Senior Staff Machine Learning Engineer — Enterprise AI

Details

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-accelerated 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, NVIDIA is increasingly known as “the AI computing company.”

Responsibilities

• Develop Intelligent AI Solutions – Leverage NVIDIA AI technologies and GPUs to build pioneering NLP and Generative AI solutions such as Retrieval-Augmented Generation (RAG) pipelines and agentic workflows that solve real-world enterprise and supply-chain problems.
• Own Key AI Features – Drive the end-to-end development of LLM-powered applications, chatbots, and optimization engines that improve organizational efficiency and resilience.
• Design Robust ML Architectures – Create machine-learning and combinatorial-optimization designs targeting high-impact challenges across employee productivity, engineering efficiency, AIOps, and supply-chain operations.
• Collaborate Across NVIDIA – Work closely with product, research, and engineering teams to translate requirements into ML solutions and deliver measurable business outcomes.
• Mentor & Share Best Practices – Guide junior engineers and peers on ML design patterns, code quality, and experiment methodology.

Requirements

• Master’s or Ph.D. in Computer Science, Operations Research, Industrial Engineering, or a related field or equivalent experience.
• 10+ years designing, building, and deploying machine-learning models and systems in production, with 12+ years industry experience.
• Solid understanding of transformers, attention mechanisms, and modern NLP / LLM techniques; experience fine-tuning or prompting large language models.
• Strong Python skills plus deep-learning frameworks such as PyTorch or TensorFlow; familiarity with CUDA-accelerated libraries (e.g., TensorRT-LLM) is a plus.
• Proven track record to take a significant ML component or feature from concept to production and collaborate effectively with multi-functional teams.

Benefits

• Eligible for equity and benefits.
• NVIDIA is committed to fostering a diverse work environment and is an equal opportunity employer.