SoC Power Modeling Engineer - AI/ML & Energy Efficiency

Apple Inc.; San Diego (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Apple Inc. in California seeks an experienced SoC power modeling engineer within the Silicon Engineering group to model power dissipation across SOC components and optimize power efficiency. You will collaborate with architecture, SOC design, and system teams to define power targets and improve silicon-level performance.

Responsibilities include correlating models to silicon data, working with labs and validation teams, and delivering scalable energy-efficient IPs for AI/ML and mobile SoCs.

Qualifications

• Bachelor's degree in a relevant field with 10+ years of industry experience.
• MSEE preferred and strong knowledge of SOC architecture and power modeling.
• Deep understanding of CPU, GPU, caches, memory and media IPs in SOCs.
• Experience with scripting and data analysis.
• Familiarity with VLSI design flow and CMOS technology.
• Ability to model thermal control loops and throttling mechanisms.
• Strong teamwork and communication skills.

Responsibilities

• Model power dissipation at the SOC level across CPUs, GPUs, and AI accelerators.
• Model power for customer use-cases.
• Define power targets with system designers and silicon teams.
• Correlate models to silicon data with lab and validation teams.

Skills

Power modeling

Scripting & data analysis

SOC architecture knowledge

CPU/GPU/Memory/Media IPs knowledge

Thermal control loops modeling

Communication skills

Team collaboration

VLSI/CMOS familiarity

EE background

Education

Bachelor's degree in a relevant field

MSEE preferred

Job description

Apple Inc. in California seeks an experienced SoC power modeling engineer within the Silicon Engineering group to model power dissipation across SOC components and optimize power efficiency. You will collaborate with architecture, SOC design, and system teams to define power targets and improve silicon-level performance.

Responsibilities include correlating models to silicon data, working with labs and validation teams, and delivering scalable energy-efficient IPs for AI/ML and mobile SoCs.