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Machine Learning Engineer, Audio
Not specifiedFull-timeAI & Machine LearningTelecommunicationsPublic Safety Technology
Join a small team developing acoustic detection and classification models, from data collection and training through optimized inference on embedded hardware.
Responsibilities
- Design, train, and iterate on deep learning models for acoustic classification in noisy real-world conditions.
- Improve and optimize audio systems for sound classification.
- Build and maintain data pipelines for field recordings, augmentation, labeling, and dataset versioning.
- Optimize models for edge deployment within latency and power constraints.
- Maintain training infrastructure, experiment tracking, reproducible pipelines, and deployed-model monitoring.
Requirements
- At least 2 years of machine learning engineering experience.
- Bachelor’s degree in electrical engineering or computer science.
- Experience with audio processing and microphone arrays.
- Hands-on experience deploying models to constrained hardware, including quantization, conversion, and runtime tuning.
- Experience with MLOps practices such as dataset versioning, experiment tracking, and reproducible training.
Nice to have
- Experience with beamforming, acoustic event detection, sound classification, or related DSP-heavy machine learning.
- Comfort working in the field and iterating on hardware.