Audio AI Engineer

Zoom•San Jose, CA

6d•$137,700 - $275,400•Onsite

About The Position

As an Audio AI Engineer on Zoom's applied research team, you will develop and deploy machine-learning models to detect synthetic speech and audio deepfakes, helping protect platform trust and security. The role translates cutting-edge research into robust, production-ready systems and combines independent experimentation with collaboration across research, engineering, product, and security teams. The position is based at Zoom's San Jose, California office and requires on-site presence five days per week. Remote and hybrid arrangements are not available for this role. You will join Zoom's AI Research and Engineering team, whose audio AI group focuses on speech intelligence, security, and trust.

Requirements

PhD in Computer Science, Electrical Engineering, Machine Learning, Speech/Audio Processing, or a related field, or equivalent experience.
Research or industry experience in audio deepfake detection, synthetic speech detection, anti-spoofing, or speaker recognition.
Strong knowledge of speech and audio signal processing.
Deep understanding of machine learning, deep learning, model evaluation, and experimental design.
Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
Ability to conduct independent research and translate findings into practical, scalable solutions.
Strong problem-solving, collaboration, and communication skills.

Nice To Haves

Experience with generative audio, voice cloning, or adversarial machine learning.
Familiarity with large-scale or multilingual audio datasets.
Experience deploying and optimizing machine-learning models in production.
Publications in recognized speech, audio, AI, or machine-learning venues.

Responsibilities

Research, design, and develop machine-learning models for audio deepfake and synthetic speech detection.
Design training datasets, evaluation frameworks, and experimental pipelines for audio AI models.
Improve model accuracy and robustness across diverse speakers, languages, devices, codecs, and acoustic environments.
Analyze model failures and engineer solutions for reliable, real-world deployment.
Translate research prototypes into scalable, production-ready systems.
Collaborate with research, engineering, product, and security teams to align priorities and deliver work.
Track and apply emerging developments in speech AI, generative audio, voice cloning, and deepfake detection.