Research Engineer — Agentic Audio ML Workflows

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.