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Audio Machine Learning Intern Jobs in Berkeley, CA

Syntiant

Machine Learning Audio Intern

Redwood City, CA · On-site

Syntiant Corp., a leader in the high-growth AI software and semiconductor solutions space, is looking for a Machine Learning Audio Intern to take on a critical role to enhance our AI Model for Turkey ...

Syntiant

Machine Learning Audio Intern

Redwood City, CA · On-site

Syntiant Corp., a leader in the high-growth AI software and semiconductor solutions space, is looking for a Machine Learning Audio Intern to take on a critical role to enhance our AI Model for Turkey ...

Pravāh

ML Summer Intern

San Francisco, CA · On-site

$5K - $10K/mo

We apply modern machine learning to complex physical infrastructure problems spanning grid ... As an ML Intern at Pravah, you will work on real, open-ended technical problems at the frontier of ...

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All JobsAudio Machine Learning Intern JobsAudio Machine Learning Intern Jobs in Berkeley, CA

Audio Machine Learning Intern information

See Berkeley, CA salary details

$31.2K

$52.1K

$107.8K

How much do audio machine learning intern jobs pay per year?

As of May 30, 2026, the average yearly pay for audio machine learning intern in Berkeley, CA is $52,141.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,800.00 and $56,300.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Audio Machine Learning Intern, and why are they important?

To thrive as an Audio Machine Learning Intern, you need a solid background in signal processing, machine learning fundamentals, and programming skills, often supported by coursework or research in computer science or electrical engineering. Familiarity with Python, TensorFlow or PyTorch, and audio processing libraries like Librosa is typically required. Creativity, problem-solving abilities, and strong collaboration skills help you stand out in this role. These skills are crucial for developing innovative audio solutions, interpreting complex data, and working effectively within research or product teams.

What types of projects can an Audio Machine Learning Intern expect to work on during their internship?

As an Audio Machine Learning Intern, you can expect to be involved in projects such as developing and fine-tuning audio classification models, working on speech recognition algorithms, or improving the accuracy of sound event detection systems. You may also assist with the collection and preprocessing of audio datasets, as well as support model evaluation and optimization. Collaboration with data scientists, audio engineers, and software developers is common, offering a hands-on learning environment and exposure to end-to-end machine learning workflows in the audio domain.

What does an Audio Machine Learning Intern do?

An Audio Machine Learning Intern assists in developing and improving machine learning models that process and analyze audio data. Their tasks may include data preprocessing, feature extraction, model training, and evaluation for applications like speech recognition, sound classification, or music analysis. Interns often collaborate with engineers and researchers to experiment with new algorithms and optimize audio-based AI systems. This role provides hands-on experience in both audio signal processing and machine learning techniques.

What is the difference between Audio Machine Learning Intern vs Audio Data Analyst?

AspectAudio Machine Learning InternAudio Data Analyst
Required CredentialsTypically pursuing or recent graduate in Computer Science, Data Science, or related fieldsDegree in Data Analysis, Statistics, or related fields; may have certifications in data tools
Work EnvironmentResearch labs, tech companies, or startups focusing on AI and audio techData-driven departments within media, entertainment, or tech companies
Employer & Industry UsageUsed in AI development, research projects, and product innovationUsed for analyzing audio data, improving user experience, and reporting

The Audio Machine Learning Intern focuses on developing models and algorithms for audio data, often in research or development settings. In contrast, the Audio Data Analyst primarily interprets audio data to generate insights and support decision-making. Both roles require familiarity with audio data, but the intern role emphasizes machine learning skills, while the analyst role centers on data analysis and reporting.

What are popular job titles related to Audio Machine Learning Intern jobs in Berkeley, CA? For Audio Machine Learning Intern jobs in Berkeley, CA, the most frequently searched job titles are:
What job categories do people searching Audio Machine Learning Intern jobs in Berkeley, CA look for? The top searched job categories for Audio Machine Learning Intern jobs in Berkeley, CA are:
What cities near Berkeley, CA are hiring for Audio Machine Learning Intern jobs? Cities near Berkeley, CA with the most Audio Machine Learning Intern job openings:
Audio Machine Learning Intern jobs near you
Infographic showing various Audio Machine Learning Intern job openings in Berkeley, CA as of May 2026, with employment types broken down into 2% Internship, 84% Full Time, 11% Part Time, and 3% Temporary. Highlights an 83% Physical, and 17% Remote job distribution, with an average salary of $52,141 per year, or $25.1 per hour.
Machine Learning Audio Intern

Machine Learning Audio Intern

Syntiant

Redwood City, CA • On-site

Internship

Posted 24 days ago

Job description

Summary Description:

Syntiant Corp., a leader in the high-growth AI software and semiconductor solutions space, is looking for a Machine Learning Audio Intern to take on a critical role to enhance our AI Model for Turkey Gobble Synthesis.

Syntiant Corp. is seeking a turkey gobble detector AED model that runs on NDP chips. It is difficult to collect good quality gobble data due to several logistical issues. As of now, only ~2K samples are available for training such a model. These samples are not enough to train a production quality turkey gobble model.

EcoGen is a neural network model that could generate synthetic but real-sounding bird sounds. It needs only a handful of recordings to synthesize similar sounds. The idea is to leverage this model to get more data for training a better turkey gobble detector AEDmodel. For details on EcoGen, please refer to the hyperlink provided. There are newer models such as BirdDiff, Audio LDM (Text-to-Turkey), Perch 2.0 etc.

Requirements

Specific Duties and Responsibilities:

  • Understanding the model architecture.
  • Running it locally or on the cluster.
  • Fine-tuning the model on the available turkey sounds.
  • Synthesizing real-sounding artificial turkey gobble sounds.
  • Explore better alternatives and pursue them.

Qualifications, Education, and Experience Required:

  • Candidate pursuing a Bachelor's or Master’s degree in Computer Science or related field with hands-on experience in AI/ML model training.
  • Industry work experience is not required, but it would be good to have.

Benefits

About Syntiant:

Founded in 2017 and headquartered in Irvine, Calif., Syntiant Corp. is a leader in delivering hardware and software solutions for edge AI deployment. The company’s purpose-built silicon and hardware-agnostic models are being deployed globally to power edge AI speech, audio, sensor and vision applications across a wide range of consumer and industrial use cases, from earbuds to automobiles. Syntiant’s advanced chip solutions merge deep learning with semiconductor design to produce ultra-low-power, high performance, deep neural network processors. Syntiant also provides compute-efficient software solutions with proprietary model architectures that enable world-leading inference speed and minimized memory footprint across a broad range of processors. The company is backed by several of the world’s leading strategic and financial investors including Intel Capital, Microsoft’s M12, Applied Ventures, Bosch Ventures, the Amazon Alexa Fund, and Atlantic Bridge Capital. More information on the company can be found by visiting www.syntiant.com.