NVIDIA is at the center of the AI revolution. Our deep learning platforms, models, frameworks, and accelerated computing technologies help developers, researchers, and enterprises build the next generation of intelligent applications The Deep Learning Product Research team sits at the intersection of engineering, product, research, developer relations, and go-to-market. We help accelerate the path from cutting-edge AI research to real-world product adoption by building high-quality technical assets, proof-of-concept applications, benchmarks, white papers, and developer-facing materials that advance NVIDIA’s generative AI platform We are looking for a hands-on engineer and generative AI practitioner who can build prototypes, write high-quality code, evaluate emerging technologies, explain sophisticated systems clearly, and turn research ideas into practical product capabilities. In this role, you will create prototypes, demos, white papers, benchmarks, blogs, sample applications, conference material, and other technical content. You will work closely with research, engineering, product, marketing, field teams, customers, and the developer community to identify opportunities, surface feedback, and improve products across NVIDIA’s AI ecosystem!
What you’ll be doing:
Build prototypes, proof-of-concept applications, benchmarks and technical demos to explore and showcase the art of possible with NVIDIA’s generative AI platform. You will translate this work directly into high-quality into scalable demo artifacts, white papers, sample code, and other developer-facing materials.
Evaluate emerging trends in generative AI, including large language models, multimodal systems, agentic applications, model evaluation, inference optimization, and AI-assisted software development.
Collaborate closely with product managers, engineering teams, researchers, field teams, customers, and marketing partners to translate product capabilities into practical, developer-focused examples. Serve as the technical bridge, translating advanced AI capabilities and research concepts into practical, developer-focused product examples.
Evaluate the technical feasibility, scalability, and product relevance of emerging technologies. Synthesize deep technical insights, authoring decision memos and feature requests to inform internal roadmaps, drive integrations, and improve NVIDIA’s software stack.
Present technical material through developer blogs, webinars, conferences, workshops, customer engagements, and community events.
Serve as a technical advocate for NVIDIA’s deep learning platform, helping developers understand how to build, optimize, and deploy AI applications using NVIDIA technologies.
Stay current with advances in deep learning, generative AI, model training, fine-tuning, inference, optimization, deployment, agentic workflows, and the broader AI developer ecosystem.
What we need to see:
Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent experience.
5+ years of meaningful experience in software engineering, machine learning engineering, AI engineering, solutions architecture, applied research, or a similar technical role.
Hands-on experience with machine learning, deep learning, or agentic AI, including building, training, fine-tuning, evaluating, deploying, or optimizing models and AI applications.
Practical experience with generative AI systems, including large language models, retrieval-augmented generation, agentic workflows, model evaluation, or AI application development.
Strong programming skills in Python, and experience with modern deep learning frameworks and libraries such as PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, TensorFlow, or similar tools.
Familiarity with modern AI-assisted development tools and coding agents such as Codex, Claude Code, Cursor, or similar systems.
Ability to create clear, accurate, technically thorough, and compelling content for developers, including tutorials, blogs, sample code, white papers, benchmarks, or demos.
Strong communication and presentation skills, with the ability to explain complex technical topics to both expert and non-expert audiences.
Ability to collaborate optimally across research, engineering, product, marketing, field, and customer-facing teams, and passion for applied AI research, technical storytelling, and improving the user experience for AI practitioners.
Ways to stand out from the crowd
PhD in Computer Science, Engineering, Machine Learning, Artificial Intelligence, or a related field.
3+ years of hands-on experience with machine learning, deep learning, generative AI, large language models, multimodal models, reinforcement learning, model optimization, or agentic applications.
Experience building production-quality AI applications, developer tools or research prototypes.
Experience designing or evaluating agentic AI systems, AI coding assistants, model evaluation harnesses, RAG pipelines, synthetic data workflows, or AI safety workflows.
Experience with NVIDIA AI software, models, or frameworks such as NeMo, NeMo Retriever, NeMo Guardrails, NeMo RL, NIM, TensorRT, Dynamo, CUDA, cuDNN, or Nemotron models.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most brilliant, forward-thinking and hardworking people in the world working for us. There has never been a more exciting time to join!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 136,000 USD - 212,750 USD for Level 3, and 160,000 USD - 253,000 USD for Level 4.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
- Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, Artificial Intelligence, or related field or equivalent experience.
- 5+ years of experience in software engineering, machine learning engineering, AI engineering, solutions architecture, applied research, or similar technical role.
- Hands-on experience building, training, fine-tuning, evaluating, deploying, or optimizing machine learning and deep learning models.
- Practical experience with generative AI systems, including large language models, retrieval-augmented generation, and agentic workflows.
- Strong programming skills in Python.
- Experience with modern deep learning frameworks and libraries such as PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, or TensorFlow.
- Familiarity with AI-assisted development tools and coding agents (e.g., Codex, Claude Code, Cursor).
- Ability to create clear, accurate, technically thorough developer content (tutorials, blogs, sample code, white papers, benchmarks, demos).
- Strong communication and presentation skills for technical and non-technical audiences.
- Ability to collaborate across research, engineering, product, marketing, field, and customer-facing teams.
- PhD in Computer Science, Engineering, Machine Learning, AI, or related field.
- Experience with NVIDIA AI software, models, or frameworks (NeMo, NeMo Retriever, NeMo Guardrails, NeMo RL, NIM, TensorRT, Dynamo, CUDA, cuDNN, Nemotron models).
- Experience building production-quality AI applications, developer tools, or research prototypes.
- Experience designing or evaluating agentic AI systems, model evaluation harnesses, RAG pipelines, synthetic data workflows, or AI safety workflows.
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.”

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