Shure is looking to hire an Associate Staff Engineer to develop advanced AI/ML algorithms and signal processing solutions to improve the performance of their audio products.
Requirements
- Proficiency in programming languages: Python required; C/C++ or Matlab also preferred
- Proficiency in leveraging frameworks and libraries including: PyTorch, Tensorflow, scikit-learn, NumPy, Matplotlib, etc.
- Proficiency in tools and technologies including: Git/GitHub, Docker, Jupyter, AWS, OnPrem GPU training tools
- Knowledge or experience with classical Digital Signal Processing
- Proficiency in developing low latency, embedded-friendly solutions
- Experience in Audio engineering, DAWs, recording, or other audio production.
Responsibilities
- Work as part of a cross-functional team to create, design & implement cutting-edge audio features and products
- Design custom machine learning models and algorithms targeting audio functionality (single and multi-channel audio processing algorithms, speech enhancement, music enhancement, audio classification, etc.) within latency/computation constraints.
- Transform and optimize models to support implementation requirements.
- Work with Software Engineers to identify and optimize input features, frame rates, model structures, and other characteristics that impact algorithmic performance.
- Measure model/algorithm performance against identified metrics and fine-tune to optimize outcomes.
- Conduct subjective listening tests to balance results with objective results.
- Utilize machine learning and advanced DSP approaches to address challenges such as processing real-time, low latency data pipelines and right-sizing solutions
Other
- Work as part of a cross-functional team
- Collaborate with colleagues, other engineers, and product managers
- Brainstorm with colleagues, stakeholders, and other engineers
- Mentors, coaches, and monitors the work of less experienced engineers.
- Adopt mature machine learning software engineering practices (e.g. shared toolkits, repos, experiment tracking).
