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Audio Machine Learning Intern Jobs in Colorado (NOW HIRING)

Apple

Data Scientist - Hardware Acoustics

Boulder, CO · On-site

... audio experiences across all our products. We are a multidisciplinary team of acoustic engineers, researchers, and machine learning experts who push the boundaries of sound quality, noise ...

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Audio Machine Learning Intern information

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 the most commonly searched types of Audio Machine Learning jobs in Colorado? The most popular types of Audio Machine Learning jobs in Colorado are:
What are popular job titles related to Audio Machine Learning Intern jobs in Colorado? For Audio Machine Learning Intern jobs in Colorado, the most frequently searched job titles are:
What job categories do people searching Audio Machine Learning Intern jobs in Colorado look for? The top searched job categories for Audio Machine Learning Intern jobs in Colorado are:
What cities in Colorado are hiring for Audio Machine Learning Intern jobs? Cities in Colorado 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 Colorado as of May 2026, with employment types broken down into 2% Internship, 87% Full Time, 7% Part Time, 3% Temporary, and 1% Contract. Highlights an 97% Physical, and 3% Remote job distribution.

AI/Machine Learning, Summer Intern (Hybrid)

accuris

Denver, CO

$17/hr

Other

Posted yesterday

Job description

AI/Machine Learning Summer Intern Supply Chain Intelligence | Accuris

Division: Supply Chain Intelligence

Level: Undergraduate (Junior/Senior) or Graduate (MS/MBA) 

Location: Denver, CO – Hybrid (3 days on-site per week)

Duration: June 1 – August 3–10, 2026 | 9–10 weeks

Compensation: Paid – $17/hour

ABOUT THE ROLE

Accuris's Supply Chain Intelligence division is transforming how engineers, procurement teams, and sustainability leaders understand the global electronics supply chain. We are looking for a creative, technically strong AI/ML Summer Intern to join our team and help build the next generation of AI-powered capabilities — from carbon footprint calculators for electronic components to predictive algorithms for supply chain risk and availability.

This is a hands-on, build-first internship. You will go from idea to working prototype, collaborating closely with product managers, engineers, and data scientists. By the end of the summer, you will have shipped a real AI tool and presented it to audiences ranging from engineers to senior executives.

WHAT YOU'LL WORK ON

  • Design and build AI-powered prototypes such as carbon footprint calculators for electronic components or predictive models for supply chain risk, demand, and component availability.
  • Apply LLM and generative AI techniques to create intelligent, data-driven tools using platforms like OpenAI, Anthropic Claude, or LangChain.
  • Develop and validate machine learning models using Python and standard ML libraries (scikit-learn, PyTorch, TensorFlow, etc.).
  • Work with cloud-based data pipelines, SQL databases, and dashboards to source and transform supply chain data.
  • Use rapid "vibe coding" methodologies to iterate quickly on AI concepts and validate ideas early.
  • Translate your technical work into clear, compelling presentations for both engineering teams and executive audiences.

WHAT YOU'LL DELIVER

By the end of the summer, you will be expected to deliver two things:

  • A working AI prototype — a functional tool or model that demonstrates clear value against a supply chain intelligence use case (e.g., component carbon footprint estimator, predictive availability scorer, or similar).
  • An executive-ready presentation — a polished deck communicating your approach, methodology, findings, and recommended next steps for the business.

REQUIRED QUALIFICATIONS

  1. Currently enrolled as a Junior or Senior undergraduate, or a Graduate (MS or MBA) student in Computer Science, Data Science, Electrical Engineering, Information Systems, or a related field.
  2. Demonstrated experience building AI applications — whether through coursework, personal projects, open-source contributions, or prior internships.
  3. Proficiency in Python with hands-on experience using ML libraries such as NumPy, Pandas, scikit-learn, PyTorch, or TensorFlow.
  4. Experience working with LLM/GenAI platforms (e.g., OpenAI API, Anthropic Claude, LangChain, RAG pipelines, or prompt engineering).
  5. Familiarity with cloud platforms (AWS, Azure, or GCP) and data tools including SQL and data pipeline or dashboard tooling.
  6. Strong written and verbal communication skills; able to present technical concepts clearly to both technical peers and non-technical stakeholders.
  7. Self-starter with the ability to move fast, iterate, and learn from ambiguous, real-world data problems.

PREFERRED QUALIFICATIONS

  • Prior exposure to supply chain, electronics manufacturing, procurement, or sustainability/ESG domains.
  • Familiarity with carbon accounting frameworks, life cycle assessment (LCA), or sustainability data (e.g., GHG Protocol, Scope 3 emissions).
  • Experience building and evaluating predictive models for time-series, classification, or regression problems.
  • Active portfolio of AI/ML projects (e.g., GitHub, Kaggle, Hugging Face, or personal website).
  • Comfort with rapid prototyping and "vibe coding" — the ability to quickly scaffold and iterate on AI-driven tools.

ABOUT ACCURIS

Accuris provides engineers, procurement specialists, and product teams with trusted data and intelligence to design better products and build more resilient supply chains. Our Supply Chain Intelligence division delivers real-time component data, risk analytics, and predictive insights to help global organizations make faster, smarter sourcing decisions. This internship puts you at the frontier of AI applied to one of the world's most complex and consequential industries.

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