FAQs
What experience is required for the role of AI/ML GPU Software Engineer?
The ideal candidate should have at least 3+ years of experience in software engineering with a focus on leveraging NVIDIA GPUs for machine learning. They should also have proficiency in Nvidia GPU frameworks, understanding of distributed training techniques, and expertise in GPU hardware and performance optimization.
What are the key responsibilities of an AI/ML GPU Software Engineer?
The responsibilities include designing and implementing GPU-accelerated algorithms and software frameworks, optimizing deep learning workflows for NVIDIA GPUs, developing custom CUDA kernels, collaborating with data scientists and engineers, staying up to date with advancements in GPU architectures, and contributing to a team culture of diversity and innovation.
What programming languages and frameworks should an AI/ML GPU Software Engineer be proficient in?
The candidate should have proficiency in programming languages such as Python and Julia, as well as experience with machine learning packages like PyTorch, TensorFlow, and Jax. They should also be familiar with Nvidia GPU frameworks like CUDA, cuDNN, TensorRT, and NCCL.
What additional skills would be advantageous for an AI/ML GPU Software Engineer?
Experience with stream processing frameworks, Google Cloud Platform MLOps tools, serverless computing, event-driven architectures, monitoring tools for distributed systems, and applications of AI/ML in energy, weather, or climate fields would be advantageous for this role.