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Intern Co-Op Machine Learning

  • Full Time Internship
  • Irvine, CA
  • Applications have closed

Website Skyworks

Job Description
It is a Summer/Fall Co-Op position from June to December 2024

The candidate will participate in the key activities in the technology development of piezoelectric bulk acoustic resonators (BAW). It is a cross-functional role in a collaborative engineering team and will have the opportunity to work with modeling, design, process, testing and layout engineers.

Desired location: Irvine, CA

Job Responsibilities

Develop and apply AI/ML algorithms to technical challenges relevant to acoustic devices

Process, cleanse and validate the integrity of data to be used for further data analysis, modeling and experimentation

Collaborate with cross-functional teams to provide technical guidance in machine learning solutions

Requirements

Current student pursuing a Master or PhD in engineering

Must have the availability to work full-time from June to December 2024

Experience in collecting, analyzing and interpreting scientific data

Experience with ML framework, such as PyTorch and TensorFlow

Experience with deep learning models, such as CNNs, GNNs, Transformers, RNNs

Experience with generative models, such as VAEs, GANs and Diffusion model

Capability to build a scalable and autonomous system for defining datasets, training models, and model evaluation
The typical pay range for an Engineering intern across the U.S. is currently USD $26.00 – $47.50 per hour and for a Non-Engineering intern across the U.S. is currently USD $22.50 – $42.00 per hour. Starting pay will depend on level of education, the ultimate job duties and requirements, and work location. Skyworks has different pay ranges for different work locations in the U.S.

Skyworks is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.