Machine Learning Engineer (Audio)

Machine Learning Engineer (Audio)
Location: Remote (US Only)
Duration: 12 months with potential for extension
Pay rate: $75 - $80/hr on W2
Benefits: Health, Dental, Vision, 401K, PTO
Experience: 4–5+ years
Note: This is a W-2-only position. We do not offer visa sponsorship and cannot accept H-1B, F-1/STEM OPT, 1099, C2C, or C2H arrangements.

About the Role

We are seeking a Software Engineer to support high-priority projects within an advanced Research Audio organization. This role will focus on machine learning model optimization, refactoring, and high-performance computing, with an emphasis on improving the efficiency and scalability of machine learning workflows.

You will work closely with research scientists and software engineers to streamline machine learning training and evaluation, reduce training time, improve model efficiency, and maintain well-organized, high-quality ML code.

This is an opportunity to work on technically challenging and research-oriented projects involving audio, speech-to-text, text-to-speech, and machine learning technologies.

Key Responsibilities

• Refactor and optimize machine learning models and training pipelines to improve performance, efficiency, and maintainability.
• Collaborate closely with research scientists and engineers to translate research requirements into effective software and ML solutions.
• Optimize GPU-based workloads and machine learning training processes to improve computational efficiency and reduce training time.
• Develop and maintain high-quality Python and PyTorch code for machine learning workflows.
• Support speech-to-text, text-to-speech, and audio-related machine learning projects.
• Evaluate models and training workflows based on performance, efficiency, and other research criteria.
• Identify bottlenecks within machine learning pipelines and implement solutions to improve performance and resource utilization.
• Clean up, restructure, and organize existing machine learning codebases.
• Contribute to research-focused projects involving experimentation, iteration, and novel machine learning approaches.
• Collaborate with a small group of engineers and researchers on a daily basis while contributing to a broader research team.

Required Qualifications

• 4–5+ years of experience in software engineering, machine learning engineering, or a related technical field.
• Strong programming experience with Python.
• Hands-on experience with PyTorch.
• Professional experience working with machine learning models, training pipelines, or ML infrastructure.
• Demonstrated experience with GPU optimization or high-performance computing for machine learning workloads.
• Experience improving model performance, training efficiency, computational efficiency, or resource utilization.
• Strong software engineering fundamentals, including writing organized, maintainable, and reusable code.
• Demonstrated ability to collaborate effectively with engineers, researchers, or other technical stakeholders.

Preferred Qualifications

• Experience working directly with research scientists on machine learning or software engineering projects.
• Experience refactoring, cleaning up, or restructuring machine learning models and codebases.
• Experience organizing and improving complex machine learning code and infrastructure.
• Experience with speech, audio, speech-to-text (STT), text-to-speech (TTS), or related machine learning applications.
• Experience working with large-scale or high-performance ML infrastructure.
• Experience optimizing model training, evaluation, inference, or GPU utilization.
• Experience working in a research-oriented or highly experimental engineering environment.

The Team & Project

The team is focused on advancing audio and machine learning technologies through research and software engineering. Current projects involve speech-to-text, text-to-speech, machine learning models, and research infrastructure.
You will work closely with approximately 5–6 team members on a daily basis as part of a larger team of approximately 20 professionals.