Audio Machine Learning Co-Op

The goal of our team is to develop novel AI-powered audio processing algorithms. The twist is that our algorithms must run in real-time, on physical devices, for applications such as voice pickup, hearing augmentation, and ones we haven't even thought of yet. As part of the team, you will work with experts in machine learning, digital signal processing, software engineering, and psychoacoustics to prototype and implement new algorithms.Current research interests of our group include machine learning (ML) methods for single- and multi-channel audio signal processing, source separation, perceptually-grounded speech enhancement, lightweight, low-latency ML models for low-resource devices, and related topics.Bose has a strong history of combining creative thinking with cutting-edge technology in the audio domain. We are looking for candidates passionate about machine learning and audio to help us shape the next chapter in the future of Bose!Responsibilities:The majority of your time will be devoted to prototyping, implementing, and evaluating ML algorithms, curating and developing internal resources, and regularly presenting your findings.You will integrate your novel solutions into new and existing systems and platforms to deliver new proofs of concept.You will be able to contribute to projects, which will be shipped to Bose customers, apply for patents, or submit papers to top-tier AI and signal processing conferences (e.g., NeurIPS, ICASSP, Interspeech, etc.).Education:Pursuing or recently finished a graduate-level degree in ML, Computer Science, Music Technology, or a related field.At a minimum, the candidate should be familiar with the material covered in introductory courses in digital signal processing, linear algebra, statistics, and data structures.Skills:Practical knowledge of applied audio ML and digital signal processing (DSP).Hands-on experience with at least one of the following research topics: source separation, speech enhancement, microphone array signal processing (e.g., beamforming), generative audio modelling, model compression (e.g., quantization, pruning).Familiarity with methods for spatial audio synthesis and/or room acoustics analysis/simulation is a plus.Experience developing ML algorithms in Python, TensorFlow/PyTorch, and/or C/C++.Familiarity with standard version control practices.Familiarity with the following technologies is also preferred: TFLite, ONNX, and/or AWS stack.Strong communication skills. You will present your work internally to a large, interdisciplinary audience on a regular basis.Bose is an equal opportunity employer that is committed to inclusion and diversity. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, genetic information, national origin, age, disability, veteran status, or any other legally protected characteristics.

#J-18808-Ljbffr