Engineer Sr, Applied Research Science

Shure•Niles, IL

9h•$115,600 - $185,000•Hybrid

About The Position

Join Shure’s Signal Processing and Applied Research team to research and develop innovative AI/ML and advanced algorithmic solutions for complex, real-world customer challenges. The Senior Engineer works alongside software, signal processing, data, and test engineers to investigate emerging technologies, develop and demonstrate new approaches, and bridge research and experimentation with product innovation. The role is based at Shure’s Niles, Illinois headquarters in a hybrid work arrangement. The position involves researching, designing, and implementing audio features and products; identifying valuable AI/ML applications; conducting literature reviews and original research; defining model and algorithm requirements; developing and evaluating custom machine learning models and algorithms for audio applications; and creating prototypes suited to real-time, low-latency and computationally constrained environments. The role also includes gathering and preparing data, advancing repeatable machine learning engineering practices, documenting findings, staying current with research and technology, contributing to technical working groups, and supporting intellectual property development. Candidates must be currently authorized to work in the United States on a full-time basis. Shure will not sponsor work visas for this position.

Requirements

Bachelor’s degree in electrical engineering, computer science, mathematics, statistics, physics, data science, machine learning, or a related research science field, with at least 5 years of related experience; or a master’s degree in one of these fields with at least 3 years of related experience; or a PhD in one of these fields without a work-experience requirement.
Proficiency in Python programming.
Proficiency with frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, NumPy, and Matplotlib.
Proficiency with Git/GitHub, Docker, Jupyter, AWS, and on-premises GPU training tools.
Currently authorized to work in the United States on a full-time basis; Shure does not sponsor work visas for this position.

Nice To Haves

C/C++ or MATLAB programming.
Experience in audio classification and scaling audio classification frameworks for efficient development and maintenance.
Experience using AI-assisted software development and research tools, such as GitHub Copilot, agentic AI systems, large language models, and code generation and analysis tools, to improve engineering productivity and solution development.
Experience independently deploying, validating, and demonstrating early machine learning proofs of concept.
Knowledge of or experience with classical digital signal processing.
Proficiency in developing low-latency, embedded-friendly solutions.
Audio engineering, recording, or other audio production experience, including use of digital audio workstations.

Responsibilities

Partner with cross-functional teams to research, design, and implement audio features and products.
Identify high-value customer AI/ML use cases with colleagues, stakeholders, and engineering partners, while exploring opportunities to optimize, scale, and platform solutions.
Review academic and industry literature and conduct original research and experimentation to solve complex technical problems; share findings, insights, and prototypes with colleagues, senior leaders, and executives.
Work with engineers and product managers to establish and document performance metrics, system constraints, technical requirements, and architectural options.
Design and train custom machine learning models and algorithms for audio applications, including single- and multi-channel audio processing and speech enhancement.
Optimize models for latency, computational efficiency, and deployment requirements in partnership with software engineers.
Evaluate models and algorithms against established performance metrics, refine and fine-tune solutions, and conduct subjective listening tests when appropriate.
Use machine learning and advanced digital signal processing techniques to build prototypes for real-time, low-latency data pipelines and computationally constrained applications.
Identify, acquire, collect, and generate relevant data for robust training and test datasets, including licensed or purchased datasets, internally collected data, and simulations.
Support high-quality dataset creation using internal annotation tools and third-party annotation partners.
Apply and strengthen machine learning software engineering practices, including shared toolkits, code repositories, and experiment tracking, to support repeatable and scalable research.
Monitor industry and academic advancements, participate in relevant training and conferences, and identify opportunities to incorporate emerging research, technologies, and techniques.
Contribute to collaborative technical working groups addressing problems in and beyond the employee’s primary area of expertise.
Document research methods, experiments, findings, results, and technical insights using collaborative documentation tools, independently and with the broader team.
Contribute to intellectual property development, participate in technical brainstorming, and foster experimentation and innovation across the organization.