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Job DescriptionSalary: $135,000 - $180,000 Location 100% Remote. We hire anywhere within the U.S. Company Overview Antares Audio Technologies is the pioneering force behind revolutionary audio processing solutions, including the industry-standard AutoTune pitch correction technology. For over two decades, we've been at the forefront of audio innovation, creating tools used by Grammy-winning artists, major recording studios, and content creators worldwide. We're seeking a talented Audio Machine Learning Engineer to join our R&D team and help define the next generation of intelligent audio processing technologies. Antares Audio Technologies is the leader in vocal enhancement software and maker of AutoTune, the best known brand in the music technology industry. AutoTune is a staple among professional singers, producers, and engineers with a near ubiquitous presence in production studios. About the Position We are looking for an experienced Audio Research Engineer to design, develop, and deploy cutting-edge machine learning models and signal processing for professional audio applications. You will work on challenging problems including intelligent pitch correction, formant shifting, vocal isolation, harmonic analysis, real-time audio effects, and next-generation creative tools for music production. Key Responsibilities Model Development & Research • Design and implement deep learning models for audio signal processing, with focus on pitch detection, formant modification, vocal processing, and harmonic analysis, • Develop solutions for source separation, vocal isolation, pitch correction, and intelligent audio effects, • Research and implement state-of-the-art techniques in music information retrieval and audio synthesis, • Optimize models for real-time performance in professional Digital Audio Workstation (DAW) environments Data Engineering & Pipeline Development • Build robust data pipelines for processing large-scale music and vocal datasets, • Implement audio preprocessing, pitch tracking, and spectral feature extraction techniques, • Design training infrastructure for models requiring high-quality audio data, • Develop automated model evaluation frameworks specific to music production quality metrics Production & Deployment • Deploy ML models as VST/AU/AAX plugins and standalone applications, • Ensure ultra-low latency performance suitable for professional recording environments, • Implement A/B testing frameworks for audio quality assessment with professional users, • Monitor model performance across different musical genres and vocal styles, • Collaborate with plugin developers to integrate ML models into Antares' product suite Cross-functional Collaboration • Work closely with product teams and professional audio engineers to understand musician and producer needs, • Partner with DSP engineers and audio researchers on breakthrough audio technologies, • Collaborate with user experience teams to design intuitive interfaces for complex audio processing, • Engage with the music production community to gather feedback and validate new features Required Qualifications Education & Experience • Master's or PhD in Computer Science, Electrical Engineering, Machine Learning, or related field, • 3+ years of experience in machine learning with focus on music/audio processing and professional audio applications, • Strong background in digital signal processing, music theory, and audio engineering fundamentals Technical Skills • Programming Languages: Proficiency in Python; experience with C++ for performance-critical applications strongly preferred, • ML Frameworks: Deep experience with PyTorch or TensorFlow, familiarity with specialized audio ML libraries (librosa, torchaudio, onnxruntime, ESPnet), • Audio Processing: Expert understanding of pitch detection algorithms, spectral analysis, and time-frequency representations, • Deep Learning: Hands-on experience with audio-specific architectures (WaveNet, CREPE, Tacotron) and generative models, • Plugin Development: Experience with VST/AU/AAX plugin development and real-time audio processing, • Software Engineering: Strong C++ skills for performance-critical audio applications, Python for ML development Domain Knowledge • Deep understanding of music theory, harmonic analysis, and vocal acoustics, • Experience with music production workflows and Digital Audio Workstations (Pro Tools, Logic, Ableton), • Knowledge of audio plugin standards (VST, AU, AAX) and real-time audio constraints, • Familiarity with pitch tracking, source separation, and vocal processing techniques Preferred Qualifications • PhD in audio signal processing, music technology, or acoustics, • Publications in audio engineering conferences (AES, ICASSP, ISMIR, DAFx), • Experience with generative audio models and neural audio synthesis, • Background in music production or professional audio engineering, • Knowledge of music copyright and licensing considerations for ML training data, • Experience with cross-platform plugin development and optimization Technical Environment • Languages: Python, C++, JUCE framework for audio applications, • Frameworks: PyTorch, TensorFlow, specialized audio ML libraries, • Infrastructure: On-premises high-performance computing clusters, cloud burst capabilities, • Tools: Pro Tools, Logic Pro, Ableton Live, MATLAB, Jupyter, Git, • Audio Stack: JUCE, WebAssembly, Core Audio, iOS, professional audio interfaces What We Offer • Comprehensive health, dental, and vision insurance, • Access to world-class recording studios and professional audio equipment, • Flexible work arrangements, • Performance bonuses, • Unlimited PTO, • Paid Federal holidays, including a week off for everyone at the end of the year, • Paid Parental Leave, • 4% 401k match after 6 months, • Music production software licenses and professional development opportunities Full compensation packages are based on candidate experience. Compensation ranges are established using national benchmarking data and apply across all geographic locations within the United States. Antares is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity or expression, age, national origin, veteran status, genetic information, union status and/or beliefs, or any other characteristic protected by federal, state, or local law. remote work