Audio ML Engineer (Research)

A Career at HARMAN

As a technology leader that is rapidly on the move HARMAN is filled with people who are focused on making life better. Innovation inclusivity and teamwork are a part of our DNA. When you add that to the challenges we take on and solve together youll discover that at HARMAN you can grow make a difference and be proud of the work you do every day.

Introduction: A Career at HARMAN Corporate

Were a global multi-disciplinary team thats putting the innovative power of technology to work and transforming tomorrow. At HARMAN Corporate you are integral to our companys 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 Audio ML Engineer (Research) develops learning-based perception and personalization models that enhance Intelligent Audio experiences across devices and contexts. You will build models that understand audio scenes predict perceptual outcomes personalize tuning and drive adaptive behaviordesigned from the start for embedded and cloud deployment Year 1 your work is expected to feed directly into productization by delivering models that are measurable reproducible and deployable (or easily productizable) with clear compute/memory tradeoffs. Success means your models improve user experience in controlled testing and remain robust in the messiness of real-world use cases.

What You Will Do

• Learning-Based Perception Models: Develop ML models for perception-related tasks (e.g. quality prediction artifact detection scene/context classification personalization embeddings preference modeling).
• Embedded Cloud Deployment Focus: Design solutions that can run on-device (quantized efficient inference) and/or scale in cloud pipelines (batch evaluation fleet learning offline training on-device inference).
• Personalization & Adaptation: Build personalization and adaptation strategies that integrate with DSP pipelines (e.g. model outputs drive adaptive EQ/DRC/spatial parameters) while maintaining stability and explainability.
• Data Strategy & Tooling: Define data collection and labeling strategies data QA augmentation bias checks and experiment trackingso results are reproducible and transferable to product.
• Model Optimization: Apply compression/acceleration techniques (quantization pruning distillation ONNX export hardware-aware training) to meet latency and footprint constraints.
• Cross-Functional Handoff: Partner with DSP perceptual and productization engineers to deliver reference pipelines integration guidelines and acceptance metrics for OneUX releases.
• AI Tools: Use modern AI tooling (LLM-based coding assistants data analysis copilots automated report generation) to accelerate iteration while keeping rigorous review and validation.

What You Need to Be Successful

• Education: MS or PhD in CS/EE/Statistics/Applied ML (or BS with strong equivalent experience).
• Experience: 5 years applied ML engineering experience; 2 years specifically in audio/speech or time-series ML strongly preferred.
• ML Stack: Strong proficiency in Python PyTorch/TensorFlow dataset pipelines evaluation methodology and experiment tracking.
• Deployment Skills: Experience deploying models to embedded (TFLite / ONNX Runtime / custom inference) and/or cloud (service or batch pipelines MLOps practices).
• Signal Perception Understanding: Working knowledge of DSP/audio fundamentals and how ML interacts with perceptual outcomes.
• AI Tools: Demonstrated experience using AI-assisted tools to speed up coding testing debugging and documentation.

Bonus Points if You Have

• Experience with audio ML domains (speech enhancement denoising source separation spatial audio ML perceptual audio metrics recommendation/personalization).
• Familiarity with on-device acceleration (NNAPI Core ML concepts CUDA/TensorRT-like optimization where applicable).
• Experience with privacy-preserving learning or on-device personalization approaches.
• Patents/publications or shipped ML features in consumer/automotive audio products.

What Makes You Eligible

• Successfully complete a background investigation and drug screen as a condition of employment (post-offer).

What We Offer

• Flexible work environment allowing for full-time remote work globally for positions that can be performed outside a HARMAN or customer location
• Access to employee discounts on world-class products (JBL HARMAN Kardon AKG and more)
• Extensive training opportunities through our own HARMAN University
• Competitive wellness benefits
• Tuition reimbursement
• Be Brilliant employee recognition and rewards program
• An inclusive and diverse work environment that fosters and encourages professional and personal development

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HARMAN is proud to be an Equal Opportunity / Affirmative Action employer. All qualified applicants will receive consideration for employment without regard torace religion color national origin gender (including pregnancy childbirth or related medical conditions) sexual orientation gender identity gender expression age status as a protected veteran status as an individual with a disability or other applicable legally protected characteristics.