Machine Learning Engineer (Audio)

Summary

Tailored Management is recruiting for a confidential client seeking a Machine Learning Engineer to support an advanced research audio organization. The role focuses on optimizing machine learning models, training pipelines, GPU workloads, and high-performance computing while supporting speech-to-text, text-to-speech, and audio-related projects.

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

Skills

• 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
• We do not offer visa sponsorship and cannot accept H-1B, F-1/STEM OPT, 1099, C2C, or C2H arrangements
• 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

Qualifications

Must Haves

• 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
• We do not offer visa sponsorship and cannot accept H-1B, F-1/STEM OPT, 1099, C2C, or C2H arrangements

Nice to Haves

• 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

Benefits

• Health
• Dental
• Vision
• 401K
• PTO