Senior Staff Machine Learning Engineer — Agentic AI

NVIDIA•Us, CA

About The Position

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, we are increasingly known as “the AI computing company.” We are seeking a Senior Staff Machine Learning Engineer to join our Enterprise AI team and build intelligent, scalable solutions that transform enterprise operations. You will develop and productionize advanced AI systems spanning smart assistants, software-engineering productivity, and data-driven analytics.

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 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.

Nice To Haves

Agentic AI Mastery – Practical experience with frameworks such as LangChain or LangGraph and a deep understanding of multi-step reasoning and planning.
LLM Inference Optimization – Expertise in accelerating LLM inference (e.g., KV caching, quantization) to achieve low latency at scale.
End-to-End ML Systems Ownership – A portfolio showing full lifecycle ownership, from data ingestion to monitoring and continuous improvement.
Research Impact – Publications or patents that advance NLP or enterprise AI.

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, etc..
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.