AI Safety Machine Learning Engineer, LLM MLOps

Key Skills

Aartificial intelligence, Algorithms, Analytical and Problem solving, Computer Vision (CV), Framework, Keras software library, Large Language Models - LLMs, Machine learning techniques, MLOps tools, PyTorch

Job Description

We are looking for a Principal Machine Learning Engineer to join our growing team focused on Enterprise AI! 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.” Are you ready to challenge yourself and build outstanding software by collaborating with the brightest minds in the industry? Join us at the forefront of technological innovation!

We are accelerating enterprise AI efforts and creating innovative solutions by leveraging Agentic AI, Retrieval-Augmented Generation (RAG), Generative AI (GenAI), and Large Language Models (LLMs). Our goal is to empower large-scale enterprise applications, from smart assistants and software engineering productivity to advanced analytics and supply chain optimization, using NVIDIA AI solutions. The team is looking for candidates with a strong background in machine learning—particularly in Agentic AI, RAG, NLP, deep learning (e.g., transformers), and reinforcement learning—as well as hands-on experience with agentic frameworks like LangChain and LangGraph, and deep learning frameworks such as PyTorch and TensorFlow.

What you'll be doing:

• Build real AI products by leveraging NVIDIA AI solutions and GPUs, harnessing state-of-the-art NLP and GenAI techniques, including RAG and agentic workflows, to solve advanced enterprise challenges.

• Lead engineers and researchers to develop new large language models for enterprise applications, chatbots, and advanced AI solutions.

• Design and implement machine learning architectures to meet a variety of business requirements.

• Collaborate with cross-functional teams to identify, design, and build ML solutions that drive business growth.

What we need to see:

• A Master’s or Ph.D. in Computer Science or a related technical field, or equivalent experience.

• 10+ years of experience building and deploying ML models and architectures in a production environment.

• Solid knowledge of NLP and deep learning techniques (transformers, attention mechanisms, etc.).

• Proven Python and deep learning programming skills using PyTorch, TensorFlow, or similar.

• A highly motivated, innovative, and curious mindset about new technologies.

• Ability to think independently and manage your development & research efforts.

Ways to stand out from the crowd:

• Practical RAG implementations and agentic frameworks experience (e.g., LangChain, LangGraph).

• Experience working with LLMs or conversational systems.

• Expertise in end-to-end ML systems design: from data collection and processing to model training, evaluation, deployment, and monitoring.

• A track record of publishing in NLP/AI conferences or patenting innovative ideas.

• NVIDIA is widely considered one of the technology world’s most desirable employers. We have some of the most forward-thinking and dedicated people on the planet working for us. If you’re creative, autonomous, and excited about shaping the future of Enterprise AI, we want to hear from you!

• The base salary range is 272,000 USD - 425,500 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

• You will also be eligible for equity and benefits. NVIDIA accepts applications on an ongoing basis.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.