Harman is looking for an Audio ML Engineer to develop 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 behavior—designed from the start for embedded and cloud deployment paths.
What You'll Do
- Develop ML models for perception-related tasks, such as quality prediction, artifact detection, scene/context classification, personalization embeddings, and preference modeling.
- Design solutions that can run on-device (quantized, efficient inference) and/or scale in cloud pipelines (batch evaluation, fleet learning, offline training plus on-device inference).
- Build personalization and adaptation strategies that integrate with DSP pipelines while maintaining stability and explainability.
- Define data collection and labeling strategies, data QA, augmentation, bias checks, and experiment tracking.
- Apply compression and acceleration techniques to meet latency and footprint constraints.
- Partner with DSP, perceptual, and productization engineers to deliver reference pipelines, integration guidelines, and acceptance metrics.
- Use modern AI tooling to accelerate iteration while keeping rigorous review and validation.
What We're Looking For
- MS or PhD in CS/EE/Statistics/Applied ML (or BS with strong equivalent experience).
- 5+ years of applied ML engineering experience.
- 2+ years specifically in audio/speech or time-series ML strongly preferred.
- Strong proficiency in Python, PyTorch/TensorFlow, dataset pipelines, evaluation methodology, and experiment tracking.
- Experience deploying models to embedded (TFLite / ONNX Runtime / custom inference) and/or cloud (service or batch pipelines, MLOps practices).
- Working knowledge of DSP/audio fundamentals and how ML interacts with perceptual outcomes.
- Demonstrated experience using AI-assisted tools to speed up coding, testing, debugging, and documentation.
Nice to Have
- Experience with audio ML domains (speech enhancement, denoising, source separation, spatial audio ML, perceptual audio metrics, recommendation/personalization).
- Familiarity with on-device acceleration concepts.
- Experience with privacy-preserving learning or on-device personalization approaches.
- Patents/publications or shipped ML features in consumer/automotive audio products.
Technical Stack
- Python
- PyTorch/TensorFlow
- TFLite
- ONNX Runtime
Benefits & Compensation
- Compensation: $134,250 - $196,900
- 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 HARMAN products
- Extensive training opportunities through HARMAN University
- Competitive wellness benefits
- Tuition reimbursement
- “Be Brilliant” employee recognition and rewards program
- An inclusive and diverse work environment that fosters professional and personal development
Work Mode
This role operates in a hybrid work mode.
HARMAN is proud to be an Equal Opportunity / Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, 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.
