Research Engineer – Agentic Audio ML Workflows

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🌐 United States, Italy, +11 more countries – Remote

🇺🇸 United States – Remote 🇮🇹 Italy – Remote 🇯🇵 Japan – Remote 🇳🇴 Norway – Remote 🇦🇺 Australia – Remote 🇰🇷 South Korea – Remote 🇫🇷 France – Remote 🇩🇰 Denmark – Remote 🇨🇭 Switzerland – Remote 🇵🇹 Portugal – Remote 🇸🇪 Sweden – Remote 🇬🇧 United Kingdom – Remote 🇩🇪 Germany – Remote

🍂 Massachusetts – Remote

🍂 Massachusetts – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

📚 Research Engineer

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Bose Corporation

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

🚘 Automotive

🏭 Manufacturing

🍽️ Food & Beverage

💰 $430.2k Equity crowdfunding on 2017-12

Automotive • Manufacturing • Food & Beverage

<Bose Corporation> is a global audio equipment company that designs, manufactures, and sells consumer and professional sound products, including headphones, earbuds, speakers, home theater systems, portable PA systems, and aviation headsets. The company operates direct-to-consumer online retail, offers protection plans and trade-in programs, and provides support services like repairs and corporate gifting. Bose emphasizes premium audio performance, noise-cancellation technology, and lifestyle-oriented marketing.

📋 Description

• 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. • Hands-on experience building LLM agents or agentic workflows that invoke tools and execute multi-step tasks. • 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. • Ability to build reliable, observable systems and communicate effectively with research scientists. • Experience with orchestration frameworks, distributed training, CI/CD, or MLOps is a plus.

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