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Audio Machine Learning Engineer, Google Beam

Exclusive opportunity

Onsite

Audio Machine Learning Engineer, Google Beam

Google

Audio Machine Learning Engineer, Google Beam

Skills

Deep LearningPythonKerasMachine Learning

23 days ago

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Important information


Contract type:

Permanent, Temporary

Salary:

$207,000 – $300,000 / year

Location:

San Francisco, CA, USA

Work mode:

Onsite

Published on:

29 September 2026

What they need


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.

About the job

Google Beam is developing advanced software and hardware technology to enable people who are separated by distance to experience a conversation as if they were together. In this role, you will have the opportunity to have a big impact inventing the future of communication with Google products. We are a distributed team with members in Seattle, Mountain View, San Francisco, Los Angeles, New York, and points in between.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

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 TFRoomSim 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.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.

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