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Audio AI Research Scientist – Audio Source Separation
Bose Corporation. Research, prototype, train, and evaluate novel source separation and extraction algorithms for speech and other real-world sounds .
Posted 11/5/2026full-timeRemote • Massachusetts • United States, Italy, Norway, Australia, France, Denmark, Portugal, Switzerland, Sweden, United Kingdom, GermanyMid-LevelSenior💰 $165,400 - $227,500 per yearWebsite
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in audio source separation and extraction algorithms, with a strong foundation in audio DSP and machine learning. Proficient in developing and evaluating models for real-time applications, leveraging Python and PyTorch for implementation.
Highest-signal resume keywords
Audio Source SeparationDeep Learning in PyTorchAudio DSP KnowledgePython ProficiencyGraduate Degree in Machine Learning or Related Field
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Audio Source SeparationTarget Sound ExtractionSpeech EnhancementNeural Audio ProcessingTime-Frequency MethodsMicrophone Array ProcessingReal-Time InferenceObjective Evaluation MethodsPerceptual Evaluation MethodsC/C++ Familiarity
Soft Skills
Strong Communication SkillsProblem-Solving
Tools & Technologies
PyTorchONNXTFLiteEmbedded Deployment
Industry Keywords
DSPMultimodal SeparationSensor-Conditioned SeparationConference PublicationsICASSPInterspeechNeurIPS
Tech Stack
Tools & technologiesPythonPyTorchC++
About the role
Key responsibilities & impact- Research, prototype, train, and evaluate novel source separation and extraction algorithms for speech and other real-world sounds
- Develop approaches for single- and multi-microphone recordings, including spatial cues and optional auxiliary sensors
- Investigate robust separation in overlapping talkers, noise, reverberation, and motion
- Evaluate perceptual quality, separation performance, generalization, latency, memory, and computational requirements
- Collaborate with DSP and embedded software teams to deploy models in real-time demonstrations and product platforms
- Contribute to patents and conference papers
- Develop next-generation algorithms that isolate, extract, or suppress individual sound sources from microphone recordings for real-time physical-device applications
Requirements
What you’ll need- Graduate degree (MS or PhD) in machine learning, electrical engineering, computer science, or a related field, or equivalent research experience
- Deep hands-on experience in audio source separation, target sound extraction, speech enhancement, or related neural audio processing
- Strong knowledge of audio DSP, time-frequency methods, and microphone array or spatial signal processing
- Experience training and evaluating audio models in PyTorch
- Proficiency in Python
- Understanding of objective and perceptual evaluation methods and real-time low-power inference tradeoffs
- Comfortable tackling complex, open-ended problems requiring experimentation
- Familiarity with C/C++ is a plus
- Experience with multimodal or sensor-conditioned separation, ONNX/TFLite, or embedded deployment is a plus
- Strong communication skills
- Publications at conferences such as ICASSP, Interspeech, NeurIPS, or similar are welcome but not required
Benefits
Comp & perks- Bonus programs
- Comprehensive health and welfare benefits
- 401(k) plan
- Exclusive perks designed to support wellbeing
- Generous employee discount
- Compensation beyond base salary