Perceptual Audio Engineer (Research)

HARMAN International is a global, multi-disciplinary team focused on innovation in technology. The Perceptual Audio Engineer (Research) will develop metrics and methods that connect algorithms to user perception, design listening tests, and create perceptual datasets to ensure high-quality audio products.

Responsibilities:

• Metrics & Methods Development: Create perceptual metrics and evaluation methodologies that correlate with human preference and quality judgments (timbre, artifacts, clarity, spatial realism, comfort/fatigue)
• User Testing & Listening Studies: Design and execute subjective evaluations (ABX, preference tests, rating studies), including stimulus generation, panel guidance, statistical analysis, and repeatability
• AI/ML-Enabled Evaluation: Where beneficial, develop ML-based quality prediction or artifact detection that supports rapid iteration (embedded-viable where relevant; cloud-viable for batch evaluation)
• Perception-to-Product Gatekeeping: Define acceptance criteria and regression tests so OneUX productization can enforce “no perceptual regressions” in CI-style workflows
• Data & Experiment Discipline: Build and maintain clean datasets, labeling strategies, metadata, and experiment tracking so results are trustworthy and reusable
• Cross-Functional Collaboration: Work closely with DSP, acoustics, ML, and system testing roles to ensure metrics reflect real user value and cover edge cases
• AI Tools: Use AI tools to accelerate analysis and reporting while maintaining rigorous validation and traceability

Requirements:

• Education: MS or PhD in EE/CS/Acoustics/Psychoacoustics/Statistics (or equivalent experience)
• Experience: 5+ years in perceptual audio, audio quality evaluation, or related applied research/engineering
• Perception + Statistics: Strong understanding of psychoacoustics and statistical analysis for human-subject experiments. Experience in using and developing perceptual models
• Programming: Python and/or MATLAB for analysis and tooling; familiarity working with ML frameworks and basic DSP concepts
• AI Tools: Experience using AI-assisted tools for data analysis, scripting, and documentation in a reproducible workflow
• Experience with perceptual evaluation for spatial audio, headphone tuning, or automotive cabin audio
• Experience creating objective metrics that become organizational standards and materially change decision-making speed/quality
• Familiarity with embedded deployment constraints for ML-based metrics
• Patents/publications in perceptual audio or audio quality evaluation