Senior AI and ML Engineer, Agentic AI Systems

Overview

• At NVIDIA, we are building the next generation of intelligent software systems powered by large language models, retrieval systems, AI agents, and sophisticated reasoning capabilities.
• As AI evolves from assistants to autonomous systems capable of reasoning, planning, learning, and acting, we are creating platforms that help engineers solve sophisticated technical problems faster and more effectively.
• We are looking for a Senior AI/ML Engineer to push forward advancements in reasoning.
• The role includes improving knowledge retrieval and autonomous problem-solving AI systems.
• What You'll Be Doing: Design and develop AI-powered systems that combine large language models, retrieval architectures, knowledge systems, and agentic workflows.
• Develop capabilities that enable AI systems to reason across multiple information sources and generate high-quality recommendations.
• Build intelligent workflows that continuously improve through evaluation, feedback, and experimentation.
• Explore emerging approaches in AI agents, planning systems, memory architectures, reasoning frameworks, and autonomous workflows.
• Collaborate with software engineers to transform research concepts into reliable production capabilities.
• Design and execute experiments to improve model accuracy, robustness, and user trust.
• Build evaluation, benchmarking, and testing frameworks for AI systems.
• Design and optimize retrieval architectures, semantic search systems, vector databases, and knowledge pipelines.
• What We Need To See: BS, MS, or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field.
• 5+ years professional experience in software engineering skills 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.
• Strong debugging, analytical thinking, and problem-solving skills.
• Ways To Stand Out From The Crowd: 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.
• Experience training, fine-tuning, adapting, or evaluating foundation models.
• Experience applying AI to software engineering, debugging, developer productivity, or operational workflows.
• Contributions to open-source AI projects, research publications, or technical communities.
• Deliver measurable improvements in AI accuracy, reliability, and user trust.
• Establish scalable evaluation and benchmarking methodologies.
• Advance the state of the art in retrieval, reasoning, and agentic AI systems.
• Influence technical direction across strategic AI initiatives.
• We are looking for engineers who treat AI accuracy as an engineering discipline.
• The ideal candidate combines strong machine learning intuition with rigorous experimentation, quantitative analysis, and software engineering excellence.
• They are equally comfortable reading research papers, designing benchmark datasets, analyzing failure cases, optimizing retrieval pipelines, and shipping reliable production systems.