Power & Performance Engineer
Intel
Power & Performance Engineer
Job Description
Job Details:Job Description: 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. Qualifications: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. Job Type:Experienced HireShift:Shift 1 (Israel)Primary Location: Israel, HaifaAdditional Locations:Israel, Petah-TikvaPosting Statement:All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.Position of TrustN/AWork Model for this RoleThis role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. * Job posting details (such as work model, location or time type) are subject to change.*
Locations
- Haifa, Israel
- Petah-Tikva, Israel
Skills Required
- powerintermediate
- CPU/GPU power managementintermediate
- data analysis using Python and/or SQLintermediate
- PUE/thermal modelingintermediate
Required Qualifications
- Bachelor's, Master's, or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field. (degree in phd in electrical engineering)
- Bachelor's, Master's, or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field. (degree in phd in electrical engineering)
- 5+ years of experience in power, performance, systems optimization, or related areas. (experience, 5 years)
- 5+ years of experience in power, performance, systems optimization, or related areas. (experience, 5 years)
- Deep knowledge of CPU/GPU power management, performance counters, Linux power frameworks. (experience)
- Deep knowledge of CPU/GPU power management, performance counters, Linux power frameworks. (experience)
- Proficiency in data analysis using Python and/or SQL. (experience)
- Proficiency in data analysis using Python and/or SQL. (experience)
- Preferred Qualifications: Experience with PUE/thermal modeling, pod-level capacity planning, AI cluster scaling. (experience)
- Preferred Qualifications: Experience with PUE/thermal modeling, pod-level capacity planning, AI cluster scaling. (experience)
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Partner with facilities, electrical, and the platform-owning team on power distribution, PUE targets, and peak-demand management at pod scale.
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