Engineer Sr, Applied Research Science

Join Shure’s Signal Processing and Applied Research team and help explore the future of intelligent audio.

As a Senior Engineer in Applied Research Science
, you’ll research and develop innovative AI/ML and advanced algorithmic solutions aimed at solving complex, real-world customer challenges.

You’ll be working alongside software, signal processing, data, and test engineers, where you will investigate emerging technologies, develop and demonstrate new approaches, and help bridge the gap between research, experimentation, and product innovation. This is an opportunity for someone who enjoys tackling difficult technical problems and seeing ideas move from concept and prototype toward real-world application.

This hybrid position is based at our Niles, IL headquarters.

• Collaborate: Partner with cross-functional teams to research, design, and implement cutting-edge audio features and products.
• Ideate: Collaborate with colleagues, stakeholders, and engineering partners to identify high-value AI/ML use cases for customers, while exploring opportunities to optimize, scale, and platform solutions.
• Conduct Research: Review relevant 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.
• Define Model & Algorithm Requirements: Work closely with engineers and product managers to establish and document performance metrics, system constraints, technical requirements, and architectural options.
• Develop & Train Models: Design and train custom machine learning models and algorithms for audio applications, including single- and multi-channel audio processing, speech enhancement, and related functionality. Optimize models for latency, computational efficiency, and deployment requirements, partnering with software engineers to improve overall performance.
• Evaluate Performance: Assess models and algorithms against established performance metrics, refine and fine-tune solutions, and conduct subjective listening tests when appropriate to complement objective evaluation.
• Build Prototypes: Apply machine learning and advanced digital signal processing (DSP) techniques to develop prototypes and solve challenges involving real-time, low-latency data pipelines, computational constraints, and appropriately sized solutions.
• Collect & Simulate Data: Identify, acquire, collect, and generate problem-relevant data to create robust training and test datasets. Sources may include licensed or purchased datasets, internally collected data, and simulations of algorithms within predefined audio paths.
• Annotate Data: Support the creation of high-quality datasets through internal annotation tools and third-party annotation partners.
• Advance Engineering Practices: Apply and strengthen machine learning software engineering practices, including shared toolkits, code repositories, experiment tracking, and other approaches that support repeatable and scalable research.
• Stay Current: Monitor advancements across industry and academia, participate in relevant training and conferences, and identify opportunities to incorporate emerging research, technologies, and techniques into your work.
• Participate in Working Groups: Contribute to collaborative technical working groups focused on solving problems across specific areas, including topics that may extend beyond your primary area of expertise.
• Document Research: Clearly document research methods, experiments, findings, results, and technical insights using collaborative documentation tools, both independently and in partnership with the broader team.
• Innovate & Invent: Contribute to intellectual property development,…