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Audio Machine Learning Jobs in California (NOW HIRING)

FieldAI

2.53 3D Machine Learning Engineer

Irvine, CA · On-site

They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D ... audio, and Building Information Models (BIM). • Work closely with the labeling and data ...

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

$150K - $200K/yr

What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and ...

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

$150K - $200K/yr

What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

$150K - $200K/yr

What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and ...

Apple

Machine Learning Engineer, SIML

Cupertino, CA

$216K - $324K/yr

Description We are seeking a machine learning research engineer with experience building modern ... audio/multimodal foundation models. You will stay at the forefront of the latest AI research to ...

Apple

Machine Learning Engineer, SIML

Cupertino, CA

$216K - $324K/yr

Description We are seeking a machine learning research engineer with experience building modern ... audio/multimodal foundation models. You will stay at the forefront of the latest AI research to ...

Meta

Audio Systems Engineer

Sunnyvale, CA

$144K/yr

Familiarity with audio algorithm development and/or machine learning techniques * 4+ years of experience designing and deploying objective test methodologies for consumer electronics devices and ...

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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 Aug 20, 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 is an audio machine learning?

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 does an audio machine learning do?

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 are the key skills and qualifications needed to thrive in audio machine learning?

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 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 August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $83,350 per year, or $40.1 per hour.

2.53 3D Machine Learning Engineer

FieldAI

Irvine, CA • On-site

Full-time

Re-posted 9 days ago

Job description

Job Summary:
FieldAI is a company based in Irvine, California, specializing in embodied AI and robotics. They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D machine learning models for processing reality capture data, contributing to automated progress tracking and scene understanding in construction environments.
Responsibilities:
• Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.
• Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).
• Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.
• Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).
• Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.
Qualifications:
Required:
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, or a related technical field.
• 2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.
• Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.
• Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.
• Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.
• Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.
• Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.
• Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.
Preferred:
• Experience working with BIM data, digital twins, or construction-related sensor data.
• Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations.
• Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.
• Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.
• Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).
• Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.
• Experience building custom modules for SparseConvNet or 3D transformers.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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