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Audio Machine Learning Intern Jobs in Berkeley, CA

Hive

Machine Learning Engineer

San Francisco, CA

$120K - $180K/yr

You have successfully trained and deployed a deep learning machine model (image, NLP, video, or audio) into production, with measurably improved performance over baseline, either in industry or as a ...

Hive

Machine Learning Engineer

San Francisco, CA · On-site

$120K - $180K/yr

You have successfully trained and deployed a deep learning machine model (image, NLP, video, or audio) into production, with measurably improved performance over baseline, either in industry or as a ...

femtoAI

Audio Deep Learning Engineer

San Bruno, CA · On-site

Design, develop, and deploy deep-learning-based and classical DSP audio algorithms for our SPU ... Desired Skills and Experience Deep learning, Machine learning, DSP, Python, PyTorch Benefits ...

David AI

Research Scientist

San Francisco, CA · On-site

$210K - $360K/yr

About our Machine Learning team Our Machine Learning team sits at the intersection of cutting-edge research and production systems, transforming raw audio into high-signal data for leading AI labs ...

Crowe LLP

Machine Learning Engineer

San Francisco, CA

$62K - $100K/yr

Your Journey at Crowe Starts Here: At Crowe, you can build a meaningful and rewarding career. With real flexibility to balance work with life moments, you're trusted to deliver results and make an ...

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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 Aug 20, 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 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 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 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 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:

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The top searched job categories for Audio Machine Learning Intern jobs in Berkeley, CA are:

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Cities near Berkeley, CA with the most Audio Machine Learning Intern job openings:

Audio Machine Learning Intern jobs near you

Founding AI/Machine Learning Engineer

TriFetch

San Francisco, CA • On-site

Full-time

Re-posted 18 days ago

Job description

Job Summary:
TriFetch is a company focused on AI and machine learning solutions, and they are seeking a Founding AI/Machine Learning Engineer. The role involves architecting post-training stacks, leveraging proprietary data for model fine-tuning, and collaborating with co-founders to define the research roadmap.
Responsibilities:
• Architect the Post-Training Stack: Lead the design and execution of alignment pipelines (SFT, RLHF, RLAIF) that bridge the gap between "exam-passing" models and "clinically useful" systems.
• Leverage Proprietary Data: Utilize our proprietary and open source medical datasets to fine-tune models on edge cases that generic models miss.
• Novel Technique Experimentation: Research and implement cutting-edge post-training methods to optimize model performance, aiming for improvements in calibration and reliability critical for healthcare.
• Safety & Evaluation: Build rigorous evaluation frameworks (LLM-as-a-judge, benchmarks) to detect hallucinations, ensure clinical correctness, and guarantee safety before deployment.
• Strategic Collaboration: Work directly with the co-founders to define the research roadmap and platform strategy.
Qualifications:
Required:
• Hands-on experience with post-training models for specific applications (SFT, RLHF, RLAIF, Reward Modeling, Knowledge Distillation, etc)
• Deep understanding of Transformer architectures (attention mechanisms, positional encodings) and ML systems
• Experience with distributed training frameworks and optimizing training jobs on GPU clusters
• You thrive in ambiguous environments, learn quickly, and have a bias toward action
• Proficiency in Python, PyTorch
• Familiarity with the modern open-source LLM stacks (HuggingFace, Vertex, Vercel, etc.)
Preferred:
• Healthcare experience
• Published research in high-impact journals or top-tier ML/AI conferences (NeurIPS, ICML, ICLR, CVPR, ACL)
• Background working or interning at top research labs (e.g., FAIR, DeepMind, OpenAI, Google DM, MSR, Stanford/CMU/MIT labs)
• Experience dealing with multimodal health data, clinical reasoning, or safety-critical ML systems
• You have founded a company, built early-stage products, or enjoy the 'zero-to-one' phase of building
Company:
TriFetch is a San Francisco-based healthcare AI startup focused on bridging AI foundations with practical healthcare applications. Founded in 2024, the company is headquartered in San Francisco, USA, with a team of 2-10 employees. The company is currently Early Stage.

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