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Research Engineer — Agentic Audio ML Workflows

Boseallaboutme

Skills

Required

  • machine learning
  • ML
  • audio ML
  • software engineering
  • Python
  • evaluation pipelines
  • PyTorch
  • experiment tracking
  • automated testing
  • Linux

Preferred

  • orchestrating
  • AI models
  • on-premise and/or cloud GPU infrastructure
  • model training
  • hyperparameter searches
  • benchmarking
  • regression testing
  • GPU
  • artifact management
  • reproducible execution

About Boseallaboutme

  • 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.
  • 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.
  • 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

  • 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.
  • 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.

Requirements

  • Degree in computer science, machine learning, electrical engineering, or a related field, or equivalent practical experience.
  • 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. ‎