NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI—the next era of computing—with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company.”
We are seeking a Principal Machine Learning Engineer to join our Enterprise AI team and lead the creation of intelligent, scalable AI solutions that transform enterprise operations—with a special focus on optimizing the complex, global supply chains that underpin NVIDIA’s chip‑design and manufacturing programs. You will develop and productionize advanced AI systems spanning smart assistants, software‑engineering productivity, analytics, and, critically, semiconductor supply‑chain optimization.

WHAT WE NEED TO SEE:

• Master’s or Ph.D. in Computer Science, Operations Research, Industrial Engineering, or a related technical field, or equivalent experience.
• 12+ years of experience designing, building, and deploying ML models and systems in production.
• Demonstrated, hands-on experience applying AI techniques to supply‑chain challenges (e.g., demand forecasting, wafer‑level yield optimization, capacity planning, material logistics, or supplier risk analytics).
• Strong knowledge of transformers, attention mechanisms, and modern NLP/GenAI techniques.
• Expert‑level Python plus deep‑learning frameworks such as PyTorch or TensorFlow; familiarity with CUDA‑accelerated libraries (cuOpt, TensorRT‑LLM) is a plus.
• Proven ability to think independently, drive research and development efforts, and mentor multidisciplinary engineering teams.
• Highly motivated, curious, and eager to push the boundaries of what AI can do for complex supply‑chain systems.

• Develop Intelligent AI Solutions – Leverage NVIDIA AI technologies and GPUs to build cutting‑edge NLP and Generative AI solutions—such as Retrieval‑Augmented Generation (RAG) pipelines and agentic workflows—that solve real‑world enterprise and supply‑chain problems.
• Lead AI Product Development – Guide engineers and researchers in developing large‑language‑model–powered applications, chatbots, and optimization engines that directly improve chip‑design supply‑chain efficiency and resilience.
• Design ML & Optimization Architectures – Create and implement machine‑learning and combinatorial‑optimization architectures (e.g., using NVIDIA cuOpt) tailored to semiconductor supply‑chain use cases such as multi‑echelon inventory, yield‑constrained scheduling, and supplier risk mitigation.
• Collaborate Across NVIDIA – Partner with supply‑chain operation teams to identify high‑impact opportunities, translate requirements into ML solutions, and drive measurable business outcomes.