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Audio Machine Learning Intern Jobs in Ridgewood, NJ

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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:
Audio Machine Learning Intern jobs near you
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.

Staff Machine Learning Engineer - Policy & Safety

Spotify

New York, NY • On-site

$227K - $324K/yr

Other

Medical, Retirement, PTO

Posted 28 days ago

Job description

We design Spotify's consumer experience-end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints-from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify.
About the Team
The Policy & Safety team sits within the Content Platform domain and builds the systems that keep Spotify safe and trustworthy at scale. We own the infrastructure behind content moderation, including detection models, policy enforcement systems, compliance pipelines, and the safety-by-default platform.
Our work sits on the critical path of every new content type and product experience-from messaging and comments to collaborative and agentic features. We partner closely with Trust & Safety, Legal, and Public Affairs to ensure that as Spotify evolves, safety is built in from the start-not added later.
What You Will Do
  • Build and scale machine learning systems for proactive content detection, classification, and pre-publish safety scanning
  • Design and implement policy evaluation frameworks, including standardized datasets, offline and online metrics, and continuous improvement loops
  • Develop multimodal models that combine text, audio, image, and video signals for safety and policy enforcement
  • Architect feedback loops that turn human reviewer input into structured training data for continuous model improvement
  • Translate regulatory requirements (e.g., precision/recall obligations, compliance reporting) into scalable ML system designs
  • Partner with cross-functional teams across Trust & Safety, Legal, Public Affairs, and Product to deliver safe user experiences
  • Drive technical direction in ambiguous problem spaces and contribute to long-term platform architecture
  • Mentor and support other machine learning engineers, helping raise the bar across the team

Who You Are
  • You have experience building and shipping production-grade machine learning systems at scale
  • You have strong expertise in ML evaluation, including dataset design, metrics, and model performance monitoring
  • You have worked with multimodal machine learning systems across text, audio, image, or video domains
  • You are experienced with human-in-the-loop systems, active learning, or feedback-driven model improvement
  • You are comfortable translating complex requirements into technical solutions, including regulatory or policy constraints
  • You have experience working across teams and influencing technical direction in large-scale systems
  • You are comfortable navigating ambiguity and making thoughtful decisions that balance speed, quality, and risk
  • You communicate clearly and collaborate effectively with both technical and non-technical stakeholders

Where You Will Be
  • This role is based in New York, NY
  • We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.

The United States base range for this position is $227,495-$324,993 USD, plus equity. The benefits available for this position include health insurance, six-month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, and paid sick leave. These ranges may be modified in the future.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. Find our AI notice here: https://lifeatspotify.com/ai-notice

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