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NVIDIA

US-CA-Santa Clara · $224,000

Senior Staff Machine Learning Engineer — Agentic AI

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Enterprise-AI_JR2000014 SCRAPED AT: 2025-12-31T17:19:35Z

Position Overview

NVIDIA is 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.

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.

Requirements & Skills

  • What We Need to See:

    • 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.

  • Ways to Stand Out from the Crowd:

    • 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.
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