Join us to help build the next generation of AI hardware solutions. You will be part of a highly skilled, agile team developing cutting-edge hardware for the AI domain, where we push the boundaries of what silicon can do for emerging AI workloads. With a startup-like culture, we move quickly and give engineers the opportunity to drive significant technical and business impact. 

We are continuously developing modern and effective working methods, including hands-on adoption of AI tools throughout the chip development flow.  

Responsibilities: 

• Own pod-level power and performance metrics for large-scale AI AI data center using a modeling and telemetry platform developed by a partner team; collect, validate, and report performance-per-watt, capacity, and utilization metrics across pods. 

• Operate and drive requirements for the modeling/telemetry platform developed by a partner team; provide feedback to improve accuracy and coverage. 

• Aggregate silicon/rack data up to the pod level; reconcile measured vs. modeled metrics and own the pod power/performance budget. 

• Drive power telemetry, capping, and dynamic power management (RAPL, P/C-states, DVFS) to maximize throughput within pod thermal/power envelopes. 

• Build workload characterization and benchmarking pipelines (SPEC, MLPerf, AI workloads) to identify bottlenecks and guide pod capacity planning. 

• Partner with facilities, electrical, and the platform-owning team on power distribution, PUE targets, and peak-demand management at pod scale. 

• Bachelor's, Master's, or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field. 

• 5+ years of experience in power, performance, systems optimization, or related areas. 

• Deep knowledge of CPU/GPU power management, performance counters, Linux power frameworks. 

• Proficiency in data analysis using Python and/or SQL. 

• Preferred Qualifications: Experience with PUE/thermal modeling, pod-level capacity planning, AI cluster scaling.