Audio Applied Research Science Intern

Shureorporated•Niles, IL

3d•Hybrid

About The Position

The Signal Processing and Applied Research Science team is seeking a highly motivated Audio Applied Research Science Intern to help develop next-generation audio technologies powered by machine learning and artificial intelligence. This role offers the opportunity to contribute to cuttingedge research and product development in professional audio applications. This Internship will be Remote, Onsite or Hybrid in Niles, IL.

Requirements

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, TensorFlow, 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.
Applicants for this position must be currently authorized to work in the United States on a full-time basis. Shure will not sponsor applicants for this position for work visas.

Nice To Haves

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.
Publications, open-source contributions, or demonstrated research achievements are a plus.

Responsibilities

Conducting research and developing machine learning solutions for challenging audio problems.
Collecting, creating, and curating datasets for model development and evaluation.
Designing, training, and evaluating deep learning models for audio applications, including: Single- and multi-channel audio processing, Speech enhancement, Music enhancement, Audio classification, Other audio intelligence and signal processing tasks.
Investigating and implementing state-of-the-art machine learning architectures and techniques.
Optimizing and adapting models for deployment across a variety of hardware and software platforms.
Applying modern machine learning engineering practices, including shared codebases, reusable toolkits, experiment tracking, and reproducible workflows.
Documenting research findings, experimental results, and technical recommendations using collaborative documentation tools.
Presenting technical results and insights to research and engineering teams.