Data Scientist for BAW/SAW Filter Solutions

Skyworks - Irvine, CA

Full-time - Entry Level

Irvine, CA

Computer and Electronic Product Manufacturing

About the position

Skyworks Solutions is seeking a skilled Data Scientist for BAW/SAW Filter Solutions to play a pivotal role in the development of SAW/BAW and MEMs technologies. This position involves the development of machine learning models and finite element models specifically for SAW/BAW resonators. The successful candidate will analyze a variety of characterization and metrology data, providing essential feedback to the device fabrication team. Collaboration is key, as this role requires working closely with engineering teams based in both the U.S. and Asia. In this fast-paced environment, the Data Scientist will be responsible for processing, cleansing, and validating data integrity to ensure accurate analysis, modeling, and experimentation. The role includes analyzing on-wafer device statistics of key parameters and performing advanced data analytics. The candidate will develop and apply machine learning algorithms to address technical challenges related to BAW/SAW devices and will work to accelerate finite element models using machine learning techniques in collaboration with other device modeling engineers. Additionally, the Data Scientist will provide technical guidance on machine learning solutions within cross-functional teams and propose innovative approaches to characterize, analyze, and model MEMs devices, pushing the boundaries of state-of-the-art BAW and SAW technologies. The desired outcomes for this role include accelerating the SAW/BAW roadmap for next-generation devices, achieving reduced size, improved performance, and cost-effectiveness. The ideal candidate will excel at multitasking across multiple projects and will be a team player, contributing to a seamless interface between BAW development teams in the U.S. and Asia.

Responsibilities

• Process, cleanse, and validate data integrity for subsequent analysis, modeling, and experimentation.
• Analyze on-wafer device statistics of key device parameters and perform advanced data analytics.
• Develop and apply machine learning algorithms to technical challenges related to BAW/SAW devices.
• Accelerate finite element models with machine learning techniques in collaboration with other device modeling engineers.
• Provide technical guidance on machine learning solutions within cross-functional teams.
• Propose novel approaches to characterize, analyze, and model MEMs devices.

Requirements

• PhD degree in Engineering or Machine Learning is required.
• Experience in collecting, analyzing, and interpreting scientific data.
• Proficiency with Machine Learning frameworks, such as PyTorch and TensorFlow.
• Experience with deep learning models, such as CNNs, GNNs, Transformers, RNNs.
• Familiarity with generative models, such as VAEs, GANs, and Diffusion models.
• Ability to build autonomous systems for dataset definition, model training, and evaluation.
• 5 years of industry or university experience is preferred.
• Experience in finite element simulations and knowledge in MEMS technology is preferred.
• Demonstrated strong communication and presentation skills.

Nice-to-haves

• Deep knowledge and experience in working with semiconductor and/or MEMs devices and test methodologies.
• Knowledge of BAW/SAW resonator technology is desired.
• Ability to function effectively in a multi-cultural, multi-geographic environment.

Benefits

• Access to healthcare benefits (including a premium-free medical plan option)
• 401(k) plan and company match
• Employee stock purchase plan
• Paid time off (including vacation, sick/wellness, parental leave)
• Eligibility to participate in an incentive plan
• Potential for additional awards based on performance.