Deep Learning Product Research Engineer

Responsibilities

• Lead product research for generative AI by evaluating emerging models, agent technology, reinforcement learning, and evaluation methods, then assessing what they mean for NVIDIA products.
• Build proof-of-concept applications, benchmarks, and reference sample code that validate new capabilities and demonstrate product value.
• Convert customer, developer, benchmark, usage, and field signals into structured product intelligence, including adoption trends, friction points, issue reproductions, and roadmap recommendations.
• Develop enterprise-ready enablement assets such as reference architectures, integration playbooks, performance tuning recipes, and demo-to-production workflows for Nemotron, NeMo, NIM, and related NVIDIA AI software.
• Partner with research, engineering, product management, technical marketing, field teams, and customers to turn insights into feature requests, launch inputs, positioning, and usability improvements.
• Advance internal LLM expertise and tooling through reusable evaluation harnesses, profiling utilities, agentic workflows, and practical analysis of model behavior.
• Distill hands-on research and engineering work into authoritative technical assets, including code examples, technical write-ups, white papers, demos, talks, and patents where appropriate.
• Stay current with advances in model training, post-training, inference, agentic systems, evaluation, deployment, safety, 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 proven 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.
• Experience with Python and 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 rigorous, 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..
• 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 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.
• Familiarity with the broader generative AI ecosystem, including open models, agent frameworks, vector databases, evaluation tools, deployment platforms, and emerging AI developer workflows.
• With comprehensive benefits package, NVIDIA is wi

Additional Information

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 Engineering (PRE) team sits at the intersection of research, product engineering, and go-to-market. PRE exists to reduce uncertainty about what will make products succeed. Our primary outputs are working cutting-edge prototypes, product intelligence, and code-backed guidance that shape what NVIDIA builds and how customers embrace it. We are looking for a hands-on engineer and generative AI practitioner who can build prototypes, write high-quality code, evaluate emerging technologies, explain complex 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. 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!

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