Job description
Exp: 2-3 years 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
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. More information about NXP in India... #LI-29f4