Audio Applied Research Science Intern

• Conduct research and develop machine learning solutions for challenging audio problems
• Collect, create, and curate datasets for model development and evaluation
• Design, train, and evaluate deep learning models for audio applications, including single- and multi-channel audio processing, speech enhancement, music enhancement, audio classification, and other audio intelligence and signal processing tasks
• Investigate and implement state-of-the‑
• Optimize and adapt models for deployment across a variety of hardware and software platforms
• Apply modern machine learning engineering practices, including shared codebases, reusable toolkits, experiment tracking, and reproducible workflows
• Document research findings, experimental results, and technical recommendations using collaborative documentation tools
• Present technical results and insights to research and engineering teams

• Currently pursuing a PhD or advanced Master's degree in Electrical Engineering, Computer Science, Mathematics, Statistics, Physics, Data Science, Machine Learning, or a related quantitative field
• Demonstrated research or project experience in machine learning, deep learning, or artificial intelligence
• Experience applying machine learning techniques to audio, speech, digital signal processing, multimedia, or related domains
• Proficiency with modern machine learning frameworks and libraries such as PyTorch, Tensor Flow, JAX, or equivalent
• Strong programming skills in Python and experience working with scientific computing tools
• Ability to independently investigate complex technical challenges and rapidly prototype solutions
• Strong written and verbal communication skills
• Experience with audio signal processing, acoustics, speech processing, or music information retrieval
• Familiarity with MLOps practices, including experiment tracking, model versioning, CI/CD workflows, or scalable training infrastructure
• Experience deploying machine learning models to embedded, edge, mobile, cloud, or realtime systems
• Experience with full-stack software development and cloud technologies
• Applicants must be currently authorized to work in the United States on a full‑time basis
• Shure will not sponsor applicants for work visas

Expertise in developing and optimizing machine learning solutions for audio applications, with strong proficiency in deep learning frameworks and audio signal processing techniques. Demonstrated ability to conduct research, document findings, and communicate technical insights effectively.

• Machine Learning Solutions Development
• Deep Learning Model Design and Evaluation
• Audio Signal Processing
• Proficiency in Python Programming
• Experience with PyTorch and Tensor Flow

• Machine Learning
• Deep Learning
• Audio Processing
• Statistical Analysis
• Model Optimization
• Data Curation
• Experiment Tracking
• Prototyping
• Signal Processing
• MLOps Practices

• Strong Communication Skills
• Independent Problem Solving

• Electrical Engineering
• Computer Science
• Data Science
• Artificial Intelligence
• Multimedia

• Py Torch
• Tensor Flow
• JAX
• Scientific Computing Tools
• CI/CD Workflows
• Cloud Technologies