Senior AI and ML Engineer, Agentic AI Systems

We are seeking a Senior AI and ML Engineer to work on Agentic AI Systems at NVIDIA.

You will design and develop AI-powered systems combining large language models, retrieval architectures, knowledge systems, and agentic workflows. Your responsibilities will include:

• Developing capabilities that enable AI systems to reason across multiple information sources and generate high-quality recommendations.
• Building intelligent workflows that continuously improve through evaluation, feedback, and experimentation.
• Exploring emerging approaches in AI agents, planning systems, memory architectures, reasoning frameworks, and autonomous workflows.
• Collaborating with software engineers to transform research concepts into reliable production capabilities.
• Designing and executing experiments to improve model accuracy, robustness, and user trust.
• Building evaluation, benchmarking, and testing frameworks for AI systems.
• Designing and optimizing retrieval architectures, semantic search systems, vector databases, and knowledge pipelines.

Requirements:

• BS, MS, or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field.
• 5+ years of professional experience in software engineering with proficiency in Python.
• Experience building AI/ML systems in production environments.
• Hands-on experience with large language models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, or intelligent software systems.
• Experience designing experiments and evaluating model performance.
• Strong understanding of machine learning fundamentals and modern AI system architectures.
• Familiarity with retrieval systems, embeddings, vector databases, semantic search technologies, or information retrieval.

Preferred qualifications include:

• Experience building production AI copilots, agents, or autonomous systems.
• Experience designing evaluation frameworks, benchmark suites, or model comparison pipelines.
• Expertise in retrieval systems, semantic search, ranking systems, recommendation systems, or knowledge graphs.
• Experience improving AI accuracy through retrieval optimization, workflow design, and prompt engineering.

The ideal candidate combines strong machine learning intuition with rigorous experimentation, quantitative analysis, and software engineering excellence.