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Audio Machine Learning Intern Jobs in Ridgewood, NJ

What we're looking for At GPTZero, we ensure that machine learning models are created for the ... Past intern projects have been the focus of demos to VCs and state-level policy leaders.

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Audio Machine Learning Intern information

See Ridgewood, NJ salary details

$25.8K

$43.1K

$89K

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

As of Jul 3, 2026, the average yearly pay for audio machine learning intern in Ridgewood, NJ is $43,085.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,900.00 and $46,500.00 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 job categories do people searching Audio Machine Learning Intern jobs in Ridgewood, NJ look for? The top searched job categories for Audio Machine Learning Intern jobs in Ridgewood, NJ are:
What cities near Ridgewood, NJ are hiring for Audio Machine Learning Intern jobs? Cities near Ridgewood, NJ with the most Audio Machine Learning Intern job openings:
Infographic showing various Audio Machine Learning Intern job openings in Ridgewood, NJ as of June 2026, with employment types broken down into 2% As Needed, 62% Full Time, 29% Part Time, 2% Temporary, 2% Contract, and 3% Nights. Highlights an 94% Physical, 3% Hybrid, and 3% Remote job distribution, with an average salary of $43,085 per year, or $20.7 per hour.
Machine Learning Intern- Immediate Start

Machine Learning Intern- Immediate Start

Phamily

New York, NY โ€ข On-site

$30 - $35/hr

Full-time, Internship

Posted 9 days ago


Job description

Machine Learning Intern (R&D)
Location: New York, NY (Onsite - 3 Days per Week)
Employment Type: Full-Time Internship
Duration: Immediate Start Through End of Summer
Reporting To: Director of AI
Compensation: $30.00-$35.00 per Hour
Overview:
This is a hands-on Machine Learning Research & Development internship supporting the AI team in developing, testing, and optimizing machine learning models and AI-driven solutions. The intern will work closely with the Director of AI and engineering teams on real-world projects involving machine learning, data science, automation, and emerging AI technologies.
About Jaan Health/Phamily
Jaan Health is a leading AI-based care management company serving healthcare providers. For nearly a decade, the company has leveraged its easy-to-use, proprietary technology to enable health systems, medical groups, and ACOs to deliver high-quality, high-ROI proactive care to hundreds of thousands of previously underserved patients.
Phamily, the company's core technology platform, has transformed chronic disease management with clinically tested AI and easy-to-use technology that enables physicians and care teams to offer high-touch, individualized patient care that has been proven to reduce investment in extra labor and the overall cost of care. Phamily helps ensure healthcare providers are compensated fairly for providing high-quality care between office visits, while improving the lives of patients with chronic diseases. Learn more at phamily.com.
Job/Role Description:
Jaan Health is building AI-powered infrastructure to transform healthcare from reactive treatment to proactive care. Our platform, Phamily, helps providers manage chronic conditions at scale-improving patient outcomes while reducing costs.
We are looking for a Machine Learning Intern to work at the intersection of applied research and production systems, helping us advance cutting-edge AI in real-world healthcare environments. This role provides hands-on experience building, evaluating, and improving machine learning systems that directly impact patient outcomes and operational efficiency.
Key Responsibilities:
โ€ข Design and prototype novel ML approaches, especially in NLP, LLMs, and transformer architectures for healthcare use cases.
โ€ข Conduct applied research through experimentation, evaluation, and model iteration.
โ€ข Develop prompting strategies, fine-tuning techniques, and retrieval workflows.
โ€ข Translate research findings into scalable production-oriented systems.
โ€ข Build evaluation frameworks connecting model performance to healthcare outcomes.
โ€ข Collaborate with engineering and product teams to deploy AI-powered features.
โ€ข Work with large, real-world healthcare datasets and derive actionable insights.
โ€ข Document methodologies, findings, and technical recommendations.
Requirements:
โ€ข MS or PhD candidate in Machine Learning, Computer Science, or related field
โ€ข Strong background in deep learning, NLP, and/or LLMs
โ€ข Hands-on experience with PyTorch / TensorFlow / Hugging Face
โ€ข Proven ability to run experiments and derive insights from data
โ€ข Solid Python skills and comfort working with real-world, messy datasets
โ€ข Interest in bridging research โ†’ production impact
Preferred Requirements:
โ€ข Experience with conversational AI
โ€ข Experience with LLM evaluation, fine-tuning, or retrieval systems
โ€ข Exposure to healthcare data or applied ML in regulated domains
Work Style:
We are a fast-growing, early-stage company with a bold mission and significant work ahead; every employee at Jaan Health must embody growth company DNA. This means you have proven success in a high-performing environment: high velocity, strong ownership, comfort with ambiguity, resilience, and a true growth mindset.
You are both a playbook builder and executor, able to design scalable approaches for today while anticipating what the business will need tomorrow, and then follow through to deliver results.
Our culture is built on five principles that shape how we work, lead, and grow:
โ€ข Care: We put patients, clients, teammates, and outcomes first.
โ€ข Curiosity: We ask better questions, challenge assumptions, and keep learning.
โ€ข Clarity: We simplify complexity, communicate directly, and create alignment.
โ€ข Co-Creation: We collaborate across teams, perspectives, and disciplines.
โ€ข Craftsmanship: We execute with excellence, ownership, and continuous improvement.
What You'll Gain
Through this internship, you will gain:
โ€ข Work on high-impact, real-world AI problems in healthcare
โ€ข Own projects that go from research ideas โ†’ deployed systems
โ€ข Collaborate with a fast-moving, product-driven ML team
โ€ข Real-world experience in a fast-paced, high-growth environment
โ€ข Exposure to executive leaders and cross-functional teams
โ€ข Mentorship and professional development support
โ€ข Experience solving real business challenges
โ€ข Stronger communication, collaboration, and problem-solving skills
โ€ข Opportunity to build confidence, ownership, and business acumen
โ€ข A collaborative, mission-driven team helping transform healthcare at scale
If you take pride in delivering results, embrace challenges, and proactively seek improvement, then this is the place for you. You'll join a smart, humble, and collaborative team dedicated to improving healthcare.
Equal Employment Opportunity
Phamily is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, genetics, veteran status, sexual orientation, gender identity or expression, or any other legally protected status.