Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world.
What you’ll be doing:
- Evaluate emerging and established AI tools, agent harnesses, and models on representative NVIDIA engineering workflows, comparing task success, output quality, reliability, latency, cost, and enterprise readiness.
- Spot opportunities where AI is the best tool. Uncover gaps, and recommend AI-first approaches over conventional solutions grounded in hands-on evaluation of modern AI-native tools.
- Design, implement, and validate reference workflows for coding, testing, code review, and troubleshooting using tools such as Codex, Claude, Cursor, Greptile, and CodeRabbit.
- Introduce technologies enabling massively parallel systems to improve turnaround time by an order of magnitude.
- Optimize for performance and cost by identifying bottlenecks across training, evaluation, and testing workflows and improve throughput, latency, and efficiency
- Collaborate with AI product vendors to gain deep insights of the AI industry, and share them with leaders and developers internally.
- Work with core and emerging tool vendors to evaluate new capabilities, influence product roadmaps, resolve adoption issues, and introduce useful features into NVIDIA's AI ecosystem.
- Provide AI tool and model selection guidance, recommended configurations, workflow examples, onboarding materials, and hands-on training.
What we need to see:
- MS in EE/CS or equivalent experience. 12+ years of work experience.
- Strong understanding of large language models (LLMs), machine learning, and agentic AI, including how models, agent harnesses, context, and tool use affect end-to-end task outcomes.
- Hands-on experience evaluating AI tools or models and applying LLMs to software engineering workflows, with skills in experiment design, benchmark development, reproducibility, and failure analysis.
- Strong software engineering skills in Python, with experience in Java or Go and extensive scripting and automation experience.
- Experience building evaluation pipelines, developer tooling, or full-stack applications, including API integration, data management, and deployment in cloud or enterprise environments.
- Experience with tools for CI/CD setup such as Jenkins, Gitlab CI, Packer, Terraform, Artifactory, Ansible, Chef or similar tools.
- Good understanding of distributed systems, understanding of microservice architecture and REST APIs.
- Familiarity with software development, testing, code review, and build workflows, and the ability to translate evaluation results into clear technical recommendations and practical user guidance.
- Ability to effectively work across organizational boundaries to enhance alignment and productivity between teams.
Ways to stand out from the crowd:
- Industry thought leader in AI, influenced AI ecosystem to deliver forward looking solutions
- Expertise in agent harnesses, tool calling, MCP, retrieval-augmented generation (RAG), context management, or fine-tuning, and in evaluating their impact on complex engineering workflows.
- Experience rolling out AI tools across engineering teams, collaborating with vendors, and turning emerging capabilities into measurable adoption and productivity improvements.
- Experience developing large-scale distributed tooling, evaluation services, or observability systems with real-time constraints.
- Strong collaborative and interpersonal skills, with a consistent record of guiding and influencing others in dynamic environments.
We have some of the most forward-thinking and versatile people in the world working for us and, due to unprecedented growth, our best-in-class engineering teams are rapidly growing. We are building a team that will truly change the world. If you are passionate about new technologies, care about software quality, and want to shape the future of AI tools and engineering productivity, we would love for you to join us.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive 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.Skills Required
- MS in Electrical Engineering, Computer Science, or equivalent experience
- 12+ years of work experience
- Strong understanding of large language models, machine learning, and agentic AI
- Hands-on experience evaluating AI tools or models and applying LLMs to software engineering workflows
- Experience with experiment design, benchmark development, reproducibility, and failure analysis
- Strong software engineering skills in Python
- Experience with Java or Go
- Extensive scripting and automation experience
- Experience building evaluation pipelines, developer tooling, or full-stack applications
- Experience with API integration, data management, and deployment in cloud or enterprise environments
- Experience with CI/CD tools such as Jenkins, GitLab CI, Packer, Terraform, Artifactory, Ansible, Chef, or similar
- Good understanding of distributed systems, microservice architecture, and REST APIs
- Familiarity with software development, testing, code review, and build workflows
- Ability to translate evaluation results into clear technical recommendations and practical user guidance
- Ability to work effectively across organizational boundaries
- Expertise in agent harnesses, tool calling, MCP, retrieval-augmented generation, context management, or fine-tuning
- Experience rolling out AI tools across engineering teams and collaborating with vendors
- Experience developing large-scale distributed tooling, evaluation services, or observability systems with real-time constraints
- Industry thought leadership in AI
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.”
