Nvidia Principal DGX Cloud Machine Learning Architect 

United States, California 

717569556

What You’ll Be Doing:

• Develop and optimize innovative generative AI models such as VLMs and WFMs using NVIDIA’s AI software stack for Physical AI applications.

• Design and implement sophisticated evaluation methodologies and automation techniques to streamline model assessments.

• Collaborate with world-class teams across NVIDIA to refine and improve foundation models for AI-powered solutions.

• Analyze, profile, and improve model performance to drive efficiency, scalability, and precision.

• Architect scalable, modular software platforms that improve AI adoption by DGX Cloud customers, ensuring seamless user experiences and broad model support.

What We Need to See:

• Master’s, PhD, or equivalent experience in Computer Science, AI, Applied Math, or a related field (or equivalent experience).

• 15+ years of demonstrated ability in deep learning, AI model development, or research.

• Proficiency in model engineering, including data curation, fine-tuning, and evaluation.

• Strong software design and debugging skills, with expertise in performance analysis and test design.

• Advanced Python and PyTorch proficiency, with experience in ML tools such as Hugging Face.

• Deep understanding of algorithms, programming fundamentals, and AI system architectures.

• Excellent written and verbal communication skills, with the ability to work independently and collaboratively in a multifaceted environment.

• Cloud AI Expertise: Hands-on experience optimizing and deploying AI systems at scale on major cloud platforms (AWS, Azure, GCP), focusing on performance and cost-efficiency.

• Automation & Scalability: Proven track record to build automated evaluation methodologies and scalable data curation pipelines to continuously boost model performance.

Ways to Stand Out from the Crowd:

• Industry Impact: A consistent record of transitioning groundbreaking AI research into production, particularly in robotics or autonomous systems.

• Open-Source Leadership: Active contributions to key AI frameworks (e.g., PyTorch, Hugging Face) or other significant open-source projects.

• Thought Leadership: Published research, patents, or recognition in generative AI, Physical AI, or related fields that highlight your ability to drive innovation.

• Cross-Disciplinary Leadership: Experience leading multi-functional teams (engineering, research, product) to deliver coordinated AI solutions.

• Performance Optimization Mastery: Expertise in GPU acceleration, hardware/software co-design, and deep performance optimization for AI workloads.