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open source cutting-edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Job stability with startup vitality.
• Simple, non-corporate work culture that respects individual beliefs.

About the company

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. The company works with leading model labs, global enterprises, and cutting-edge AI-native startups.