Deep Audio Coding Optimization & Deployment Engineer

<p><p><b>Job Title:</b> Deep Audio Coding Optimization & Deployment Engineer (P4)<br/><br/><b>Location:</b> Sydney, Australia (in-office preferred / remote accepted)<br/><br/>- Open to APAC (with overlap to Sydney time zone)<br/><br/>- LATAM & Europe are also acceptable<br/><br/>- Eastern Europe may be challenging<br/><br/><b>Duration:</b> 1+ Year Contract (40 hrs/week)<br/><br/><b>Summary : </b><br/><br/>We are looking for a Deep Audio Coding Optimization & Deployment Engineer to design and implement optimization strategies for training, refining, and deploying deep learningbased audio coding models.<br/><br/>The role involves model compression (quantization, pruning, distillation), custom deployment for mobile inference engines, and performance optimization in a reference implementation environment.<br/><br/>You will collaborate with cross-functional teams to integrate optimized models into standards and refine methods for performance evaluation.<br/><br/><b>Key Responsibilities :</b><br/><br/>- Develop and implement algorithms/software for efficient offline training and real-time inference of deep audio coding models.<br/><br/>- Monitor and evaluate model performance in production, optimizing for accuracy, speed, size, and compute efficiency.<br/><br/>- Optimize AI models for deployment as standards reference code.<br/><br/>- Profile and test reference code in mobile architectures.<br/><br/>- Design and implement tooling/strategies for optimized model development and deployment.<br/><br/>- Collaborate with product managers, engineers, and researchers to integrate optimized models into workflows.<br/><br/><b>Requirements :</b><br/><br/>- Masters or PhD in Electrical Engineering, Computer Science, or related field with 4+ years experience in deep learning.<br/><br/>- Strong background in AI/ML theory and practice, with recent deep learning experience.<br/><br/>- Experience with audio codecs and digital signal processing (audio/speech focus).<br/><br/>- Hands-on with model optimization for constrained environments (pruning, quantization, distillation, etc.<br/><br/>- 3+ years of experience with ML frameworks (PyTorch, ONNX, NNAPI, TensorFlow, etc.<br/><br/>- 3+ years programming in Python, C/C++, or MATLAB.<br/><br/>- Familiarity with embedded systems, computer architecture, and high-performance computing.<br/><br/>- Knowledge of optimizing ML models for inference using hardware acceleration (a plus).<br/><br/>- Strong software engineering practices (VCS, CI/CD).<br/><br/>- Excellent problem-solving, analytical, and teamwork skills.<br/><br/>- Strong written and verbal communication skills.</p><br/></p> (ref:hirist.tech)