Machine Learning Engineer / Research Engineer (Speech & Audio)

Job Title: Software Engineer - Machine Learning Optimization (Speech & Audio AI)

Location: Remote (Preferred)

Duration: 12-Month Contract (Potential Extension)

We are seeking a Software Engineer with strong machine learning systems and optimization experience to help improve the performance, efficiency, and scalability of speech and audio AI models. This role will partner closely with research scientists to refactor, optimize, and streamline machine learning training and evaluation pipelines.

The ideal candidate has hands-on experience with GPU optimization, PyTorch, Python, and large-scale machine learning workloads. You will work on cutting-edge speech-to-text (ASR) and text-to-speech (TTS) initiatives, helping accelerate model training, improve infrastructure efficiency, and support novel research efforts.

Required Qualifications

4+ years of software engineering experience.

Strong Python programming skills.

Strong PyTorch experience.

Experience optimizing machine learning models and training pipelines.

Experience with GPU performance optimization and acceleration.

Experience building, maintaining, or improving machine learning infrastructure.

Understanding of model training, evaluation, and experimentation workflows.

Preferred Qualifications

Experience working alongside AI/ML research scientists.

Experience with speech technologies such as ASR (Speech-to-Text) or TTS (Text-to-Speech).

Experience refactoring research code into efficient, maintainable systems.

Experience improving machine learning code quality, structure, and maintainability.

Familiarity with distributed training and high-performance computing environments.

Responsibilities

Optimize machine learning training and inference workflows for performance and scalability.

Refactor and improve existing ML models and training pipelines.

Collaborate closely with research scientists to translate research into efficient production-quality code.

Improve GPU utilization and reduce model training time.

Enhance evaluation frameworks and model benchmarking processes.

Organize, maintain, and improve machine learning codebases and infrastructure.

Analyze system bottlenecks and implement performance improvements.

Support speech, audio, and generative AI research initiatives.