About The Position
Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in an inclusive, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world. NVIDIA seeks a Software Engineer specializing in Deep Learning Inference for our growing team. As a key contributor, you will help design, build, and optimize the GPU-accelerated software that powers today’s most sophisticated AI applications. Our team is responsible for developing and maintaining high-performance open-source frameworks, which are at the forefront of efficient large-scale model serving and inference. You will play a central role in improving these platforms, facilitating smooth deployment and serving of groundbreaking language models. You’ll work closely with the deep learning community to implement the latest algorithms for public release in inference frameworks. Your work will focus on identifying and driving performance improvements for state-of-the-art LLM and Generative AI models across NVIDIA accelerators, from datacenter GPUs to edge SoCs. You'll bring to bear open-source tools and plugins—including CUTLASS, OAI Triton, NCCL, and CUDA kernels—to implement and optimize model serving pipelines.
Requirements
- Pursuing or recently completed a MS or PhD Computer Engineering, Computer Science, EECS, AI or related field or equivalent experience.
- Software development experience.
- Excellent C/C++ programming and software design skills.
- SW Agile skills are helpful and Python experience is a plus.
Nice To Haves
- Prior experience with training, deploying or optimizing the inference of DL models in production is a plus.
- Prior background with performance modeling, profiling, debug, and code optimization or architectural knowledge of CPU and GPU is a plus.
- GPU programming experience (CUDA, OAI TRITON or CUTLASS) is a plus.
- Contribute to deep learning software projects, such as PyTorch, vLLM, and SGLang to drive advancements in the field.
- Experience with Multi GPU Communications (NCCL, NVSHMEM)
Responsibilities
- Performance optimization, analysis, and tuning of DL models in various domains like LLM, Multimodal and Generative AI.
- Scale performance of DL models across different architectures and types of NVIDIA accelerators.
- Contribute features and code to NVIDIA’s inference libraries, vLLM and SGLang, FlashInfer and LLM software solutions.
- Work with cross-collaborative teams across frameworks, NVIDIA libraries and inference optimization innovative solutions.
Benefits
- equity
- benefits
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
