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Remote Audio Signal Processing Machine Learning Jobs in New York

Shaped

Senior Machine Learning Engineer

Brooklyn, NY · On-site +1

$130K - $200K/yr

We're remote but have an office in Brooklyn, New York. We are looking for a machine learning engineer to design, build, experiment and optimize Shaped's AI discovery engine. You will be a founding ...

Mercor

Machine Learning Engineer

New York, NY · Remote

$70 - $100/hr

Remote Commitment: 40 hours/week Role Responsibilities * Guide research and engineering teams to ... Application Process (Takes 20-30 mins to complete) * Upload resume * AI interview based on your ...

Career Renew

Senior Machine Learning Engineer

New York, NY · Remote

$165K - $225K/yr

... remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ... chemical staining processes. This innovation supports the critical evolution from research ...

Mercor

Senior Machine Learning Engineer

New York, NY · Remote

$70 - $100/hr

Remote Commitment: 40 hours/week Role Responsibilities * Guide research and engineering teams to ... Application Process (Takes 20-30 mins to complete) * Upload resume * AI interview based on your ...

Orita

Senior Machine Learning Engineer

New York, NY · On-site +1

$180K - $250K/yr

The Role As a Senior Machine Learning Engineer at Orita, you will: * Build and Productionize Models ... Experience with scalable data processing (Spark, Ray, BigQuery). * Job orchestration (Airflow)

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Remote Audio Signal Processing Machine Learning information

What is the difference between Remote Audio Signal Processing Machine Learning vs Remote Audio Engineering?

AspectRemote Audio Signal Processing Machine LearningRemote Audio Engineering
Required CredentialsKnowledge of machine learning, signal processing, programming (Python, MATLAB)Audio engineering certifications, audio production experience
Work EnvironmentResearch labs, tech companies, remote collaborationRecording studios, broadcast companies, remote or onsite
Industry UsageDeveloping algorithms for audio enhancement, noise reduction, speech recognitionMixing, mastering, live sound, audio content creation

Remote Audio Signal Processing Machine Learning focuses on developing algorithms using machine learning techniques to improve audio quality and analysis. In contrast, Remote Audio Engineering involves practical audio production, mixing, and recording tasks. Both roles require audio knowledge, but the former emphasizes programming and data science, while the latter centers on sound quality and production skills.

What are popular job titles related to Remote Audio Signal Processing Machine Learning jobs in New York? For Remote Audio Signal Processing Machine Learning jobs in New York, the most frequently searched job titles are:
What job categories do people searching Remote Audio Signal Processing Machine Learning jobs in New York look for? The top searched job categories for Remote Audio Signal Processing Machine Learning jobs in New York are:
What cities in New York are hiring for Remote Audio Signal Processing Machine Learning jobs? Cities in New York with the most Remote Audio Signal Processing Machine Learning job openings:
Remote Audio Signal Processing Machine Learning jobs near you
Infographic showing various Remote Audio Signal Processing Machine Learning job openings in New York as of July 2026, with employment types broken down into 77% Full Time, 20% Part Time, 1% Temporary, and 2% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution.
Sr. Lead Machine Learning Engineer/Remote

Sr. Lead Machine Learning Engineer/Remote

Apetan Consulting llc

Paterson, NJ • Remote

$80 - $150/hr

Contractor

Posted 27 days ago

Job description

Sr. Lead Machine Learning EngineerLocation-RemoteJob Summary

The Sr. Lead Machine Learning Engineer is responsible for leading the design, development, deployment, and optimization of machine learning solutions that drive business value. This role combines technical expertise, strategic leadership, and cross-functional collaboration to build scalable AI/ML systems, mentor engineering teams, and guide the organization's machine learning initiatives.

Key Responsibilities
  • Lead the development and deployment of machine learning models and AI-driven solutions.
  • Design scalable ML architectures, pipelines, and production-ready systems.
  • Collaborate with data scientists, software engineers, product managers, and business stakeholders to define and deliver ML solutions.
  • Oversee data preparation, feature engineering, model training, evaluation, and monitoring processes.
  • Optimize model performance, scalability, reliability, and operational efficiency.
  • Establish best practices for MLOps, model governance, testing, and deployment.
  • Conduct code reviews and provide technical leadership and mentorship to engineering teams.
  • Evaluate emerging AI/ML technologies and recommend innovative solutions.
  • Ensure compliance with security, privacy, and responsible AI standards.
  • Support production systems by troubleshooting and resolving complex ML-related issues.
  • Drive technical roadmaps and contribute to strategic AI initiatives.
Required Qualifications
  • Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.
  • 8+ years of software engineering experience, including 5+ years in machine learning engineering.
  • Strong proficiency in Python and machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Experience building and deploying machine learning models in production environments.
  • Strong knowledge of data structures, algorithms, statistics, and machine learning techniques.
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud.
  • Knowledge of MLOps tools, CI/CD pipelines, and model monitoring practices.
  • Excellent leadership, communication, and problem-solving skills.
Preferred Qualifications
  • Master’s degree or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
  • Experience with large-scale distributed systems and big data technologies.
  • Knowledge of Generative AI, Large Language Models (LLMs), NLP, computer vision, or recommendation systems.
  • Experience with Kubernetes, Docker, and cloud-native architectures.
  • Prior experience leading technical teams and enterprise AI initiatives.