CUDA with C++ and Python Developer

As a CUDA Developer with experience in C++ and Python, you will play a crucial role in working on cutting-edge AI and machine learning projects. Your responsibilities will include solving complex coding problems, optimizing GPU-based workloads, reviewing model-generated code, and assisting in training AI systems to deliver high-quality CUDA and parallel computing solutions. You are expected to have a strong expertise in CUDA, GPU programming, parallel computing, performance optimization, and Python-based machine learning ecosystems. Key Responsibilities: - Solve advanced CUDA and parallel computing problems involving GPU acceleration and performance optimization. - Review, evaluate, and enhance AI-generated CUDA/C++/Python code. - Analyze GPU kernel performance and optimize algorithms for throughput, latency, and memory efficiency. - Utilize CUDA libraries and frameworks like Thrust, cuBLAS, and cuDNN. - Develop high-quality prompts, solutions, explanations, and evaluations to enhance AI model reasoning and coding performance. - Debug and troubleshoot issues related to CUDA kernels, memory management, synchronization, and resource utilization. - Collaborate with cross-functional teams engaged in AI model training and evaluation. - Stay abreast of the latest advancements in CUDA, GPU architectures, and parallel computing best practices. Qualifications Required: - Bachelor's degree in Computer Science, Computer Engineering, or a related technical field. - 5+ years of professional software development experience with a strong focus on CUDA development. - Proficiency in C/C++. - Hands-on experience with Python, especially in scientific computing using PyTorch and NumPy. - Familiarity with CUDA version 12.3 or above. - Understanding of GPU programming concepts, parallel computing, and performance optimization. - Experience in optimizing code for efficient resource utilization and high-performance execution. - Knowledge of CUDA frameworks and libraries such as Thrust, cuBLAS, and cuDNN. - Ability to independently solve complex technical problems. - Strong written and verbal communication skills. - Previous involvement in AI/ML systems or large language model (LLM) projects is advantageous. As a CUDA Developer with experience in C++ and Python, you will play a crucial role in working on cutting-edge AI and machine learning projects. Your responsibilities will include solving complex coding problems, optimizing GPU-based workloads, reviewing model-generated code, and assisting in training AI systems to deliver high-quality CUDA and parallel computing solutions. You are expected to have a strong expertise in CUDA, GPU programming, parallel computing, performance optimization, and Python-based machine learning ecosystems. Key Responsibilities: - Solve advanced CUDA and parallel computing problems involving GPU acceleration and performance optimization. - Review, evaluate, and enhance AI-generated CUDA/C++/Python code. - Analyze GPU kernel performance and optimize algorithms for throughput, latency, and memory efficiency. - Utilize CUDA libraries and frameworks like Thrust, cuBLAS, and cuDNN. - Develop high-quality prompts, solutions, explanations, and evaluations to enhance AI model reasoning and coding performance. - Debug and troubleshoot issues related to CUDA kernels, memory management, synchronization, and resource utilization. - Collaborate with cross-functional teams engaged in AI model training and evaluation. - Stay abreast of the latest advancements in CUDA, GPU architectures, and parallel computing best practices. Qualifications Required: - Bachelor's degree in Computer Science, Computer Engineering, or a related technical field. - 5+ years of professional software development experience with a strong focus on CUDA development. - Proficiency in C/C++. - Hands-on experience with Python, especially in scientific computing using PyTorch and NumPy. - Familiarity with CUDA version 12.3 or above. - Understanding of GPU programming concepts, parallel computing, and performance optimization. - Experience in optimizing code for efficient resource utilization and high-performance execution. - Knowledge of CUDA frameworks and libraries such as Thrust, cuBLAS, and cuDNN. - Ability to independently solve complex technical problems. - Strong written and verbal communication skills. - Previous involvement in AI/ML systems or large language model (LLM) projects is advantageous.