Memory Quality & Reliability PhD Intern - Data Analytics

Intel Corporation; Folsom (CA); Hybrid; USD 121,000 - 122,000; Full time

Benefits offered by this job

Hybrid work model

Competitive pay

Stock bonuses

Health benefits

Job summary

Intel Corporation’s TD Memory Quality and Reliability Internship offers hands-on experience in memory characterization, silicon data analytics, and statistical modeling to drive technology readiness for future process nodes.

You will analyze data from SRAM, RF, and Fuse memory test structures, develop predictive models, and present findings to engineering teams, working with Design, Device Engineering, PDE, and Yield groups.

Qualifications

• Pursuing a Ph.D. in Electrical Engineering or related field; GPA 3.0+.
• Programming in Python or similar languages; experience with data analysis.
• Strong academic performance (GPA 3.0+).

Responsibilities

• Analyze silicon characterization data for SRAM, RF, and Fuse memory structures to identify performance trends and risks.
• Support memory characterization across voltage, temperature, and process conditions.
• Develop predictive models and technology readiness assessments.
• Generate technical reports and contribute to technology certification reviews.
• Collaborate with cross-functional teams including Design, Device Engineering, Process Integration, PDE, and Yield Engineering.
• Present data-driven findings to engineering teams and stakeholders.

Skills

Python programming

Data analysis

Statistical modeling

Communication

Education

Ph.D. in Electrical Engineering

Tools

Python

Pandas

NumPy

JMP

Job description

Intel Corporation’s TD Memory Quality and Reliability Internship offers hands-on experience in memory characterization, silicon data analytics, and statistical modeling to drive technology readiness for future process nodes.

You will analyze data from SRAM, RF, and Fuse memory test structures, develop predictive models, and present findings to engineering teams, working with Design, Device Engineering, PDE, and Yield groups.