Job Description

At Dolby, science meets art, and high tech means more than computer code. We continue to revolutionize how people create, deliver, and enjoy entertainment worldwide. To do that, we need the absolute best talent—people inspired by the intersection of audio engineering and artificial intelligence.

We are currently building the next generation of Mobile AI Audio/Video products. This role is responsible for establishing a comprehensive quality evaluation system for multimedia AI capabilities, covering audio algorithms, model performance, and real-world mobile use cases.

As a Quality & Evaluation Engineer, you will be the bridge between algorithmic potential and user experience. You will help define how AI-driven audio "should" sound. We are looking for someone who doesn't just check if a feature works, but asks: "Is the sound authentic?" "Is the experience natural?" "Is the model stable?"

This position is ideal for engineers with a solid foundation in Audio Engineering, Acoustics, Signal Processing, or Music Technology who want to build a long-term career in the AI A/V domain.

Responsibilities

1. Architecting AI Audio Evaluation Systems

• Design and implement subjective and objective audio quality assessment methodologies.
• Establish verification workflows for AI algorithms, including Deep Noise Suppression (DNS), Acoustic Echo Cancellation (AEC), Speech Separation, and Spatial Audio.
• Define testing standards across diverse application scenarios (Mobile, Headphones, Live Streaming, RTC, etc.).

2. Audio Data Analysis & Performance Evaluation

• Utilize Python and other tools for advanced audio signal analysis (spectral analysis, THD, latency, stability, and artifacts).
• Conduct batch testing on AI models to analyze performance across different noise environments and hardware constraints.
• Generate structured evaluation reports that provide data-driven insights for algorithm optimization.

3. Test Tooling & Automation Support

• Develop and maintain automation scripts for audio processing and data collection.
• Build batch-processing pipelines for large-scale audio datasets.
• Support model regression verification within CI/CD environments.

4. Problem Triage & Cross-functional Collaboration

• Identify, isolate, and help resolve complex audio artifacts or performance bottlenecks.
• Collaborate closely with Research, System, and Product teams to drive issue resolution.
• Participate in the rapid prototyping and verification of new technologies.

Qualifications

• Bachelor’s degree or higher in Audio Engineering, Music Engineering/Technology, Music Computing, Electrical & Computer Engineering (Audio/DSP track), or a related field with audio-specific project experience.
• Solid understanding of Digital Signal Processing (DSP) fundamentals (FFT, Filtering, Time-Frequency analysis, etc.).
• Proficiency in Python for data analysis and scripting.
• Familiarity with or exposure to Deep Learning applications in audio (e.g., noise reduction, source separation).
• Strong curiosity and research skills—the ability to read academic papers and translate findings into practical test cases.
• Excellent English communication skills (written and oral) for seamless collaboration with global R&D teams.

Preferred Qualifications

• Experience in Subjective Listening Tests (MOS, MUSHRA, etc.).
• Hands-on experience in Music Production, Recording, Mixing, or laboratory acoustics.
• Experience with PyTorch or TensorFlow for model inference or evaluation.
• Previous involvement in audio algorithm research or commercial audio projects.
• International study background or experience in cross-cultural professional environments.
• Proficiency in a musical instrument or experience creating content in Dolby Atmos.

What We Look For (The Plus Factor)

• Acoustic Sensitivity: You have "Golden Ears" and can articulate what sounds right and what doesn't.
• Analytical Mindset: You can explain perceptual feelings with hard data.
• Passion: A genuine, long-term interest in the future of AI-driven audio/video.
• Growth Mindset: An eagerness to evolve alongside product-level quality systems.

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