Develop agentic workflows and automation for real-time audio machine learning research. Responsibilities include orchestrating training, evaluation, experimentation, reporting, data preparation, hyperparameter searches, benchmarking, regression testing, experiment tracking, artifact management, and reproducible execution. The role integrates LLM agents with research tools, datasets, repositories, evaluation suites, and GPU infrastructure while partnering with audio researchers to create reusable workflows and dashboards.
At Bose Corporation, we believe sound is the most powerful force on earth - and for over 60 years, we have been a company built on innovation, excellence, and independence. Privately owned, fiercely customer-focused, and driven by our values, we continue to lead industries and transform lives through sound.
Today, Bose Corporation is entering an exciting new era. Across multiple global Business Units and Global Functions, we are shaping the future of audio technology, automotive, luxury, and premium experiences. We invite you to join us in this transformation.
Job Description
Description
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. You will build agentic tools and automation that make our audio ML research dramatically faster and more reproducible. The focus is on orchestrating training, evaluation, experimentation, and reporting across many types of AI models, using on-premise and/or cloud GPU infrastructure.
Bose has a strong history of combining creative thinking with cutting-edge audio technology. We are looking for people passionate about machine learning and audio to help shape what comes next.
Responsibilities
• Design and implement agentic workflows that plan, launch, monitor, diagnose, and summarize audio ML experiments, with appropriate human oversight.
• Automate data preparation, model training, hyperparameter searches, benchmarking, regression testing, and result comparison across research projects.
• Build reliable tooling for on-premises GPU scheduling and utilization, experiment tracking, artifact management, and reproducible execution.
• Connect LLM-powered agents to scripts, repositories, datasets, evaluation suites, and compute resources through well-defined tools and safeguards.
• Partner with audio researchers to turn repetitive experiments into reusable workflows and clear dashboards or reports.
Education
Degree in computer science, machine learning, electrical engineering, or a related field, or equivalent practical experience.
Skills
• Strong software engineering and Python skills, with hands-on experience building LLM agents or agentic workflows that invoke tools and execute multi-step tasks.
• Comfortable tackling complex, open-ended problems where you have to experiment with and discard many ideas before finding the right solution.
• Experience with ML training and evaluation pipelines, PyTorch, experiment tracking, and automated testing.
• Practical experience operating on-premises Linux GPU machines, including CUDA environments, containers, job scheduling, and resource monitoring.
• Working knowledge of audio ML and DSP, including common datasets, audio metrics, and model development workflows.
• Experience with orchestration frameworks, distributed training, CI/CD, or MLOps is a plus.
• Ability to build reliable, observable systems and communicate effectively with research scientists.
At Bose, you're inspired to be and do your best and are rewarded for your unique talents! Our compensation is thoughtfully tailored to your skills, experience, education, and location, and goes beyond base salary. The hiring range for this position in the primary work location of Framingham, Massachusetts is: $165,400-$227,500.The hiring range for other Bose work locations may vary. In addition to competitive base pay we offer rewards including bonus programs, comprehensive health and welfare benefits, a 401(k) plan, plus exclusive perks designed to support your wellbeing, and a generous employee discount where you can immerse yourself in our products and experiences. We are a proudly independent company-driven by purpose, guided by our values, and united by a belief in the power of sound. As the world leader in audio experiences, we're creating what's next-pushing boundaries and delivering transformative sound experiences for people everywhere. Join us and make your next career move a mic-drop. Let's Make Waves.
Today, Bose Corporation is entering an exciting new era. Across multiple global Business Units and Global Functions, we are shaping the future of audio technology, automotive, luxury, and premium experiences. We invite you to join us in this transformation.
Job Description
Description
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. You will build agentic tools and automation that make our audio ML research dramatically faster and more reproducible. The focus is on orchestrating training, evaluation, experimentation, and reporting across many types of AI models, using on-premise and/or cloud GPU infrastructure.
Bose has a strong history of combining creative thinking with cutting-edge audio technology. We are looking for people passionate about machine learning and audio to help shape what comes next.
Responsibilities
• Design and implement agentic workflows that plan, launch, monitor, diagnose, and summarize audio ML experiments, with appropriate human oversight.
• Automate data preparation, model training, hyperparameter searches, benchmarking, regression testing, and result comparison across research projects.
• Build reliable tooling for on-premises GPU scheduling and utilization, experiment tracking, artifact management, and reproducible execution.
• Connect LLM-powered agents to scripts, repositories, datasets, evaluation suites, and compute resources through well-defined tools and safeguards.
• Partner with audio researchers to turn repetitive experiments into reusable workflows and clear dashboards or reports.
Education
Degree in computer science, machine learning, electrical engineering, or a related field, or equivalent practical experience.
Skills
• Strong software engineering and Python skills, with hands-on experience building LLM agents or agentic workflows that invoke tools and execute multi-step tasks.
• Comfortable tackling complex, open-ended problems where you have to experiment with and discard many ideas before finding the right solution.
• Experience with ML training and evaluation pipelines, PyTorch, experiment tracking, and automated testing.
• Practical experience operating on-premises Linux GPU machines, including CUDA environments, containers, job scheduling, and resource monitoring.
• Working knowledge of audio ML and DSP, including common datasets, audio metrics, and model development workflows.
• Experience with orchestration frameworks, distributed training, CI/CD, or MLOps is a plus.
• Ability to build reliable, observable systems and communicate effectively with research scientists.
At Bose, you're inspired to be and do your best and are rewarded for your unique talents! Our compensation is thoughtfully tailored to your skills, experience, education, and location, and goes beyond base salary. The hiring range for this position in the primary work location of Framingham, Massachusetts is: $165,400-$227,500.The hiring range for other Bose work locations may vary. In addition to competitive base pay we offer rewards including bonus programs, comprehensive health and welfare benefits, a 401(k) plan, plus exclusive perks designed to support your wellbeing, and a generous employee discount where you can immerse yourself in our products and experiences. We are a proudly independent company-driven by purpose, guided by our values, and united by a belief in the power of sound. As the world leader in audio experiences, we're creating what's next-pushing boundaries and delivering transformative sound experiences for people everywhere. Join us and make your next career move a mic-drop. Let's Make Waves.
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