Nvidia Senior Systems Software Engineer TAO Deep Learning 

United States, California 

632118013

What you’ll be doing:

• Architect, analyze, develop, and prototype key deep learning algorithms and solutions as a core member of our growing software team.

• Collaborate with diverse software, research, and hardware teams across geographies to analyze the interplay of hardware and software architectures solve critical problems and future applications

• Develop algorithms (such as zero/few-shot learning, unsupervised learning) to address data scarcity and collection challenges.

• Apply generative models (Diffusion, GANs, VAEs) and LLMs for data generation to overcome data scarcity issues.

• Drive the design and implementation of complex AI projects, providing technical guidance and support, mentoring junior engineers.

• Create and refine algorithms for a varied number of computer vision and multi-modal tasks

What we need to see:

• 3 years or more of working experience

• MS or PhD or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, or a related field with a focus on Deep Learning, Machine Learning, and Computer Vision.

• Experience in algorithm development for AI, computer vision or multi-modal algorithms, especially with LLMs and Multi-Modal Foundation models.

• Experience working with and curating multi-modal datasets.

• Experience with algorithms including zero/few-shot learning, fully-supervised, weakly-supervised, self-supervised and unsupervised learning techniques, and domain adaptation techniques like Parameter Efficient Fine-Tuning

• Proficiency in working with deep learning frameworks such as TensorFlow and PyTorch, Strong programming skills in Python and/or C++, and Experience developing integrated AI solutions.

• Ability to lead projects, manage timelines, and deliver results.

• Expert analytical and problem-solving skills with a focus on practical and scalable AI solutions.

• Strong communication skills and ability to work in a collaborative environment.

Ways to stand out from the crowd:

• Proven experience in building and deploying optimized AI models

• Experience with model optimization techniques like model distillation, quantization, and pruning

• Experience with techniques for optimizing training and fine-tuning pipeline development such as PEFT, AutoML

• Background with NVIDIA SDKs such as TensorRT, RAPIDS, CUDA, and CUDNN