Software Engineer

Responsibilities

• Port and deploy deep learning models from frameworks like PyTorch and TensorFlow to proprietary or commercial ML accelerator hardware platforms.
• Analyze and improve the performance of ML models for target hardware, focusing on latency and throughput.
• Contribute to model quantization efforts (e.g., INT8) to reduce model size and accelerate inference while maintaining accuracy.
• Use profiling tools to identify and fix performance bottlenecks in the ML inference pipeline on the accelerator.

Requirements

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.
• Proficiency in deep learning frameworks such as PyTorch and TensorFlow.
• Hands-on experience deploying and optimizing models on GPUs or other specialized accelerators.
• Experience with model quantization (e.g., Post-Training Quantization).
• Strong proficiency in C++ and Python.
• Familiarity with ML inference engines and runtimes such as TensorRT, OpenVINO, or TensorFlow Lite.
• Foundational understanding of computer architecture principles and proficiency with Git.

Preferred Qualifications

• Experience with GPU programming models like CUDA/cuDNN.
• Knowledge of hardware-aware model design and compiler technologies for deep learning.
• Experience with real-time or embedded systems.
• Knowledge of cloud platforms (AWS, GCP, Azure) and CI/CD pipelines for ML models.

About the Company

NXP Semiconductors enables a smarter, safer, and more sustainable world through innovation. As a world leader in secure connectivity solutions for embedded applications, NXP pushes boundaries in the automotive, industrial & IoT, mobile, and communication infrastructure markets.