Audio ML Research Co-op - Real-Time DSP Prototyping

Bose Corporation, U.S.A; Massachusetts; Sur place; USD 55 000 - 71 000; Plein temps

Avantages offerts par ce poste

Health benefits

401(k) plan

Employee discount

Résumé du poste

Bose Corporation in Framingham, MA is seeking a highly motivated Audio Machine Learning Research candidate to prototype real-time algorithms for on-device audio processing. You will collaborate with ML, DSP, software engineering and psychoacoustics experts, and contribute to projects that may be patented or presented at top AI conferences.

The role runs Jan 11–June 25, 2027, full-time. Experience with TensorFlow or PyTorch and C/C++ is valued; you will present findings to an interdisciplinary

Qualifications

• Pursuing or recently finished a graduate‑level degree in ML, Computer Science, Music Technology or a related field.
• Hands‑on experience with applied audio ML techniques and DSP concepts is preferred.
• Familiarity with ML frameworks and programming in Python/ Matlab/C/C++ is desired.

Responsabilités

• Prototype, implement and evaluate ML algorithms for real-time on-device audio processing.
• Integrate innovative solutions into existing Bose systems and platforms.
• Contribute to patents and top-tier AI and DSP conference papers (e.g., NeurIPS, ICASSP).

Connaissances

Applied audio ML

TensorFlow

PyTorch

TFLite

ONNX

Audio DSP

Python

Matlab

C/C++

Audio source separation

Speech enhancement

Microphone array

Tiny ML

Generative audio modelling

Spatial sound synthesis

Room acoustics

Communication

Formation

Graduate degree in ML/CS or related field

Outils

Python

Matlab

C/C++

Description du poste

Bose Corporation in Framingham, MA is seeking a highly motivated Audio Machine Learning Research candidate to prototype real-time algorithms for on-device audio processing. You will collaborate with ML, DSP, software engineering and psychoacoustics experts, and contribute to projects that may be patented or presented at top AI conferences.

The role runs Jan 11–June 25, 2027, full-time. Experience with TensorFlow or PyTorch and C/C++ is valued; you will present findings to an interdisciplinary