Senior Software Engineer Modelzoo

Job Title: Software Engineer (2-3 years experience) Job Summary We're looking for a skilled and motivated Machine Learning Software to join our team. The ideal candidate will have a solid foundation in deep learning and a strong interest in optimizing and deploying ML models on specialized hardware. This role involves implementing model optimizations, with a particular focus on quantization, to improve the performance of machine learning inference on target platforms. Key Responsibilities Model Porting & Deployment: Port and deploy deep learning models from frameworks like PyTorch and TensorFlow to proprietary or commercial ML accelerator hardware platforms. Performance Optimization: Analyze and improve the performance of ML models for target hardware, focusing on latency and throughput. Quantization: Contribute to model quantization efforts (e.g., INT8 ) to reduce model size and accelerate inference while maintaining model accuracy. Profiling & Debugging: Use profiling tools to identify and fix performance bottlenecks in the ML inference pipeline on the accelerator. Required Qualifications Technical Skills: Proficiency in deep learning frameworks such as PyTorch and TensorFlow . Hands-on experience with deploying and optimizing models on GPUs or other specialized accelerators. Some experience with model quantization ( Post-Training Quantization ). Strong proficiency in C++ and Python . Experience with GPU programming models like CUDA/cuDNN is a plus. Familiarity with ML inference engines and runtimes (e.g., TensorRT , OpenVINO , TensorFlow Lite ). Foundational understanding of computer architecture principles. Version Control: Proficient with Git and collaborative development workflows. Education: Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field. Preferred Qualifications Knowledge of hardware-aware model design. Familiarity with compiler technologies for deep learning. Experience with real-time or embedded systems. Knowledge of cloud platforms (AWS, GCP, Azure). Experience with CI/CD pipelines for ML models.

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