Deep Learning Product Research Engineer - Product Innovation

NVIDIA•Us, CA

About The Position

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!

Requirements

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.

Nice To Haves

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.

Responsibilities

Build prototypes, proof-of-concept applications, benchmarks and technical demos to explore and showcase the art of possible with NVIDIA’s generative AI platform.
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.