Senior ML Engineer

At Momenta, we're committed to creating a safer digital world by protecting individuals and businesses from voice-based fraud and scams. Through innovative AI technology and community collaboration, we're building a future where communication is secure and trustworthy.

The Role

Key Responsibilities

Design and Deliver Core Detection Pipeline

Lead the development of a robust, modular deepfake detection pipeline capable of ingesting, processing, and classifying real-time audio streams with high accuracy and low latency. Architect the system to operate under telecom-grade conditions with configurable interfaces and scalable deployment strategies.

Model Strategy, Development, and Optimization

Own the experimentation and refinement of state-of-the-art deep learning models for voice fraud detection. Evaluate multiple model families, benchmark performance across datasets, and strategically select or ensemble models that balance precision, robustness, and compute efficiency for real-world deployment.

Latency-Conscious Production Readiness

Ensure the entire detection stack meets strict performance targets, including sub-20ms inference latency. Apply industry best practices in model compression, preprocessing optimization, and system-level integration to support high-throughput inference on both CPU and GPU environments.

Evaluation Framework and Continuous Testing

Design and implement a comprehensive evaluation suite to validate model accuracy, false positive rates, and environmental robustness. Conduct rigorous testing across domains, including cross-corpus validation, telephony channel effects, adversarial scenarios, and environmental noise conditions.

Deployment Engineering and API Integration

Deliver a fully containerized, production-ready inference service with REST / gRPC endpoints. Build CI / CD pipelines, integration tests, and monitoring hooks to ensure system integrity, traceability, and ease of deployment across environments.

Ideal Profile

Technical Skills :

• ML Frameworks : PyTorch, TensorFlow, ONNX, OpenVINO, TorchScript
• Audio Libraries : Librosa, Torchaudio, FFmpeg
• Model Development : CNNs, Transformers, Wav2Vec / WavLM, AASIST, RawNet
• Signal Processing : VAD, noise reduction, band-pass filtering, codec simulation
• Optimization : Quantization, pruning, GPU acceleration
• DevOps : Git, Docker, CI / CD, FastAPI or Flask, REST / gRPC

Preferred Experience :

What's on Offer?