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.
Skills Required
- BS, MS, or PhD in Computer Science, Machine Learning, AI, or related technical field
- 5+ years professional software engineering experience
- Proficiency in Python
- Experience building AI/ML systems in production environments
- Hands-on experience with large language models (LLMs), 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, or information retrieval
- Strong debugging, analytical thinking, and problem-solving skills
- Experience building production AI copilots, agents, or autonomous systems
- Experience designing evaluation frameworks, benchmark suites, or model comparison pipelines
- Expertise in retrieval systems, ranking systems, recommendation systems, or knowledge graphs
- Experience training, fine-tuning, adapting, or evaluating foundation models
- Experience applying AI to software engineering, debugging, or developer productivity workflows
- Contributions to open-source AI projects, research publications, or technical communities
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
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, NVIDIA is increasingly known as “the AI computing company.”
