Audio ML Engineer - Research

Introduction: A Career at HARMAN Corporate

We’re a global, multi-disciplinary team that’s putting the innovative power of technology to work and transforming tomorrow. At HARMAN Corporate, you are integral to our company’s award-winning success.

• Enrich your managerial and organizational talents – from finance, quality, and supply chain to human resources, IT, sales, and strategy
• Augment your comprehensive skillset with expert training across decision-making, change management, leadership, and business development
• Obtain 360-degree support throughout your career life cycle, from early-stage to seasoned leader

About the Role

The HARMAN Research Intelligent Audio team is working to set a new standard for audio: create experiences that understand the listener, adapt to their environment, and become more personal and useful over time. As an Audio ML Engineer in HARMAN Research, you will help turn that ambition into learning-based technologies that enable adaptive audio behavior across devices and contexts.

Reporting to the Director of Intelligent Audio, you will utilize your demonstrated technical depth in audio systems to develop machine learning models that enable audio systems to perceive, understand, and respond intelligently to our customers and the world around them. This is a hands-on research role that combines scientific rigor, rapid experimentation, and real-world product impact. Success means delivering models that create a perceptible benefit for users and meet product and platform constraints, that product teams can adopt with confidence.

What You Will Do

• Advance Audio ML research: Follow advances in relevant audio ML research fields and apply findings to solve business-relevant challenges.
• Learning-Based Perception Models: Develop ML models for perception-related tasks (e.g., quality prediction, artifact detection, scene/context classification, personalization embeddings, preference modeling).
• Build reliable data and evaluation: Define data collection, labeling, and representative evaluation conditions. Combine objective and perceptual evidence where appropriate.
• Engineer for product constraints: Design models for the intended embedded and cloud deployment paths. Optimize accuracy, latency, memory, compute, and power through methods such as quantization, pruning, and distillation.
• Cross-functional handoff: Partner with DSP, perceptual, and productization engineers to deliver reference pipelines, integration guidelines, and jointly agreed acceptance measures.
• Use modern AI tools: Lead in continuously improving results by applying AI-assisted tools to accelerate experimentation, coding, testing, analysis, and documentation.

What You Need to Be Successful

Essential

• MS or PhD in computer science, electrical engineering, statistics, applied machine learning, or a related field, or demonstrated equivalent experience.
• Relevant experience, typically at least five years of applied machine learning research or engineering experience, including at least two years applying machine learning to audio, speech, acoustics, perception, or related time series signals.
• Evidence of taking an idea from literature or hypothesis through baseline comparison, controlled experiments, reproducible evaluation, runtime optimization, and product integration or a product-ready prototype.
• Strong Python and modern machine learning framework capability, together with audio and signal processing fundamentals, evaluation design, dataset pipelines, and experiment tracking.
• Experience designing for embedded, edge, or cloud constraints and working with real-time engineering partners. You can explain model tradeoffs, limitations, failure modes, and supporting evidence.
• Ability to communicate research results and product implications clearly to specialist, engineering, and product audiences.

Preferred

• Depth in one or more areas such as speech enhancement and recognition, perceptual modeling, spatial or multichannel processing, acoustic scene understanding, source separation, or personalization.
• Experience with embedded inference toolchains, C or C++, model compression, hardware-aware optimization, and model lifecycle tooling.
• Experience documenting model and dataset provenance, intended use, performance, limitations, and relevant privacy or bias considerations.
• Experience with privacy-preserving learning or approved on-device personalization approaches.
• Patents, publications, or shipped machine learning features in consumer, automotive, or professional audio. Demonstrated product impact matters more than credentials alone.

What Makes You Eligible

• Successfully complete a background investigation and drug screen as a condition of employment after an offer.

What We Offer

• Access to employee discounts on world-class products from JBL, HARMAN Kardon, AKG, and more.
• Extensive training opportunities through HARMAN University.
• Competitive wellness benefits.
• Tuition reimbursement.
• The Be Brilliant employee recognition and rewards program.
• An inclusive and diverse work environment that encourages professional and personal development.

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