<p><b>Introduction: A Career at HARMAN Corporate</b></p><p>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.</p><ul><li>Enrich your managerial and organizational talents – from finance, quality, and supply chain to human resources, IT, sales, and strategy</li><li>Augment your comprehensive skillset with expert training across decision-making, change management, leadership, and business development</li><li>Obtain 360-degree support throughout your career life cycle, from early-stage to seasoned leader</li></ul><p></p><p><b>About the Role</b></p><p></p><p>The Audio ML Engineer (Research) develops <b>learning-based perception and personalization models</b> 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 behavior—designed from the start for <b>embedded and cloud</b> deployment paths. In Year 1, your work is expected to feed directly into <b>productization</b> 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 <b>and</b> remain robust in the messiness of real-world use cases.</p><p></p><p><b>What You Will Do</b></p><p></p><ul><li><b>Learning-Based Perception Models:</b> Develop ML models for perception-related tasks (e.g., quality prediction, artifact detection, scene/context classification, personalization embeddings, preference modeling).</li><li><b>Embedded + Cloud Deployment Focus:</b> 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).</li><li><b>Personalization & Adaptation:</b> 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.</li><li><b>Data Strategy & Tooling:</b> Define data collection and labeling strategies, data QA, augmentation, bias checks, and experiment tracking—so results are reproducible and transferable to product.</li><li><b>Model Optimization:</b> Apply compression/acceleration techniques (quantization, pruning, distillation, ONNX export, hardware-aware training) to meet latency and footprint constraints.</li><li><b>Cross-Functional Handoff:</b> Partner with DSP, perceptual, and productization engineers to deliver reference pipelines, integration guidelines, and acceptance metrics for OneUX releases.</li><li><b>AI Tools:</b> Use modern AI tooling (LLM-based coding assistants, data analysis copilots, automated report generation) to accelerate iteration while keeping rigorous review and validation.</li></ul><p></p><p><b>What You Need to Be Successful</b></p><p></p><ul><li><b>Education:</b> MS or PhD in CS/EE/Statistics/Applied ML (or BS with strong equivalent experience).</li><li><b>Experience:</b> <b>5+ years</b> applied ML engineering experience; <b>2+ years</b> specifically in audio/speech or time-series ML strongly preferred.</li><li><b>ML Stack:</b> Strong proficiency in <b>Python</b>, PyTorch/TensorFlow, dataset pipelines, evaluation methodology, and experiment tracking.</li><li><b>Deployment Skills:</b> Experience deploying models to <b>embedded</b> (TFLite / ONNX Runtime / custom inference) and/or <b>cloud</b> (service or batch pipelines, MLOps practices).</li><li><b>Signal + Perception Understanding:</b> Working knowledge of DSP/audio fundamentals and how ML interacts with perceptual outcomes.</li><li><b>AI Tools:</b> Demonstrated experience using AI-assisted tools to speed up coding, testing, debugging, and documentation.</li><li></li></ul><p><b>Bonus Points if You Have </b></p><p></p><ul><li>Experience with audio ML domains (speech enhancement, denoising, source separation, spatial audio ML, perceptual audio metrics, recommendation/personalization).</li><li>Familiarity with on-device acceleration (NNAPI, Core ML concepts, CUDA/TensorRT-like optimization where applicable).</li><li>Experience with privacy-preserving learning or on-device personalization approaches.</li><li>Patents/publications or shipped ML features in consumer/automotive audio products.</li></ul><p></p><p></p><p></p><p><b>What Makes You Eligible</b></p><p></p><ul><li>Successfully complete a background investigation and drug screen as a condition of employment (post-offer).</li><li>Ability to work from an office in Northridge, CA. 3+ days per week (hybrid)</li></ul><p></p><p></p><p><b>What We Offer </b></p><ul><li>Access to employee discounts on world-class products (JBL, HARMAN Kardon, AKG, and more)</li><li>Extensive training opportunities through our own HARMAN University</li><li>Competitive wellness benefits</li><li>Tuition reimbursement</li><li>“Be Brilliant” employee recognition and rewards program</li><li>An inclusive and diverse work environment that fosters and encourages professional and personal development</li></ul><p></p><p>#LI-DPWHITE1</p><p><b>#LI-hybrid</b></p>