Audio Applied Research Science Intern

Jobtailor; Niles (IL); On-site; USD 90,000 - 140,000; Full time

Job summary

Jobtailor is seeking a highly skilled ML researcher to develop and optimize audio-focused machine learning solutions. The role involves dataset curation, designing and evaluating deep learning models for audio tasks, and deploying across multiple platforms.

You will collaborate with research and engineering teams to share insights and advance audio intelligence initiatives. You will apply modern ML engineering practices, document findings, and communicate results effectively.

Qualifications

• Pursuing a PhD or advanced Master’s in a quantitative field (EE, CS, math, stats, physics, data science, ML)
• Demonstrated research or project experience in ML, DL, or AI
• Experience applying ML to audio, speech, DSP, multimedia or related domains
• Proficiency with ML frameworks such as PyTorch, TensorFlow, or JAX
• Strong Python programming and scientific computing skills
• Ability to independently investigate complex technical challenges and prototype solutions
• Strong written and verbal communication skills
• Experience with audio signal processing, acoustics, speech processing, or music information retrieval
• Familiarity with MLOps: experiment tracking, versioning, CI/CD, scalable training
• Experience deploying ML models to embedded, edge, mobile, cloud, or realtime systems
• Experience with full-stack development and cloud technologies
• US work authorization for full-time employment; no visa sponsorship
• Shure will not sponsor applicants for work visas

Responsibilities

• Conduct research and develop machine learning solutions for audio problems
• Collect, create, and curate datasets for model development and evaluation
• Design, train, and evaluate deep learning models for audio applications (single- and multi-channel processing, speech/music enhancement, audio classification)
• Investigate and implement state-of-the-art approaches
• Optimize and adapt models for deployment across hardware and software platforms
• Apply modern ML engineering practices (shared codebases, reusable toolkits, experiment tracking, reproducible workflows)
• Document research findings and recommendations using collaboration tools
• Present technical results and insights to research and engineering teams

Skills

Machine Learning

Deep Learning

Audio Processing

Python Proficiency

MLOps

Experiment Tracking

Data Curation

Prototyping

Signal Processing

Communication Skills

Independent Problem Solving

Education

PhD or advanced Master’s in a quantitative field

Tools

PyTorch

TensorFlow

JAX

CI/CD Tools

Cloud Technologies

Scientific Computing Tools

Job description

• 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

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
• 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

Core Competencies

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.

Highest-signal resume keywords

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

ATS Optimization Keywords

Hard Skills

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

Soft Skills

• Strong Communication Skills
• Independent Problem Solving

Industry Keywords

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

Tools & Technologies

• PyTorch
• TensorFlow
• JAX
• Scientific Computing Tools
• CI/CD Workflows
• Cloud Technologies