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Audio Machine Learning Intern Jobs in Colorado (NOW HIRING)

Preferred Qualifications Experience working with acoustic data, audio signal processing, or sensor data. Familiarity with machine learning concepts and experience using ML libraries (e.g., scikit ...

As a Data Science intern, you will play a key role in supporting critical finance initiatives while ... Learn to build and evaluate introductory machine learning models (e.g., linear/logistic regression ...

Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters ... speech/audio, and LLM-based systems * Build and maintain CI/CD workflows for ML services, model ...

Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters ... speech/audio, and LLM-based systems * Build and maintain CI/CD workflows for ML services, model ...

Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters ... speech/audio, and LLM-based systems * Build and maintain CI/CD workflows for ML services, model ...

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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 cities in Colorado are hiring for Audio Machine Learning Intern jobs? Cities in Colorado with the most Audio Machine Learning Intern job openings:
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.
Transportation Systems Intern (Year-Round)

Transportation Systems Intern (Year-Round)

The National Renewable Energy Laboratory (NREL)

Golden, CO • On-site

$44.50K - $71.20K/yr

Part-time

Medical, Dental, Vision, Retirement

Posted 15 days ago


Job description

Posting Title
Transportation Systems Intern (Year-Round)
Location
CO - Golden
Position Type
Intern (Fixed Term)
Hours Per Week
20
Working at NLR
NLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.
Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.
At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.
Job Description
The Advanced Computing Solutions Group in the NLR Computational Science Center has an opening for a graduate student researcher in in Transportation Systems with special emphasis on transportation system modeling and control. The researcher will assist in supporting large-scale modeling efforts for charging station impact analysis and developing AI-based traffic operation algorithms.
We are looking for a dynamic, motivated researcher with a strong technical background. The successful candidate will collaborate with NLR staff and researchers to design and implement data-informed traffic singal control and transportation system planning models to enable efficient and robust transportation system operations and planning.
Responsibilities include:
  • Assist in developing Reinforcement-Learning-based traffic control algorithms.
  • Collaborate with NLR researchers to build high-fidelity traffic simulations.
  • Support development of synthetic population, demand modeling, and total cost of ownership workflows.
  • Process and analyze large-scale transportation datasets.
  • Process and visualize results from simulations scenarios to inform real-time traffic operations and planning decisions.
  • Author, present and assist in the preparation of technical papers, reports and conference proceedings on topics related to data-driven traffic modeling and simulation and their application.

As a year-round intern, this role can adjust the working hours between 20-40 hours depending on your coursework and workload.
Basic Qualifications
Minimum of a 3.0 cumulative grade point average.
Graduate: Must be enrolled as a full-time student in a master's degree program from an accredited institution.
Post Graduate: Earned a master's degree within the past 12 months. Eligible for an internship period of up to one year.
Graduate + PhD: Completed master's degree and enrolled as PhD student from an accredited institution.
Please Note:
• Applicants are responsible for uploading official or unofficial school transcripts, as part of the application process.
• If selected for position, a letter of recommendation will be required as part of the hiring process.
• Must meet educational requirements prior to employment start date.
* Must meet educational requirements prior to employment start date.
Additional Required Qualifications
  • Strong programming skills in Python
  • Experience with data analysis and scientific computing
  • Experience working with large datasets
  • Strong analytical and problem-solving skills
  • Excellent written and verbal communication skills
  • Ability to work independently and collaboratively

Preferred Qualifications
  • Would prefer an intern candidate who is specifically in a PhD program.
  • Experience with transportation modeling or transportation systems analysis
  • Experience with HPC environments (Slurm, clusters, parallel computing)
  • Experience with machine learning and reinforcement learning
  • Experience working with synthetic population and travel demand modeling
  • Experience with SUMO

Job Application Submission Window
The anticipated closing window for application submission is up to 30 days and may be extended as needed.
Annual Salary Range (based on full-time 40 hours per week)
Job Profile: / Annual Salary Range: $44,500 - $71,200
NLR takes into consideration a candidate's education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee's salary history will not be used in compensation decisions.
Benefits Summary
Benefits include medical, dental, and vision insurance; 403(b) Employee Savings Plan with employer match*; and sick leave (where required by law). NLR employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement. Internships projected to be less than 20 hours per week are not eligible for medical, dental, or vision benefits.
* Based on eligibility rules
Badging Requirement
NLR is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as required by Homeland Security Presidential Directive 12 (HSPD-12), which includes a favorable background investigation. Intern assignments extending beyond six months will be subject to this requirement.
Drug Free Workplace
NLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.
If you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.
Submission Guidelines
Please note that in order to be considered an applicant for any position at NLR you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.
Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
Reasonable Accommodations
E-Verify www.dhs.gov/E-Verify For information about right to work, click here for English or here for Spanish.
E-Verify is a registered trademark of the U.S. Department of Homeland Security. This business uses E-Verify in its hiring practices to achieve a lawful workforce.