Digital Signal Processing Engineer

Summary Ribbon is looking for a Digital Signal Processing Engineer with rich hands-on experience and strong technical expertise to design, develop, and implement advanced Digital Signal Processing (DSP), Machine Learning (ML), and Deep Learning solutions for voice security, cybersecurity, and voice fraud  this role, you will research, develop, and deploy solutions that leverage speech processing, signal analysis, machine learning, and deep learning to address challenges such as synthetic voice detection, audio deepfake detection, speaker verification, Automatic Speaker Verification (ASV) spoofing detection, voice authentication security, and fraud detection.

You will be responsible for end-to-end software development, including requirements discussions, design, implementation, model development, testing, deployment, and collaboration with architecture and test teams across multiple geographies.

Responsibilities
· Conduct innovative research in Digital Signal Processing (DSP), Machine Learning, Deep Learning, and Speech Processing technologies to develop solutions for voice security, fraud prevention, and cybersecurity applications.
· Design, develop, train, and optimize machine learning models for:o Synthetic voice and audio deepfake detectiono Text-to-Speech (TTS) generated speech identificationo Automatic Speaker Verification (ASV) spoofing detectiono Speaker authentication and voice biometric securityo Fraud detection and anomaly detection
· Apply Digital Signal Processing (DSP) techniques to analyze, preprocess, enhance, and extract meaningful information from speech and audio signals.
· Design and develop feature extraction pipelines leveraging techniques such as FFT, spectrograms, Mel-spectrograms, MFCCs, LFCCs, cepstral analysis, and speaker embeddings.
· Research, develop, and evaluate advanced countermeasures against synthetic speech, voice deepfakes, and ASV spoofing attacks using state-of-the-art machine learning and deep learning techniques.
· Analyze large-scale speech, audio, and security datasets to identify meaningful patterns, emerging threats, and actionable insights.
· Develop algorithms that combine signal processing and machine learning techniques to improve detection accuracy and robustness against evolving voice-based attacks.
· Write clean, maintainable, and high-performance code using modern programming languages and AI/ML frameworks. Debug, troubleshoot, and resolve software defects efficiently.
· Work closely with cross-functional engineering, architecture, product, and security teams to integrate AI/ML solutions into existing and next-generation Ribbon products.
· Stay current with advancements in speech processing, digital signal processing, machine learning, deep learning, voice biometrics, and audio security technologies.

Requirements
· Degree in Computer Science, Electrical Engineering, Computer Engineering, Signal Processing, Speech Processing, Artificial Intelligence, Data Science, or a related field with 5-8 years of rich industry experience or PhD. (Candidates with research experience and/or with Published research papers would be preferred)
· Demonstrated proficiency in Python and/or C/C++/GOLANG, and hands-on experience with machine learning and deep learning frameworks such as PyTorch, Tensor Flow, Scikit-learn, Num Py, Pandas, and Jupyter Notebook.
· Experience developing, training, and optimizing machine learning and deep learning models for speech, audio, voice biometrics, signal classification, cybersecurity, or related applications.
· Strong understanding of Digital Signal Processing (DSP) and speech processing concepts, including:o Sampling Theory and Signal Reconstructiono Time-Domain and Frequency-Domain Analysiso Fourier Transform (FFT) and Spectral…