Responsibilities : • Design, build, and optimize scalable machine learning pipelines for multimodal model training, fine-tuning, and evaluation across text, image, audio, video, and 3D data. • ...
Responsibilities : • Design, build, and optimize scalable machine learning pipelines for multimodal model training, fine-tuning, and evaluation across text, image, audio, video, and 3D data. • ...
Responsibilities : • Design, build, and optimize scalable machine learning pipelines for multimodal model training, fine-tuning, and evaluation across text, image, audio, video, and 3D data. • ...
Responsibilities : • Design, build, and optimize scalable machine learning pipelines for multimodal model training, fine-tuning, and evaluation across text, image, audio, video, and 3D data. • ...
2026 Fall Applied Science Internship - Recommender Systems/ Information Retrieval (Machine Learning)
Seattle, WA · On-site
Must be eligible and available for a full-time (40h / week) 12 week internship between May 2026 and September 2026 Amazon has positions available for Machine Learning Applied Science Internships in ...
2026 Fall Applied Science Internship - Recommender Systems/ Information Retrieval (Machine Learning)
Seattle, WA · On-site
Must be eligible and available for a full-time (40h / week) 12 week internship between May 2026 and September 2026 Amazon has positions available for Machine Learning Applied Science Internships in ...
Collaborate cross-functionally to develop and execute the audio roadmap, spanning low-level embedded implementations, HLOS audio system architecture, and audio signal processing and machine learning ...
Collaborate cross-functionally to develop and execute the audio roadmap, spanning low-level embedded implementations, HLOS audio system architecture, and audio signal processing and machine learning ...
Collaborate cross-functionally to develop and execute the audio roadmap, spanning low-level embedded implementations, HLOS audio system architecture, and audio signal processing and machine learning ...
Collaborate cross-functionally to develop and execute the audio roadmap, spanning low-level embedded implementations, HLOS audio system architecture, and audio signal processing and machine learning ...
2026 Fall Applied Science Internship - Information & Knowledge Management (Machine Learning) - Unite
Seattle, WA · On-site
Amazon has positions available for Machine Learning Applied Science Internships in, but not limited to Arlington, VA; Bellevue, WA; Boston, MA; New York, NY; Palo Alto, CA; San Diego, CA; Santa Clara ...
2026 Fall Applied Science Internship - Information & Knowledge Management (Machine Learning) - Unite
Seattle, WA · On-site
Amazon has positions available for Machine Learning Applied Science Internships in, but not limited to Arlington, VA; Bellevue, WA; Boston, MA; New York, NY; Palo Alto, CA; San Diego, CA; Santa Clara ...
Collaborate cross-functionally to develop and execute the audio roadmap, spanning low-level embedded implementations, HLOS audio system architecture, and audio signal processing and machine learning ...
Collaborate cross-functionally to develop and execute the audio roadmap, spanning low-level embedded implementations, HLOS audio system architecture, and audio signal processing and machine learning ...
Machine Learning Engineer
Austin, TX · On-site
$199K - $331K/yr
About the Role: Engineers on the BCI team utilize signal processing and machine learning to ... Temporary Employees & Interns excluded
Machine Learning Engineer
Austin, TX · On-site
$199K - $331K/yr
About the Role: Engineers on the BCI team utilize signal processing and machine learning to ... Temporary Employees & Interns excluded
Responsibilities : • Build reliable machine learning systems and optimize audio inference serving efficiency using innovative techniques. • Advance core audio model serving metrics, including ...
Responsibilities : • Build reliable machine learning systems and optimize audio inference serving efficiency using innovative techniques. • Advance core audio model serving metrics, including ...
This includes but is not limited to crafting creative techniques to analyze audio & video datasets, designing metrics to understand user behavior & evaluate performance of machine learning models.
This includes but is not limited to crafting creative techniques to analyze audio & video datasets, designing metrics to understand user behavior & evaluate performance of machine learning models.
Senior Software Engineer, Machine Learning (Audio)
Redwood City, CA · On-site
$150K - $197K/yr
Responsibilities : • Own and train audio models to run on edge devices. • Ensure the models run to spec and meet the customer needs. • Apply machine learning in real world applications.
Senior Software Engineer, Machine Learning (Audio)
Redwood City, CA · On-site
$150K - $197K/yr
Responsibilities : • Own and train audio models to run on edge devices. • Ensure the models run to spec and meet the customer needs. • Apply machine learning in real world applications.
Innovate in audio machine learning through fundamental and applied research, advancing the state-of-the-art in audio playback, capture, generation, and editing. * Research and develop novel ML models ...
Innovate in audio machine learning through fundamental and applied research, advancing the state-of-the-art in audio playback, capture, generation, and editing. * Research and develop novel ML models ...
As a Machine Learning Engineer I, you will lead the deployment of machine learning models ... or audio/video processing • Proficient developing and debugging code in an embedded environment ...
As a Machine Learning Engineer I, you will lead the deployment of machine learning models ... or audio/video processing • Proficient developing and debugging code in an embedded environment ...
As a Machine Learning Engineer, you will deploy machine learning models and collaborate with cross ... or audio/video processing • Proficient developing and debugging code in an embedded environment ...
As a Machine Learning Engineer, you will deploy machine learning models and collaborate with cross ... or audio/video processing • Proficient developing and debugging code in an embedded environment ...
Machine Learning Engineer
Washington, DC · On-site +1
$130K - $200K/yr
Design, train, evaluate, and deploy machine learning models across text, image, audio, and multimodal domains. * Develop and improve classification systems for safety, security, abuse detection, and ...
Machine Learning Engineer
Washington, DC · On-site +1
$130K - $200K/yr
Design, train, evaluate, and deploy machine learning models across text, image, audio, and multimodal domains. * Develop and improve classification systems for safety, security, abuse detection, and ...
Our Machine Learning Internship is designed for curious, ambitious researchers who want to apply machine learning to complex, real-world problems. Over 10-12 weeks, you'll work alongside experienced ...
Our Machine Learning Internship is designed for curious, ambitious researchers who want to apply machine learning to complex, real-world problems. Over 10-12 weeks, you'll work alongside experienced ...
Machine Learning Manager
$180K - $250K/yr
Knowledgeable in at least one focus area of machine learning, such as computer vision, audio, or NLP * 2+ years experience managing machine learning teams * You have an ability to understand and make ...
Machine Learning Manager
$180K - $250K/yr
Knowledgeable in at least one focus area of machine learning, such as computer vision, audio, or NLP * 2+ years experience managing machine learning teams * You have an ability to understand and make ...
Machine Learning Engineer
Cheyenne, WY · On-site
$90K - $110K/yr
... audio data. We focus on delivering practical, deployable solutions that support real-world ... Machine Learning Systems & Engineering * Design and implement end-to-end ML pipelines, including ...
New
Machine Learning Engineer
Cheyenne, WY · On-site
$90K - $110K/yr
... audio data. We focus on delivering practical, deployable solutions that support real-world ... Machine Learning Systems & Engineering * Design and implement end-to-end ML pipelines, including ...
New
Machine Learning Manager
Seattle, WA · On-site
$180K - $250K/yr
Knowledgeable in at least one focus area of machine learning, such as computer vision, audio, or NLP * 2+ years experience managing machine learning teams * You have an ability to understand and make ...
Machine Learning Manager
Seattle, WA · On-site
$180K - $250K/yr
Knowledgeable in at least one focus area of machine learning, such as computer vision, audio, or NLP * 2+ years experience managing machine learning teams * You have an ability to understand and make ...
Machine Learning Manager
$180K - $250K/yr
Knowledgeable in at least one focus area of machine learning, such as computer vision, audio, or NLP * 2+ years experience managing machine learning teams * You have an ability to understand and make ...
Machine Learning Manager
$180K - $250K/yr
Knowledgeable in at least one focus area of machine learning, such as computer vision, audio, or NLP * 2+ years experience managing machine learning teams * You have an ability to understand and make ...
Internship Audio Machine Learning information
See salary details
$25.5K - $31.2K
5% of jobs
$33.1K is the 25th percentile. Wages below this are outliers.
$31.2K - $36.9K
59% of jobs
$36.9K - $42.5K
9% of jobs
$43K is the 75th percentile. Wages above this are outliers.
$42.5K - $48.2K
17% of jobs
$48.2K - $53.9K
4% of jobs
$53.9K - $59.6K
2% of jobs
$59.6K - $65.3K
3% of jobs
$65.3K - $71K
0% of jobs
$71K - $76.6K
0% of jobs
$76.6K - $82.3K
0% of jobs
$82.3K - $88K
0% of jobs
$25.5K
$42.6K
$88K
How much do internship audio machine learning jobs pay per year?
As of Jul 10, 2026, the average yearly pay for internship audio machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.
What is the difference between Internship Audio Machine Learning vs Audio Signal Processing Intern?
| Aspect | Internship Audio Machine Learning | Audio Signal Processing Intern |
|---|---|---|
| Required Skills | Machine learning, programming, audio analysis | Signal processing, audio engineering, MATLAB |
| Work Environment | Research labs, tech companies, AI-focused teams | Audio labs, audio equipment companies, research institutions |
| Industry Usage | AI, speech recognition, music analysis | Audio engineering, sound design, acoustics |
Internship Audio Machine Learning focuses on developing algorithms for audio data, emphasizing machine learning techniques. In contrast, Audio Signal Processing Interns work on analyzing and manipulating audio signals using signal processing methods. Both roles often require programming skills and are found in tech and audio industries, but their core focus differs: AI-driven analysis versus traditional signal processing.
What cities are hiring for Internship Audio Machine Learning jobs? Cities with the most Internship Audio Machine Learning job openings:
What are the most commonly searched types of Audio Machine Learning jobs? The most popular types of Audio Machine Learning jobs are:
What states have the most Internship Audio Machine Learning jobs? States with the most job openings for Internship Audio Machine Learning jobs include:
Full-time
Re-posted 28 days ago
Job description
Job Summary:
Abaka AI is built on the mission to be the world’s most trusted data partner for AI companies, supporting global partners with reliable and scalable data solutions. The Machine Learning Engineer will design and develop scalable training pipelines for multimodal AI systems, collaborate with data engineering and research teams, and influence core decisions around model training strategy and infrastructure.
Responsibilities:
• Design, build, and optimize scalable machine learning pipelines for multimodal model training, fine-tuning, and evaluation across text, image, audio, video, and 3D data.
• Work closely with data engineering and research teams to develop efficient data workflows, including collection, preprocessing, annotation, versioning, and model integration.
• Implement and refine training strategies for large-scale AI systems, including vision, video, and diffusion models, ensuring reproducibility, efficiency, and strong model performance.
• Develop tools and automation frameworks that accelerate model experimentation, hyperparameter tuning, and deployment.
• Identify and address performance bottlenecks in data or training pipelines to improve throughput, stability, and resource utilization.
• Collaborate with product and infrastructure teams to ensure smooth integration of model outputs into both internal and client-facing applications.
• Support internal best practices for model governance, experiment tracking, and documentation to maintain high engineering standards and reproducibility.
Qualifications:
Required:
• Strong academic background in computer science, artificial intelligence, machine learning, or related fields.
• 3+ years of experience in applied machine learning or ML engineering, with a demonstrated ability to deliver production-ready models or pipelines.
• Proficient in Python and ML frameworks such as PyTorch, TensorFlow, or JAX, with hands-on experience in large-scale distributed training and inference systems.
• Familiarity with multimodal data processing (e.g., text-image pairing, video understanding, speech-audio modeling) and dataset optimization for model training.
• Solid understanding of ML system design, including feature pipelines, data loaders, model serving, and evaluation frameworks.
• Experience with modern infrastructure tools such as Kubernetes, Ray, Airflow, or MLflow, along with cloud-based training environments (AWS, GCP, Azure).
• Excellent communication and collaboration skills, capable of working effectively across engineering, research, and product teams to accomplish shared goals.
• Self-driven and adaptable, comfortable operating in a fast-paced startup environment, and able to demonstrate strong ownership and urgency in execution.
Preferred:
• Master’s degree or Ph.D. is preferred.
Company:
Abaka AI provides accurate and efficient AI data services, including data collection, data cleaning, data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA, with a team of 51-200 employees. The company is currently Growth Stage.
Abaka AI is built on the mission to be the world’s most trusted data partner for AI companies, supporting global partners with reliable and scalable data solutions. The Machine Learning Engineer will design and develop scalable training pipelines for multimodal AI systems, collaborate with data engineering and research teams, and influence core decisions around model training strategy and infrastructure.
Responsibilities:
• Design, build, and optimize scalable machine learning pipelines for multimodal model training, fine-tuning, and evaluation across text, image, audio, video, and 3D data.
• Work closely with data engineering and research teams to develop efficient data workflows, including collection, preprocessing, annotation, versioning, and model integration.
• Implement and refine training strategies for large-scale AI systems, including vision, video, and diffusion models, ensuring reproducibility, efficiency, and strong model performance.
• Develop tools and automation frameworks that accelerate model experimentation, hyperparameter tuning, and deployment.
• Identify and address performance bottlenecks in data or training pipelines to improve throughput, stability, and resource utilization.
• Collaborate with product and infrastructure teams to ensure smooth integration of model outputs into both internal and client-facing applications.
• Support internal best practices for model governance, experiment tracking, and documentation to maintain high engineering standards and reproducibility.
Qualifications:
Required:
• Strong academic background in computer science, artificial intelligence, machine learning, or related fields.
• 3+ years of experience in applied machine learning or ML engineering, with a demonstrated ability to deliver production-ready models or pipelines.
• Proficient in Python and ML frameworks such as PyTorch, TensorFlow, or JAX, with hands-on experience in large-scale distributed training and inference systems.
• Familiarity with multimodal data processing (e.g., text-image pairing, video understanding, speech-audio modeling) and dataset optimization for model training.
• Solid understanding of ML system design, including feature pipelines, data loaders, model serving, and evaluation frameworks.
• Experience with modern infrastructure tools such as Kubernetes, Ray, Airflow, or MLflow, along with cloud-based training environments (AWS, GCP, Azure).
• Excellent communication and collaboration skills, capable of working effectively across engineering, research, and product teams to accomplish shared goals.
• Self-driven and adaptable, comfortable operating in a fast-paced startup environment, and able to demonstrate strong ownership and urgency in execution.
Preferred:
• Master’s degree or Ph.D. is preferred.
Company:
Abaka AI provides accurate and efficient AI data services, including data collection, data cleaning, data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA, with a team of 51-200 employees. The company is currently Growth Stage.