Senior AI and ML Engineer, Agentic AI Systems at NVIDIA

Hà Nội, , Vietnam -

Full Time

Start Date

Immediate

Expiry Date

05 Oct, 26

Salary

0.0

Posted On

07 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Large Language Models, Retrieval-Augmented Generation, AI Agents, Vector Databases, Semantic Search, Machine Learning, Software Engineering, Prompt Engineering, Model Evaluation, Knowledge Graphs, Reasoning Frameworks

Industry

Computer Hardware Manufacturing

Description

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. NVIDIA pioneered accelerated computing. Today, our AI infrastructure powers global intelligence, transforming every industry. Learn more about NVIDIA.

Responsibilities

Design and develop AI-powered systems combining LLMs, retrieval architectures, and agentic workflows to solve complex technical problems. Build and optimize evaluation frameworks and knowledge pipelines to improve AI reasoning, accuracy, and reliability in production.