ZipRecruiter

Log In

1

Audio Machine Learning Jobs in California (NOW HIRING)

Apple

Machine Learning Engineer - LLM

Cupertino, CA

$147K - $220K/yr

Description We are seeking a highly capable and dynamic Machine Learning Engineer to work cross-functional in building out a GenAI system in support of Beats and Apple Audio products. The ideal ...

next page

Showing results 1-20

People also search for

All JobsAudio Machine Learning JobsAudio Machine Learning Jobs in California

Audio Machine Learning information

See California salary details

$29.1K

$83.3K

$169.3K

How much do audio machine learning jobs pay per year?

As of Jun 7, 2026, the average yearly pay for audio machine learning in California is $83,350.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,300.00 and $111,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Audio Machine Learning position, and why are they important?

To thrive in Audio Machine Learning, you need a strong background in machine learning, digital signal processing, and proficiency with programming languages such as Python or MATLAB, typically supported by a relevant degree in computer science, electrical engineering, or a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with audio libraries (e.g., Librosa), and knowledge of cloud computing tools are highly valued, as are certifications in AI or data science. Strong problem-solving skills, creativity, and effective communication are essential soft skills for success in this field. These skills are crucial for developing innovative solutions, collaborating across multidisciplinary teams, and addressing complex audio data challenges in real-world projects.

What are the typical daily responsibilities of someone working in Audio Machine Learning?

Professionals in Audio Machine Learning typically spend their days designing, developing, and optimizing machine learning models tailored to audio data, such as speech or music recognition systems. You may also preprocess large datasets, extract and engineer relevant features, and collaborate closely with data scientists, audio engineers, and software developers to integrate your work into larger applications. Regular tasks often include running experiments, evaluating model performance, tuning hyperparameters, and keeping up with the latest advancements in the field. Team meetings, code reviews, and presenting findings to stakeholders are also common parts of the workweek.

What is an Audio Machine Learning job?

An Audio Machine Learning job involves developing algorithms and models that analyze, process, and generate audio data. Responsibilities typically include working with speech recognition, music analysis, sound classification, and audio enhancement. Professionals in this field use deep learning, signal processing, and neural networks to improve audio-based applications like voice assistants, noise reduction systems, and music recommendation engines. They often work with datasets of speech, music, or environmental sounds to build models that understand and manipulate audio signals effectively.

What are the most commonly searched types of Audio Machine Learning jobs in California? The most popular types of Audio Machine Learning jobs in California are:
What are popular job titles related to Audio Machine Learning jobs in California? For Audio Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Audio Machine Learning jobs in California look for? The top searched job categories for Audio Machine Learning jobs in California are:
What cities in California are hiring for Audio Machine Learning jobs? Cities in California with the most Audio Machine Learning job openings:
Audio Machine Learning jobs near you
Infographic showing various Audio Machine Learning job openings in California as of May 2026, with employment types broken down into 99% Full Time, and 1% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $83,350 per year, or $40.1 per hour.
Senior Machine Learning - Avatar, Core AI

Senior Machine Learning - Avatar, Core AI

Roblox

San Mateo, CA

$142K - $197K/yr

Other

Posted yesterday

Job description

As a Senior Machine Learning Engineer, you will work on challenging problems leveraging state-of-art AI/ML to empower a new generation of Roblox experiences for our growing community of users. We are looking for senior AI/ML engineers to lead the vision of low compute cost, in experience machine learning at Roblox, taking leads on designing core components of on-device ML operations. We are designing a system for advancing model architecture, enabling more efficient deployment to cloud and client, while delivering on complex operations including Generative AI model deployment. The engineer will be driving the architecture optimization across a large range of compute devices, making efficient use of available compute resources. Join us in our exciting journey!

You will:
  • Develop and implement state-of-the-art ML models
  • Stay up-to-date with the latest research in ML and related fields, and apply this knowledge to improve the model performance
  • Establish a pipeline for delivering Roblox ML model on range of computing devices
  • Build cross-functional partnership on deployment and architecture optimization
  • Design and implement primary components of the in-experience machine learning system.
You have:
    • 2+ years experience in developing machine learning models, preferably in image, video, audio or 3D representations with Python
    • A strong understanding of deep learning frameworks including PyTorch and Tensorflow.
    • Experience in shipping ML features in production environments, including low powered devices.
    • Excellent cross-functional skills, partnering with organization leaders, driving to a conclusion.
    • Knowledge in state-of-the-art deep learning network architectures and primitives.
    • Experience with model optimization techniques.

Apply