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Remote Audio Machine Learning Jobs in New York, NY

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Machine Learning Engineer

New York, NY · Remote

We're looking for a senior-level Machine Learning Engineer who can move quickly while maintaining ... We're remote-first, flexible, and distributed across North and South America, bringing together ...

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All JobsAudio Machine Learning JobsRemote Audio Machine Learning JobsRemote Audio Machine Learning Jobs in New York, NY

Remote Audio Machine Learning information

See New York, NY salary details

$32.3K

$92.4K

$187.6K

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

As of Sep 12, 2026, the average yearly pay for remote audio machine learning in New York, NY is $92,397.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,700.00 and $123,600.00 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.

What are the most commonly searched types of Audio Machine Learning jobs in New York, NY?

The most popular types of Audio Machine Learning jobs in New York, NY are:

What are popular job titles related to Remote Audio Machine Learning jobs in New York, NY?

For Remote Audio Machine Learning jobs in New York, NY, the most frequently searched job titles are:

What job categories do people searching Remote Audio Machine Learning jobs in New York, NY look for?

The top searched job categories for Remote Audio Machine Learning jobs in New York, NY are:

What cities near New York, NY are hiring for Remote Audio Machine Learning jobs?

Cities near New York, NY with the most Remote Audio Machine Learning job openings:

Remote Audio Machine Learning jobs near you
Infographic showing various Remote Audio Machine Learning job openings in New York, NY as of September 2026, with employment types broken down into 100% Contract. Highlights an 100% Remote job distribution, with an average salary of $92,397 per year, or $44.4 per hour.

Machine Learning Engineer - Remote

New York, NY • Remote

YO AI Labs
See more jobs at YO AI Labsexternal

Full-time

Posted 17 days ago

Job description

Machine Learning Engineer

Role Type: Contractor
Location: Remote

About the Role

We are looking for skilled Machine Learning Engineers to support AI training and evaluation projects. You'll use Python, machine learning, and data expertise to develop, evaluate, and improve ML solutions.

Key Responsibilities
  • Develop and refine machine learning models using Python.
  • Analyze and manage datasets using MongoDB.
  • Evaluate models, tune performance, and benchmark results.
  • Build data preprocessing and ML workflows.
  • Identify opportunities to improve model performance.
  • Document experiments, methodologies, and results.
Required Skills
  • Python
  • Machine Learning
  • MongoDB
  • Data Analysis & Preprocessing
  • Model Evaluation
  • Feature Engineering
Preferred Qualifications
  • Strong experience with Python and ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
  • Hands-on MongoDB experience in ML or data projects.
  • Strong problem-solving and analytical skills.
  • Understanding of ML evaluation metrics and data modeling.
  • Experience deploying ML models in cloud or enterprise environments.
  • Strong technical documentation and communication skills.