Machine Learning Modeling Winter/Spring Co-Op (Jan-June '27)

Description

Skyworks is a global leader in wireless connectivity, delivering cutting-edge RF, acoustic wave, and semiconductor technologies that power next-generation communication systems. As we continue to expand our artificial intelligence and machine learning capabilities, we are leveraging advanced analytics and data-driven approaches to accelerate innovation, optimize manufacturing processes, and enhance product performance.

We are seeking a highly motivated Machine Learning Modeling Engineer Intern to join our Acoustic Device Group. In this role, you will develop, implement, and optimize machine learning models to support the design, analysis, and development of advanced acoustic and RF devices. You will work with complex engineering datasets and collaborate closely with multidisciplinary teams to apply AI and machine learning techniques to real-world semiconductor challenges.

The ideal candidate has strong analytical and problem-solving skills, a solid foundation in machine learning algorithms and data science, and a passion for applying AI technologies to RF, acoustic wave, and semiconductor engineering. This position offers a unique opportunity to contribute to next-generation wireless products while gaining hands-on experience at the intersection of machine learning and advanced semiconductor technology.

Co-Op Period from January to June 2027

Responsibilities

• Apply artificial neural network (ANN) and machine learning techniques to support microwave CAD and semiconductor design projects.
• Develop and evaluate ANN-based models for acoustic devices, electromagnetic (EM) analysis, and parametric modeling.
• Assist in the development of data-driven models for RF filter design and performance prediction.
• Support neural network-based multiphysics modeling of electrical, acoustic, thermal, and mechanical interactions.
• Collect, clean, and preprocess structured, unstructured, time-series, and image datasets for machine learning applications.
• Perform exploratory data analysis (EDA), feature engineering, and data visualization to improve model accuracy and insights.
• Build and optimize LLM-powered agents with capabilities such as tool integration, workflow automation, and multi-step reasoning.
• Research emerging developments in AI, machine learning, and data science, and explore their applications in semiconductor design and manufacturing optimization.

Required Experience and Skills

• Currently pursuing an M.S. or Ph.D. degree in Computer Science, Electrical Engineering, Applied Mathematics, Data Science, or a related technical field.
• Solid foundation in machine learning, deep learning, statistical modeling, and data analysis techniques.
• Proficiency in Python programming; experience with C++ and SQL is a plus.
• Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, or Keras.
• Familiarity with data analysis and scientific computing libraries, including NumPy, Pandas, and Scikit-learn.
• Basic knowledge of cloud computing platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Experience developing machine learning workflows, data pipelines, or scalable software solutions through coursework, research, or projects.
• Strong analytical and problem-solving skills, with attention to detail and the ability to work independently.
• Self-motivated learner with a passion for artificial intelligence, innovation, and continuous professional growth.

Desired Experience and Skills

Coursework or project experience in large language models (LLMs), generative AI, agent-based systems, or deep learning applications
• Coursework or project experience in RF, microwave engineering, signal processing, computational electromagnetics, or semiconductor devices
• Experience working with simulation-generated datasets and physics-based models: knowledge of Physics-Informed Neural Networks (PINNs), surrogate modeling, or digital twin methodologies.
• Familiarity with scientific computing, numerical optimization, and surrogate modeling techniques.
• Familiarity with reinforcement learning, Bayesian optimization, or other optimization techniques for engineering applications.
• Interest in applying AI and machine learning techniques to engineering design automation, semiconductor technologies, and manufacturing optimization.
• Familiarity with Git, Jira, and MLflow for version control and model management.