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Remote Audio Machine Learning Jobs (NOW HIRING)

Apex Informatics

Machine Learning Engineer

Remote We are seeking an Applied Machine Learning Engineer with a strong focus on practical ... audio and other unstructured data. • Collaborate with Product and Engineering teams to ensure ...

ManTech International

Machine Learning Engineer

$128K - $214K/yr

General information Requisition # R67616 Locations USA-Remote Work Posting Date 05/19/2026 Security ... The Machine Learning Engineer will leverage their strong technical background and knowledge to ...

Shure

Audio Applied Research Science Intern

Niles, IL · On-site +1

This Internship will be Remote, Onsite or Hybrid in Niles, IL. Responsibilities * Conducting research and developing machine learning solutions for challenging audio problems. * Collecting, creating ...

Shure

Audio Applied Research Science Intern

Niles, IL · On-site +1

This Internship will be Remote, Onsite or Hybrid in Niles, IL. Responsibilities * Conducting research and developing machine learning solutions for challenging audio problems. * Collecting, creating ...

Shure

Audio Applied Research Science Intern

Niles, IL · On-site +1

This Internship will be Remote, Onsite or Hybrid in Niles, IL. * Conducting research and developing machine learning solutions for challenging audio problems. * Collecting, creating, and curating ...

FloVision Solutions

Machine Learning Engineer

OR · On-site +1

ABOUT FLOVISION FloVision is a remote-first startup focused on improving the food supply chain, starting with protein processing. We design computer vision and machine learning-assisted production ...

FloVision Solutions

Machine Learning Engineer

ABOUT FLOVISION FloVision is a remote-first startup focused on improving the food supply chain, starting with protein processing. We design computer vision and machine learning-assisted production ...

1 point system

Machine Learning Engineer

Fort Mill, SC · Remote

$48/hr

Job Title Machine Learning Engineer Location Remote Rate $48/hr on W2 Must Haves: Neaural networks NLP Python AZURE Pytorch or tensorflow Machine Learning Engineer / AI Engineer Role Role Overview ...

MatrixSpace

Machine Learning Engineer

Burlington, MA · Remote

$165K - $200K/yr

S. government security clearance in the future.' This is NOT a fully remote position! Required * BS, MS, or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Machine Learning, AI ...

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site +1

Vienna, VA / Chantilly, VA (Hybrid / Flexible Remote options available) Responsibilities * Prototype to Production: Support the full machine learning lifecycle, taking computer vision models from ...

PTP

Machine Learning Engineer - REMOTE

Austin, TX · On-site +1

We are looking for a Machine Learning Engineer to help us design and deliver CX solutions that provide our clients with a beautiful customer journey that achieves results. At PTP we value aptitude ...

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Remote Audio Machine Learning information

See salary details

$29.5K

$84.5K

$171.5K

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

As of Sep 15, 2026, the average yearly pay for remote audio machine learning in the United States is $84,456.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,000.00 and $113,000.00 per year, depending on experience, location, and employer.

What is a remote audio machine learning?

A Remote Audio Machine Learning job involves using machine learning techniques to analyze, process, or generate audio data while working from a remote location. Professionals in this field develop algorithms for tasks such as speech recognition, music classification, noise reduction, or audio synthesis. They often work with large datasets, build and train models, and collaborate with teams online. These roles typically require skills in programming, signal processing, and experience with machine learning frameworks.

What are the key skills and qualifications needed to thrive as a remote audio machine learning engineer?

To thrive as a Remote Audio Machine Learning Engineer, you need strong foundations in digital signal processing, machine learning algorithms, and programming (often Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with tools such as TensorFlow, PyTorch, and audio processing libraries (e.g., LibROSA), as well as experience with cloud platforms, is highly valuable. Excellent problem-solving skills, self-motivation, and clear remote communication are essential soft skills for collaborating across distributed teams. These competencies enable the development of robust, innovative audio ML solutions while ensuring effective teamwork and project delivery in a remote setting.

How does a remote audio machine learning role typically collaborate with cross-functional teams, and what communication tools are commonly used?

In a Remote Audio Machine Learning position, collaboration with cross-functional teams such as software engineers, data scientists, and product managers is essential. Regular communication is maintained through tools like Slack, Zoom, and project management platforms such as Jira or Trello. Team members often participate in virtual stand-ups, sprint planning sessions, and code reviews to ensure alignment on project goals and timelines. Effective asynchronous communication and clear documentation are especially important in remote settings to keep everyone informed and foster a productive workflow.

What is the difference between Remote Audio Machine Learning vs Remote Audio Engineer?

AspectRemote Audio Machine LearningRemote Audio Engineer
Required CredentialsBackground in machine learning, data science, or AI; often a degree in computer science or related fieldsAudio engineering, sound design, or music production degree or certification
Work EnvironmentPrimarily focused on developing algorithms, data analysis, and model training, often in a tech or research settingRecording, mixing, editing audio, often in studios or remote production setups
Employer & Industry UsageTech companies, research labs, AI startups working on audio recognition or enhancementMusic, film, broadcasting, and media production companies

Remote Audio Machine Learning specialists focus on developing algorithms to process and analyze audio data, while Remote Audio Engineers handle the practical aspects of recording and editing sound. Both roles may collaborate but serve different functions within the audio industry.

More about Remote Audio Machine Learning jobs

What cities are hiring for Remote Audio Machine Learning jobs?

Cities with the most Remote 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 Remote Audio Machine Learning jobs?

States with the most job openings for Remote Audio Machine Learning jobs include:

What job categories do people searching Remote Audio Machine Learning jobs look for?

The top searched job categories for Remote Audio Machine Learning jobs are:

What other helpful pages are available for Remote Audio Machine Learning?

Other pages related to Remote Audio Machine Learning:

Remote Audio Machine Learning jobs near you
Infographic showing various Remote Audio Machine Learning job openings in the United States as of September 2026, with employment types broken down into 100% Contract. Highlights an 100% Remote job distribution, with an average salary of $84,456 per year, or $40.6 per hour.

Machine Learning Engineer

Remote

Apex Informatics
IT Services • 1 - 10 employees
Learn more about Apex Informaticsexternal

Other

This job post has expired 2 days ago. Applications are no longer accepted.

Job description

Applied Machine Learning Engineer | Music Software (Multiple Roles open)
Role: Applied Machine Learning Engineer (Mid - Senior Opportunity) Company: Splash
Employment Type: Contract (3 months +, potential for extension) Location: Remote
We are seeking an Applied Machine Learning Engineer with a strong focus on practical solutions and software development (ability to work on both open-ended research problems and production-ready API code). In this role, you'll leverage off-the-shelf tools and custom-built ML models to solve challenges in music product development and improve manual music processes. This position is ideal for engineers with demonstrable experience building functional, production-ready models and who are passionate about user experience and Product.
Key Responsibilities:
• Design and implement ML algorithms to enhance music creation tools and solve various user problems in line with product goals.
• Identify and implement off-the-shelf ML and AI tools to solve practical problems efficiently.
• Understand the requirements of running models in production, including domain shift testing, QA, A/B testing and so on.
• Maintain production-ready code with considerations for how solutions fit the product and enhance the user experience.
• Build scalable, maintainable data pipelines to handle audio and other unstructured data.
• Collaborate with Product and Engineering teams to ensure seamless integration of ML solutions into production systems.
• Evaluate, deploy, and fine-tune pre-trained models for tasks like audio analysis, melody generation, and process automation.
• Uphold ethical AI practices, ensuring fairness and responsible AI use in music-related applications.
What You Bring
• Proven software development experience, ideally in Python (other languages a plus).
• Experience implementing and deploying ML models, using PyTorch framework.
• Familiarity with AWS cloud environment for deploying and scaling ML solutions.
• Ability to preprocess and model unstructured data, especially audio.
• A strong focus on applied problem-solving, with a practical approach to integrating existing tools and systems.
• A good understanding of music, production, or audio technology processes (or a strong interest in music)
• Familiarity with GenAI architectures like transformers, LLMs, or diffusion models.
• Proactive nature, ability to creatively solve problems you face and bring new ideas to the team.
• Clear and effective communication with technical and non-technical stakeholders.
• Ability to work independently and remotely while collaborating closely with cross-functional teams.

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