Machine Learning Engineer (Sound Detection)

Job description

Company Description We are looking for an Audio Machine Learning / Edge AI Engineer to design and deploy real-time acoustic detection systems operating in complex and noisy environments. This role focuses on sound-based situational awareness and requires end-to-end ownership — from audio signal processing and ML model development to optimization and deployment on edge devices operating under constrained conditions. You will work at the intersection of audio signal processing, machine learning, and embedded systems, building robust solutions capable of reliable performance in real-world field environments. Job Description Design and develop audio-based detection and classification systems for challenging real-world environments Implement robust signal processing pipelines tailored for noisy outdoor conditions Build and optimize machine learning models for sound event detection Develop low-latency, high-reliability streaming pipelines Handle imbalanced and imperfect datasets using augmentation and synthetic data techniques Deploy and optimize models on edge hardware platforms (Jetson, Raspberry Pi, etc.) Optimize inference performance using ONNX, TensorRT, and OpenVINO Develop production-grade Python systems with modular architecture and multiprocessing capabilities Ensure system robustness under variable acoustic conditions and hardware constraints Collaborate with ML, hardware, and systems engineering teams to deliver integrated solutions Qualifications 4+ years in ML / Audio / DSP / Edge AI Strong knowledge of audio signal processing (spectrograms, noise reduction, feature extraction) Experience working with noisy environments (wind, city, nature) Hands-on experience with ML for audio (CNNs, YAMnet, ONNX) Proficiency in training on imbalanced datasets and applying augmentation techniques Ability to build low-latency streaming pipelines for real-time audio processing Experience deploying models on edge devices (Raspberry Pi, Jetson Nano) Optimization skills using ONNX, TensorRT, OpenVINO Production-level Python engineering experience (clean architecture, multiprocessing, modular pipelines) Proven track record of production deployment in real-world scenarios Professional proficiency in English WILL BE A PLUS Acoustic domain knowledge (drone frequency ranges, Doppler effect, microphone arrays) Sensor fusion experience (audio + video, audio + RF detection) Hardware integration skills (GPIO, signal triggering) Additional Information PERSONAL PROFILE High level of ownership and problem-solving mindset Ability to work across R&D, engineering, and hardware constraints Comfortable with real-world, non-ideal data Strong collaboration skills in cross-functional teams