Machine Learning Engineer for Audio Processing

Project Overview:
We are looking for an experienced Machine Learning Engineer specialized in audio processing and deep learning. The goal is to design, train, and deploy a high-performance AMD (Answering Machine Detection) model for telephony, using an existing dataset of approximately 67,000 labeled audio samples. The model must operate in real-time with low latency, and integrate into our existing calling infrastructure (Drachtio / Asterisk / FreeSWITCH / Vicidial).

Mission Responsibilities:

Analyze and preprocess the existing dataset (cleaning, balancing, train/val/test split)

Extract audio features such as Mel-spectrograms, MFCC, STFT, normalization

Design and train a CNN/CRNN model for AMD classification (Human / Voicemail / Silence / Fax / Other if needed)

Optimize the model for real-time inference (target <200 ms decision, ideally <100 ms)

Build a reproducible training pipeline (PyTorch or TensorFlow)

Evaluate the model with solid metrics (per-class F1 score, confusion matrix, recall/precision)

Test and validate performance using real call samples and streaming input

Develop an inference microservice (REST/gRPC/WebSocket) for integration with backend systems

Integrate the solution into our current telephony infrastructure (Drachtio / Asterisk / FreeSWITCH / Vicidial)

Provide technical documentation and handover

Optional: Implement a continuous training pipeline for incremental model improvement

Required Skills:

Strong experience in deep learning applied to audio

Knowledge of CNN/CRNN architectures for classification

Solid background in speech/audio signal processing

Expertise in PyTorch or TensorFlow

Experience generating audio features (Mel Spectrogram, MFCC, STFT)

Ability to optimize models for real-time inference on GPU/CPU

Experience deploying ML models in production (Docker, APIs, inference servers)

Experience with streaming audio or RTP real-time processing

English or French communication skills

Preferred (not mandatory but strong advantage):

Previous AMD or call-center related project experience

Knowledge of Asterisk, FreeSWITCH, Drachtio, WebRTC

Experience with Whisper/Vosk/Wav2Vec or speech models

Experience with large-scale deployment and optimization

Deliverables:

Fully documented and clean source code

Trained AMD model with evaluation report

Production-ready inference service (API)

Performance metrics and confusion matrix

Documentation for deployment and usage