Collaborate with the deep learning community to implement the latest algorithms for public release in TensorRT and DL benchmarks. Identify performance opportunities and optimize SoTA DL models across the spectrum of NVIDIA accelerators, from datacenter GPUs to edge SoCs. Implement optimizations using TensorRT, its open source tools like Polygraphy, TensorRT plugins, Triton and CUDA kernels. Work and collaborate with a diverse set of teams involving performance modeling, performance analysis, kernel development and inference software development.

What you'll be doing:

• 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.

What we need to see:

• 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.

#LI-Hybrid

The base salary range is 148,000 USD - 276,000 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

You will also be eligible for equity and benefits. NVIDIA accepts applications on an ongoing basis.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.