Nvidia Principal Machine Learning Engineer - Enterprise AI 

United States, California 

625619930

What You’ll Be Doing:

• 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 developinglarge‑language‑model–poweredapplications, chatbots, and optimization engines that directly improve chip‑design supply‑chain efficiency and resilience.

• Design ML & Optimization Architectures – Create and implement machine‑learning andcombinatorial‑optimizationarchitectures (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.

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.

Ways to Stand Out from the Crowd:

• Agentic AI Expertise – 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) to achieve sub‑second latency at scale.

• End‑to‑End ML Systems Design – A portfolio showing ownership of the full ML lifecycle, from data ingestion to monitoring and continuous improvement.

• Research Impact – Publications or patents that advance NLP, or supply‑chain AI.