AI Software Platform Engineer

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR Master's degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field. Master's degree in Computer Science, Electrical Engineering, or related field. Proficiency in programming languages such as C, C++, or Python. Strong knowledge of Object-Oriented Programming, data structures, algorithms and operating system. Self-motivated and capable of working independently with minimal oversight. Ability to communicate technical concepts effectively and work collaboratively within cross-functional teams. Bachelor's degree in Computer Science, Electrical Engineering, or a related field. Proficiency in languages like C++, CUDA, or OpenCL for GPU programming. Understanding of GPU/DSP/NPU architectures and parallel programming concepts. Familiarity with embedded systems and constraints related to edge devices. Experience in low-level programming for efficient hardware utilization. Knowledge of optimizing algorithms and libraries for GPU/DSP/NPU execution. Ability to analyze and optimize performance bottlenecks in GPU/NPU code. Proficiency with version control systems like Git and software development tools like Gerrit and JIRA. Master's or Ph.D. in Computer Science, Electrical Engineering, or a related field. Familiarity with frameworks such as TensorFlow, PyTorch for DSP/GPU/NPU integration. Understanding of mainstream ML runtime frameworks formats and their runtime environments. Experience in optimizing code specifically for different DSP/GPU/NPU architectures. Understanding of machine learning and deep learning concepts for efficient utilization of hardware. Proven experience in fine-tuning performance-critical applications on edge devices. Knowledge of hardware-software co-design principles for edge devices. Proficiency in using tools for debugging and profiling GPU/NPU code. Strong communication skills to collaborate with hardware engineers and communicate complex technical concepts effectively. Expertise in at least one of the following areas Enthusiasm in machine learning technology. Hands-on experience in design/implementation of deep learning networks via modern frameworks, including TensorFlow, Pytorch, etc. Ability to quickly learn new technologies and work on resolving customer reported technical problems during mobile or wireless communication product development cycles. Excellent analytical, problem solving and Communication skills and willingness to work with customers Deliver high-quality code working with open-source software communities. Performance profiling and optimization for parallel computing: Strong understanding and practical experience in profiling and optimizing performance for parallel computing tasks.