Deep Learning Product Research Engineer - Product Innovation

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

NVIDIA is seeking a Deep Learning Product Research Engineer to bridge cutting-edge AI research and real-world product adoption. This role involves building prototypes, evaluating emerging technologies, creating technical assets (demos, white papers, sample code), and collaborating across research, engineering, and product teams to advance NVIDIA's generative AI platform. The engineer will translate research concepts into practical, developer-focused product examples and stay current with generative AI trends.

What you'd actually do

  1. 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.
  2. Evaluate emerging trends in generative AI, including large language models, multimodal systems, agentic applications, model evaluation, inference optimization, and AI-assisted software development.
  3. 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.
  4. 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.
  5. Present technical material through developer blogs, webinars, conferences, workshops, customer engagements, and community events.

Skills

Required

  • 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 have

  • 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.

What the JD emphasized

  • 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.
  • Ability to create clear, accurate, technically thorough, and compelling content for developers, including tutorials, blogs, sample code, white papers, benchmarks, or demos.
  • 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.

Other signals

  • building prototypes
  • evaluating emerging technologies
  • translating research into product capabilities
  • developer-facing materials