Machine Learning Engineer (Sound Detection)

Requirements

• 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)

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

Responsibilities

• 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