onsite
Senior AI Architect, Applied Research
Senior AI Architect, Applied Research
NVIDIA is seeking Senior AI Architects to lead cutting-edge research and development in AI, focusing on deep learning, large language models, and generative AI. This role involves designing and implementing novel AI algorithms, optimizing models for NVIDIA GPUs, and collaborating with cross-functional teams to integrate AI solutions into products.
About the role
About the team
NVIDIA’s Applied Research organization has openings for Senior AI Architects. Our team of applied researchers and engineers is passionate about creating the next generation of AI technologies, from large language models and foundation models to multimodal and generative AI models. We work on cutting-edge research, develop novel algorithms, and build scalable systems that enable new AI capabilities across various domains.
What you'll be doing
- Leading research and development efforts in AI, focusing on deep learning, computer vision, large language models, transformers, diffusion models, speech processing, and conversational AI.
- Designing and implementing novel AI algorithms and models, pushing the boundaries of what's possible with AI.
- Optimizing AI models for performance and scalability on NVIDIA GPUs and distributed systems.
- Collaborating with cross-functional teams to integrate AI solutions into products and platforms.
- Mentoring junior researchers and engineers, fostering a culture of innovation and excellence.
- Staying up-to-date with the latest advancements in AI research and technologies, and evaluating their potential impact on our projects.
- Publishing research findings in top-tier conferences and journals.
What we need to see
- PhD or Master's degree in Computer Science, Electrical Engineering, or a related field, with a focus on AI/Machine Learning.
- 8+ years of experience in AI research and development, with a strong publication record in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ACL).
- Expertise in deep learning frameworks such as PyTorch or TensorFlow, and experience with distributed training of large-scale models.
- Strong programming skills in Python and experience with software development best practices.
- Deep understanding of AI model architectures, including transformers, CNNs, GANs, and diffusion models.
- Experience with cloud-native infrastructure and MLOps tools (e.g., Kubernetes, MLflow) is a plus.
- Excellent communication and collaboration skills, with the ability to work effectively in a fast-paced, dynamic environment.
Ways to stand out from the crowd
- Experience with developing and deploying large language models or foundation models.
- Familiarity with NVIDIA's AI platform and tools (e.g., NVIDIA NeMo, Triton Inference Server).
- Proven track record of leading successful AI projects from conception to deployment.