
Deep Learning Audio Codec Engineer
1 week ago
Location: Remote – India (must have overlap with Sydney, Australia time zone)
About the Role:
We are seeking a Deep Audio Coding Optimization and Deployment Engineer to design, implement, and optimize deep learning models for audio coding. This role focuses on improving model efficiency, deploying models to real-world environments, and collaborating with cross-functional teams to advance audio coding technology.
Key Responsibilities:
- Develop and implement algorithms for efficient training and real-time inference of deep audio coding models.
- Optimize models for accuracy, speed, size, and computational efficiency, including model compression techniques (quantization, pruning, distillation).
- Deploy AI models as reference code for standards and ensure performance on mobile and embedded architectures.
- Profile, test, and optimize models using hardware acceleration where applicable.
- Collaborate with product managers, engineers, and researchers to define and refine model evaluation methods.
- Research and implement the latest machine learning and inference technologies to enhance audio coding solutions.
Required Qualifications & Skills:
- Master's or PhD in Electrical Engineering, Computer Science, or a related field with a focus on deep learning.
- Strong experience in audio/speech codecs and digital signal processing.
- Hands-on experience with deep learning frameworks: PyTorch, TensorFlow, ONNX, NNAPI.
- Proven experience in model optimization for constrained environments (quantization, pruning, distillation).
- Programming proficiency in Python, C/C++, or Matlab.
- Experience with embedded systems, computer architecture, or high-performance computing.
- Strong analytical, problem-solving, and team collaboration skills.
Preferred Qualifications:
- Experience with hardware-accelerated inference.
- Familiarity with standards-based audio coding projects.
- Prior contributions to open-source audio or ML libraries.
Job Type: Full-time
Pay: ₹1,600, ₹2,400,000.00 per year
Benefits:
- Health insurance
- Provident Fund
- Work from home
Education:
- Master's (Required)
Experience:
- total work: 4 years (Required)
Work Location: Remote
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