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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

$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 ...

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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 Sep 9, 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, 71% Full Time, 25% Part Time, and 3% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $83,350 per year, or $40.1 per hour.

Senior Machine Learning Research Engineer

San Francisco, CA • On-site

AI Talent Now
See more jobs at AI Talent Nowexternal

$200 - $250/hr

Other

Posted 3 days ago

New

Job description

Senior Machine Learning Research Engineer

San Francisco, United States | Posted on 09/03/2026

AI Talent Now, LLC is a powerhouse in direct-hire talent acquisition, connecting exceptional talent with industry-leading organizations across IT, Engineering, Financial Services & Fintech, and Manufacturing & Robotics. Headquartered in Atlanta, Georgia, we proudly serve clients and candidates nationwide.

Job Description

AI Talent Now job # ZR 104

We are looking for a Machine Learning Research Engineer with 5+ years of experience (PhD + 1 year industry, or 5 years industry with publications) to own the full lifecycle of ML research and development at the frontier of audio AI. You'll join a fast-growing team backed by NVIDIA and top-tier investors, building cutting-edge speech and audio models that power the data layer for the world's leading AI labs. This is a role for someone who thrives in a research-driven environment, has published at top conferences, and is excited to take end-to-end ownership of novel ML directions — from design and training to deployment and fine-tuning.

What will you be doing?

  • Owning full research directions end-to-end — designing, training, deploying, and fine-tuning ML models for speech and audio applications
  • Publishing and advancing the state of the art in speech, audio, and multimodal ML (NeurIPS, ICML, ICLR caliber work)
  • Building production-grade inference systems and resilient pipelines that process terabytes of audio data daily
  • Collaborating cross-functionally with operations and engineering teams to gather training/evaluation datasets and improve model quality
  • Setting ML roadmaps and mentoring other engineers as the team scales
Requirements
  • Must have
  • 1+ years in industry with full lifecycle ML ownership: design, train, deploy, fine-tune
  • Education:
  • PhD from a top-25 CS program (or 5+ years industry ML research experience without PhD)
  • Baseline
  • Seniority:
  • 5 - 15 years of experience in ML research engineering, with focus on speech/audio/multimodal models using Python and PyTorch
  • Experience at a high-growth startup
  • Research in speech, audio, multimodal, text-to-speech, or related domains
  • Strong Python and PyTorch with production deployment experience
  • Excited to own a research direction end-to-end independently
  • Based in or willing to relocate to San Francisco (in-office)
  • At least 1 year of industry experience (post-PhD)
  • Nice-to-have
  • Experience at a top AI lab or research-focused startup (e.g., DeepMind, xAI, Anthropic, OpenAI, Meta FAIR)
  • Mix of big company and startup experience
  • Education
  • Many publications, especially at NeurIPS, ICML, or ICLR in speech, audio, multimodal, or related areas
  • Many publications, especially at NeurIPS, ICML, or ICLR in speech, audio, multimodal, or related areas
  • Updated
  • Experience training large neural network models
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