Machine Learning Engineer (Audio & Video)

Key Responsibilities:

• Machine Learning Development:
• Design, develop, and train machine learning algorithms for real-time audio and video enhancement systems, ensuring optimal performance in production environments.
• Optimize models for seamless deployment, maintaining high quality and low latency for real-world applications.
• Audio and Video Processing:
• Implement cutting-edge techniques for noise reduction, audio clarity, video resolution upscaling, and frame interpolation.
• Develop multi-modal models that integrate audio and video processing to improve overall user experience.
• Data Processing:
• Build and maintain robust data processing pipelines to curate, organize, and manage large, complex audio-visual datasets required for training models.
• Implement efficient data preprocessing methods, including feature extraction, synchronization, and augmentation for both audio and video data.
• Framework Development:
• Develop modular, Python-based frameworks to streamline the evaluation, benchmarking, and deployment processes for audio and video machine learning algorithms.
• Ensure frameworks are scalable and adaptable for continuous experimentation and refinement.
• Collaboration and Stakeholder Engagement:
• Collaborate with interdisciplinary teams, including software engineers, data scientists, and product managers, to align machine learning solutions with project objectives.
• Communicate technical concepts, challenges, and results effectively to both technical and non-technical stakeholders.

Required Skills & Qualifications:

• Proven expertise in machine learning, with a strong focus on audio and video signal processing or real-time systems.
• Proficiency in Python, including experience with developing frameworks and pipelines for machine learning projects.
• Hands-on experience with large-scale data processing, feature engineering, and model evaluation techniques.
• Familiarity with machine learning libraries and tools such as TensorFlow, PyTorch, or scikit-learn.
• Strong analytical and problem-solving skills, with a focus on delivering production-ready solutions.

Preferred Skills:

• In-depth knowledge of audio and video processing techniques, including noise reduction, clarity enhancement, and resolution improvement.
• Experience with cloud platforms or distributed computing for large-scale data and model processing.
• Strong understanding of software engineering best practices, including version control, CI/CD pipelines, and system design for machine learning applications.

Key Achievements During Tenure:

• Successfully developed and deployed machine learning algorithms for real-time audio and video enhancement systems in production.
• Designed and implemented data pipelines capable of managing large-scale, synchronized audio-visual datasets efficiently.
• Streamlined model evaluation processes through the development of modular and scalable Python frameworks.