Audio Machine Learning Engineer, Google Beam

Responsibilities: Define the roadmap and set goals and priorities for the Audio ML effort. Coordinate workstreams with supporting team members. Work closely with research scientists, audio engineers, and software developers to integrate your models into the Starline system and launch groundbreaking audio features. Train, evaluate, and fine-tune models for speech enhancement, sound separation, classification, and remixing. Develop and maintain the audio infrastructure in C++, ensuring scalability, reliability, and efficiency. Optimize model inference for real-time, low-latency performance on both CPU and GPU platforms. Design and implement data processing pipelines to prepare and augment training data, leveraging techniques like TFRoom Sim and automated labeling. Polish models and infrastructure to deliver optimal processing. Participate in On-Duty rotations to address alerts and concerns across the Audio stack, including performing diagnosis and triage of incoming bugs and issues. In most instances, this position requires in-person interviews as part of the hiring process. Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.

Minimum Qualifications: Bachelor's degree or equivalent practical experience.8 years of experience in software development.5 years of experience working with and evaluating audio or speech processing ML models.5 years of experience with speech enhancement, audio quality assessment, audio signal processing, and sound separation.5 years of experience working in C++ and Python to develop, integrate, and maintain code.5 years of experience analyzing problems, designing solutions, and implementing them with code.

Preferred Qualifications: Master's degree or PhD in Engineering, Computer Science, or a related technical field. Extensive experience in Python programming, including deep learning frameworks (JAX, Keras, etc.).Experience with real-time audio processing and optimization techniques. Experience with understanding issues and either managing them or routing them appropriately. Excellent communication and teamwork skills, with the ability to grow in a multidisciplinary environment.