Software Engineering Intern, Power Modeling & AI Tools
$104,000–$145,600 year
On-siteSanta Clara, California, United States
Job Summary
Integrate and analyze engineering data to improve power model accuracy, while building automation tools, scripts, and visualizations that make complex datasets actionable. Develop internal software solutions and AI-enabled interfaces, including LLM-based tools and agentic AI, to support workload profiling and model validation. Collaborate with experienced engineers on next-generation power modeling workflows for CPU and AI products using Python, SQL, and Git-based practices. This on-site role in Santa Clara, CA, offers exposure to hardware, systems, and semiconductor data within a team focused on building high-performance RISC-V CPU and AI platforms.
Required Qualifications
- Currently pursuing a degree in Computer Science, Data Science, Statistics, Mathematics, Electrical Engineering, or a related quantitative field
- Experience with Python
- Comfort working in code-first analysis environments
- Familiarity with SQL
- Familiarity with notebooks
- Familiarity with scripting workflows
- Familiarity with Git-based development practices
- Ability to build visualizations, dashboards, or lightweight analytical tools to communicate insights
- Support the development of software tools, automation workflows, and data-driven solutions for engineering applications
- This role is on-site based out of Santa Clara, CA
- Eligible to access U.S. export-controlled technology
Desired Qualifications
- A software-minded engineer with strong analytical skills and curiosity about AI-enabled engineering workflows
- A strong problem solver who enjoys working with complex technical datasets and developing practical solutions
- A collaborative teammate with strong communication skills and a willingness to learn new tools, APIs, and engineering workflows
- Ability to build tools, automation, and data-driven solutions that make complex engineering data and models more accessible, intuitive, and actionable
- Integrating and analyzing engineering data
- Developing internal tools and scripts
- Creating visualizations and AI-enabled interfaces
- Supporting workload profiling, model validation, and analytics to improve power model accuracy and reliability
- Contribute to modern software workflows, including LLM-based tools and agentic AI interfaces
- Following strong software development practices
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