Deep Audio Coding Optimization

7 days ago

Sydney, New South Wales, Australia IMR Soft LLC Full time $120,000 - $180,000 per year

Job Title: Deep Audio Coding Optimization & Deployment Engineer (P4)

Location: Sydney, Australia (in-office preferred / remote accepted). Open to APAC (with overlap to Sydney time zone). LATAM & Europe are also acceptable. Eastern Europe may be challenging.

Duration: 1 Year Contract (40 hrs/week)

Rate: Open, DOE

Summary

We are looking for a Deep Audio Coding Optimization & Deployment Engineer to design and implement optimization strategies for training, refining, and deploying deep learning–based audio coding models. The role involves model compression (quantization, pruning, distillation), custom deployment for mobile inference engines, and performance optimization in a reference implementation environment. You will collaborate with cross-functional teams to integrate optimized models into standards and refine methods for performance evaluation.

Key Responsibilities

  • Develop and implement algorithms/software for efficient offline training and real-time inference of deep audio coding models.
  • Monitor and evaluate model performance in production, optimizing for accuracy, speed, size, and compute efficiency.
  • Optimize AI models for deployment as standards reference code.
  • Profile and test reference code in mobile architectures.
  • Design and implement tooling/strategies for optimized model development and deployment.
  • Collaborate with product managers, engineers, and researchers to integrate optimized models into workflows.

Requirements

  • Master's or PhD in Electrical Engineering, Computer Science, or related field with 4+ years' experience in deep learning.
  • Strong background in AI/ML theory and practice, with recent deep learning experience.
  • Experience with audio codecs and digital signal processing (audio/speech focus).
  • Hands-on with model optimization for constrained environments (pruning, quantization, distillation, etc.).
  • 3+ years experience with ML frameworks (PyTorch, ONNX, NNAPI, TensorFlow, etc.).
  • 3+ years programming in Python, C/C++, or MATLAB.
  • Familiarity with embedded systems, computer architecture, and high-performance computing.
  • Knowledge of optimizing ML models for inference using hardware acceleration (a plus).
  • Strong software engineering practices (VCS, CI/CD).
  • Excellent problem-solving, analytical, and teamwork skills.
  • Strong written and verbal communication skills.

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