Architecture Energy Modeling Engineer - New College Grad 2026 - CA, Santa Clara, United States
$116,000 - $218,500 USD yearly
Originally posted 21 August 2026 by the employer.
Join our Power Modeling, Methodology and Analysis Team building energy models for NVIDIA GPUs, CPUs, and Tegra SOCs.
About the role
As an Architecture Energy Modeling Engineer, you will join the Power Modeling, Methodology and Analysis Team responsible for researching, developing, and deploying methodologies to improve energy efficiency and building energy models for NVIDIA's products. Your work will focus on developing Machine Learning based power models to analyze and reduce power consumption of NVIDIA GPUs.
What you'll do
- Develop an energy-efficient GPU in collaboration with architects, designers, and performance engineers.
- Identify key design features and workloads for building Machine Learning based unit power/energy models.
- Develop and own methodologies and workflows to train models using ML and/or statistical techniques.
- Improve the accuracy of trained models by using different model representations, objective functions, and learning algorithms.
- Develop methodologies to estimate data movement power/energy accurately.
- Correlate predicted energy from models built at different stages of the design cycle with silicon.
What you'll need
- Pursuing or recently completed a MS or PhD in Electrical Engineering, Computer Engineering, Computer Science or equivalent experience.
- Strong coding skills in Python, C++.
- Background in machine learning, AI, and/or statistical modeling.
- Background in computer architecture and interest in energy-efficient GPU designs.
- Ability to formulate and analyze algorithms, and comment on their runtime and memory complexities.
- Basic understanding of fundamental concepts of energy consumption, estimation, and low power design.
Nice to have
- Familiarity with Verilog and ASIC design principles.
Skills: machine learning, power models, GPU architecture, energy modeling, ASIC design
This role has been open 44 days — well below the 75-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
501
|
Median days open
75 d
|
Median salary
$232k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | NVIDIA | All employers we track in this specialty (501 roles · 75 employers) |
|---|---|---|
| Open roles in this specialty | 84 | 501 |
| Open roles in the wider Software, Firmware & Systems family | 949 | 5938 · 145 employers |
| Median days open | 71 d | 75 d (−4 d vs this employer) |
| Median salary (USD postings) | — | $232k |
Skills observed across this category: machine learning, power models, GPU architecture, energy modeling, ASIC design
Who's hiring in this category
- Qualcomm · 99 open roles · median 108 d
- NVIDIA (this employer) · 84 open roles · median 71 d
- AMD · 42 open roles · median 65 d
- Micron Technology · 30 open roles · median 57 d
- Mobileye · 20 open roles · median 72 d
- NXP Semiconductors · 15 open roles · median 62 d
How we counted: 501 open AI/ML Hardware Engineering (AI ML Hardware) roles from 75 employers tracked in the SemiconductorJobs index, counted 5 Oct 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.