Senior Systems Software Engineer, TAO Deep Learning

NVIDIA is hiring a Senior Systems Software Engineer, Deep Learning to join the TAO Toolkit Deep Learning Architectures team. Our team develops scalable and pioneering training, fine-tuning, and optimization algorithms for Computer Vision and Multi-Modal AI, to help advance the state of the art while improving performance. 

We are seeking someone who can help advance, scale, and optimize our Deep Learning Architectures. If you have a passion for brand-new technologies and a commitment to developing scalable, optimized, and ethical AI, we invite you to join our dynamic team at NVIDIA. In this role, you will be developing state-of-the-art algorithms to train computer vision models and implementing methods to optimize these models for latency.

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

NVIDIA is widely considered to be one of the technology industry's most desirable employers. We have some of the most forward-thinking and hard-working people working with us and our engineering teams. If you're a creative engineer with a real passion for building scalable and robust infrastructure, we want to hear from you.

The base salary range is 148,000 USD - 287,500 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

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.