Senior Deep Learning Software Engineer, Inference

Details

We are now looking for a Senior Deep Learning Software Engineer, Inference! NVIDIA is seeking an experienced Deep Learning Engineer focused on analyzing and improving performance of DL inference! NVIDIA is rapidly growing our research and development for Deep Learning Inference and is seeking excellent Software Engineers at all levels of expertise to join our team. Companies around the world are using NVIDIA GPUs to power a revolution in deep learning, enabling breakthroughs in areas like LLM, Generative AI, Recommenders and Vision that has put DL into every software solution. Join the team that builds the software to enable the performance optimization, deployment and serving of these DL solutions. We specialize in developing GPU-accelerated Deep learning software like TensorRT, DL benchmarking software and performant solutions to deploy and serve these models.

Responsibilities

• Performance optimization, analysis, and tuning of DL models in various domains like LLM, Recommender, GNN, Generative AI.
• Scale performance of DL models across different architectures and types of NVIDIA accelerators.
• Contribute features and code to NVIDIA's inference benchmarking frameworks, TensorRT, Triton and LLM software solutions.
• Work with cross-collaborative teams across generative AI, automotive, image understanding, and speech understanding to develop innovative solutions.

Requirements

• Masters or PhD or equivalent experience in relevant field (Computer Engineering, Computer Science, EECS, AI).
• At least 3 years of relevant software development experience.
• You'll need excellent C/C++ programming and software design skills. SW Agile skills are helpful and Python experience is a plus.
• Prior experience with training, deploying or optimizing the inference of DL models in production is a plus.
• Prior experience with performance modelling, profiling, debug, and code optimization or architectural knowledge of CPU and GPU is a plus.
• GPU programming experience (CUDA or OpenCL) is a plus.

GPU deep learning has provided the foundation for machines to learn, perceive, reason and solve problems posed using human language. The GPU started out as the engine for simulating human imagination, conjuring up the amazing virtual worlds of video games and Hollywood films. Now, NVIDIA's GPU runs deep learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and artificial intelligence come together in our architecture. Two modes of the human brain, two modes of the GPU. This may explain why NVIDIA GPUs are used broadly for deep learning, and NVIDIA is increasingly known as “the AI computing company.” Come, join our DL Architecture team, where you can help build the real-time, cost-effective computing platform driving our success in this exciting and quickly growing field.